ELUCIDATION OF THE UNDERLYING MECHANISMS
GOVERNING ALGAE AND CYANOBACTERIA
SEPARATION USING THE POSIDAFTM PROCESS
By
Narasinga Rao Hanumanth Rao
A thesis submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
SCHOOL OF CHEMICAL ENGINEERING FACULTY OF ENGINEERING UNSW Australia
February 2018
THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet
Surname or Family name: HANUMANTH RAO
First name: NARASINGA RAO Other name/s: -
Abbreviation for degree as given in the University calendar: Ph.D.
School: CHEMICAL ENGINEERING Faculty: ENGINEERING
Title: Elucidation of the underlying mechanisms governing algae and cyanobacteria separation using the PosiDAFTM process
Abstract (350 words maximum) The novel PosiDAF process that uses cationic-polymer modified bubbles has been suggested as an alternative to conventional dissolved air flotation for the separation of algae. However, a cationic PosiDAF effluent compared to an anionic influent as detected by charge measurements indicated that effluent contained high polymer residuals and was undesirable. To prevent this, prior research investigated stronger polymer-bubble adhesion by developing hydrophobically modified polymers (HMPs) of poly(dimethylaminoethyl methacrylate) (PDMAEMA). However, while bench scale tests using the HMPs were successful, commercially available poly(diallyldimethylammonium chloride) (PDADMAC) outperformed the HMPs in pilot scale, suggesting that PDADMAC has a more suitable polymer backbone. Moreover, algal organic matter (AOM) released by cells, particularly biopolymers, was observed to influence cell separation. Further research is required to investigate alternate polymers and to determine more precisely the underlying mechanisms governing polymer-bubble-AOM interactions in PosiDAF. In this study, PDADMAC was modified with various aromatic and aliphatic pendant groups to generate several HMPs. Select HMPs of PDADMAC and previously investigated PDMAEMA were compared to evaluate polymer- bubble attachment and PosiDAF performance to separate algae and cyanobacteria. The composition of AOM, particularly biopolymers from each strain tested was characterised and their influence examined by conducting experiments with various AOM, protein and carbohydrate concentrations. The results showed that HMP coated bubbles had lower surface tensions and consequently, anionic effluents and strong polymer-bubble adhesion. Concurrently, cell separation was either comparable, or slightly better between HMPs. However, separation effectiveness varied for several algae, indicating that AOM impacted separation. Moreover, cell separation of the strains increased to > 95% when exudates from the best separated strain were added. On bulk and molecular characterisation of the cultures, the best separated strain was found to be biopolymer rich in comparison to the other strains. Hence, proteins and carbohydrates were dosed to study their influence and were observed to either depress or enhance the flotation depending on their character. It was concluded that the interplay of biopolymers with polymer-bubble-cell was responsible for the variations in cell removal observed across several strains. Overall, with a well-defined AOM character and low polymer residual in effluent, PosiDAF has been demonstrated as a robust and sustainable process.
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‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.'
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Originality Statement
‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’
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Acknowledgements
I would first like to express my heartfelt gratitude to my academic parents: Rita Henderson and Tony Granville, for providing me the opportunity to undertake a PhD and for their continuous advice and reassurance whenever I thought it was the end of the world during research. I have been incredibly fortunate to have supervisors who have been instrumental in my personal and professional growth during my PhD. Many thanks in particular to Rita, who was willing to sacrifice her personal time during maternity leave to proofread the multiple iterations of the chapters within this thesis. I would also like to profusely thank Prof. Richard Stuetz, Prof. Ray Dagastine and Prof. Bruce Jefferson who provided critical inputs throughout the research, and Prof. Pascal Guiraud and Prof. Michael Eppink for examining my thesis.
I am also extremely grateful to my uncle, mother, and sister, to who I wholeheartedly dedicate this thesis. I have been greatly inspired by the immense strength of character they have shown during times of financial hardship back home and when all the odds were stacked against us. I would like to thank Prof. Subramanian Swamy who, with his inspiring talks and philosophy has been a constant source of motivation throughout my candidature.
At this juncture, I want to extend my appreciation to all my friends, particularly the following: Shamina – for being a great source of encouragement and forbearance through thick and thin, and by asking often: When will you ever finish your work?!! I wouldn’t be as successful without your help! Manjusha – for helping me settle down during the early part of my candidature and during hard times. I owe a lot to you especially for helping me overcome a difficult 2014. Pradeep, Mei and Ari – for being fantastic flatmates who I would gladly keep hold of in my life. I’ve had a great time and memories being around and learning from you guys. Florence – for being my best buddy at uni and for showing me what it means to multi-task. Andrea – for helping me with experiments, data analysis and teaching me the ropes in algae back in 2014. Over the course of our respective PhDs, we have learnt immensely from each other and it has been a great pleasure working with you. Dries, Xiang, Sara and Yunlong – for helping me with data analysis and for being a great bunch of friends to hang out with. Russell – for encouraging me to take up PhD and helping me with the research during important times.
Lastly, this section would in no way be complete without extending my thanks to my Sriguru Sri Raghavendra, the saints of Kanchi and the lord almighty! ~ v ~
Publications and Presentations
Henderson, R.K, Yap, R.K.L., Stuetz, R.M., Peirson, W.L, Whittaker, M.R, Narasinga Rao, Granville, A.M., Jefferson, B.J., 2015. PosiDAF™: Simplifying Algal Cell Separation Through Process Intensification, APPChE Congress, Melbourne, Australia, 27 September – 1 October 2015. – Paper and presentation. Rao, N.R.H., Yap, R.K.L., Granville, A.M., Stuetz, R.M., Henderson, R.K, 2016. Algae flotation using PosiDAF: A comparison of polymers as bubble surface modifiers, Flotation 2016 – 7th International IWA Conference on Flotation for Water and Wastewater Systems, Toulouse, France, 26th – 30th September 2016. – Paper and presentation. Anthony Granville, Rita Henderson, Narasinga Rao Hanumanth Rao, Russell Yap, 2015. New approach to dissolved air flotation: Bubble surface modification using tailored polymers. In proceedings: PACIFICHEM 2015, Dec 15-20, 2015, Hawaii, US. – Paper and presentation. Hanumanth Rao, N. R., R. Yap, M. Whittaker, R. M. Stuetz, B. Jefferson, W. L. Peirson, A. M. Granville and R. K. Henderson (2018). "The role of algal organic matter in the separation of algae and cyanobacteria using the novel “Posi” - Dissolved air flotation process." Water Research 130(Supplement C): 20-30. – Published. Rao,N.R.H., Granville, A.M., Browne, C.I., Dagastine, R.R., Yap, R.K.L., Jefferson, B., Henderson, R.K,. Determining how polymer-bubble interactions impact algal separation using the novel “Posi”- dissolved air flotation process – Under review at Separation & Purification Technology. Rao,N.R.H., Granville, A.M., Henderson, R.,. The impact of M. aeruginosa CS-564/01 proteins on algae and cyanobacteria separation in PosiDAF – Prepared manuscript for submission to Water Research. Rao,N.R.H., Granville, A.M., Henderson, R.K,. The strength of interplay of carbohydrate and lectins in determining the efficiency of PosiDAF – Manuscript in preparation for submission to Water Research. Rao,N.R.H., Granville, A.M., Yap, R.K.L., Henderson, R.K,. The application of polymers for enhanced separation via flotation processes – Manuscript in preparation for submission to Advances in colloid and interface science.
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Abstract
A novel dissolved air flotation (DAF) process, termed PosiDAF, uses polymers to modify bubble surfaces and make them cationic. PosiDAF has been suggested as an alternative to conventional DAF, which is traditionally preceded by metal-salt coagulation-flocculation for the separation of algae in either water treatment or harvesting contexts. Previously, it was demonstrated that PosiDAF reduced the chemical demand and removed algae without metal salt contamination of the float, while increasing its solid-liquid ratio. However, a change in the zeta potential of the treated effluent from negative to positive indicated that a significant proportion of the polymers were not closely attached to the bubble surface, reducing the efficiency of the process and contaminating the effluent. To mitigate this, subsequent studies investigated enhancing the adhesion of polymers to bubble surfaces through the development of hydrophobically modified polymers (HMPs) by adding hydrophobic pendant groups to poly(N,N-dimethylaminoethyl methacrylate) (PDMAEMA). However, challenges remained; while successful separation was obtained at bench scale using the HMPs, this was not the case at pilot scale, where commercially available polydiallyldimethyl ammonium chloride (PDADMAC) outperformed the selected HMP. Moreover, large variations in process performance were observed dependent on algal species, attributed to the difference in concentration and character of algal organic matter (AOM) released by the cells, although the precise mechanisms of interaction remained unknown. Further research is also required to investigate PosiDAF performance when hydrophobically modifying alternative polymer backbones and to determine the underlying mechanisms by which the polymers interact at the bubble surface and with AOM. Hence, the aim of this research was to study the underlying mechanisms governing separation of algae and cyanobacteria using PosiDAF.
For this study, PDADMAC was de-methylated and quaternised with various aromatic and aliphatic pendant groups to generate several HMPs and the performance evaluated. Select PDADMAC based and previously investigated PDMAEMA HMPs were compared via atomic force microscopy to better understand the degree of polymer- bubble attachment. The same polymers were then trialled using PosiDAF for performance when separating the following algal and cyanobacterial species: Chlorella
~ vii ~ vulgaris CS-42/7, Mychonastes homosphaera CS-556/01 and two strains of Microcystis aeruginosa (CS-564/01 and CS-555/1). The composition of AOM from each strain was characterised with a number of conventional and advanced analytical techniques, including spectrometric and chromatographic analyses of proteins and carbohydrates, and their individual and collective influence examined by conducting controlled experiments with various AOM concentrations. The impact of the presence of specific carbohydrates and proteins was also examined through their addition during flotation experiments.
Analysis of the AFM results in conjunction with the cell separation and effluent analysis showed that the cell separation between both types of HMPs were comparable. However, the PDMAEMA derivatives were found to adsorb very tightly to the bubble surface unlike those of PDADMAC which projected away from the bubble and into the surrounding matrix. The differing characteristics of the AOM had the most significant impact on cell separation effectiveness, which varied from 35-95% between species. When the exudates from the strain that gave the best separation performance (M. aeruginosa CS-564) were dosed into the other strains that were not well separated, it was possible to improve the cell separation of the latter to greater than 95%, indicating that AOM character dictates cell separation effectiveness. Further analysis of the AOM revealed that the conjugation between glycoproteins, acidic carbohydrates and carbohydrates in the AOM were responsible for the variations in cell separation. Subsequently, native AOM was removed from all strains and, instead, cells were prepared in a “synthetic” AOM solution comprising galacturonic acid, fucose and proteins that were extracted from M. aeruginosa CS-564/01. On repeating PosiDAF jar testing under these conditions, the cell separation increased to at least 90% for all the species. This led to the conclusions that process optimisation requires an in-depth understanding of AOM, as the interplay between proteins and carbohydrates impacted cell removal in the majority of strains. Moreover, for biotechnology applications, cultivating strains with a high acidic carbohydrate and glycoprotein composition would be beneficial. Further research on optimising PosiDAF performance on pilot scale is recommended.
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Table of Contents
Chapter 1
INTRODUCTION ...... 1
1.1 Background ______1
1.2 Research objectives ______4
1.3 Thesis Synopsis ______5
Chapter 2
LITERATURE REVIEW ...... 8
2.1 Review A: Polymer interactions with bubbles and particles in flotation: Lending
insight to the PosiDAF process ______8
2.1.1 Flotation principles: Current understanding ______10
2.1.2 Polymer application for pre-treatment of particles to optimise bubble-particle
attachment ______12
2.1.3 An alternative to particle modification: chemically modifying bubble surfaces _ 14
2.1.4 Factors impacting polymer-particle-bubble interactions and flotation efficiency 18
2.1.4.1 Polymer molecular weight and charge ______19
2.1.4.2 The impact of polymer functional groups on flotation ______25
2.1.4.3 The use of branched polymers in flotation ______26
2.1.4.4 Influence of post-polymerisation modification in polymers on flotation
efficiency______27
2.1.4.5 The impact of polymer hydrophobicity on flotation efficiency ______28
2.1.5 The importance of studying interactions between polymers, particles and bubbles 30
2.1.6 Conclusions and knowledge gaps ______33
2.2 Review B: Algal organic matter: characterisation and influence on flotation _____ 35
2.2.1 The influence of algal and cyanobacterial cell characteristics on flotation ______36 ~ ix ~
2.2.2 AOM and its components ______38
2.2.2.1 Algal and cyanobacterial carbohydrates ______38
2.2.2.2 Algal and cyanobacterial proteins ______39
2.2.3 Impact of AOM and its components on flotation efficiency ______40
2.2.3.1 Influence of algal and cyanobacterial carbohydrates on flotation efficiency __ 41
2.2.3.2 Influence of algal and cyanobacterial proteins on flotation efficiency ______43
2.2.4 Techniques for characterising AOM and its components ______44
2.2.4.2 Fourier transform infrared spectroscopy (FT-IR) ______46
2.2.4.3 Techniques for the characterisation of biopolymers in AOM ______47
2.2.4.3.1 Carbohydrate characterisation ______48
2.2.4.3.2 Protein Characterisation ______Error! Bookmark not defined.
2.2.5 Conclusions and knowledge gaps ______54
Chapter 3
APPLICATION OF HYDROPHOBICALLY MODIFIED POLYMERS IN POSIDAF:
BUBBLE PROPERTIES AND FLOTATION PERFORMANCE ...... 56
3.1 Introduction ______56
3.2 Materials & methods ______58
3.2.1 Materials ______58
3.2.1.1 Chemicals ______58
3.2.1.2 Algae and cyanobacteria ______59
3.2.2 Experimental methods ______59
3.2.2.1 Synthesis of hydrophobically functionalised PDADMAC ______59
3.2.2.2 Free radical polymerisation of DMAEMA and its quaternisation ______61
3.2.2.3 Jar testing ______62
3.2.2.3.1 Synthetic DAF medium ______62
3.2.2.3.2 PosiDAF jar tests ______62
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3.2.3 Characterisation and Analytical Methods ______63
3.2.3.1 Cell characterisation ______63
3.2.3.2 NMR characterisation ______63
3.2.3.3 Zeta potential and hydrodynamic size measurements ______64
3.2.3.4 Surface tension measurements of polymer in water ______64
3.2.3.5 Charge density analysis ______64
3.2.3.6 Bubble characterisation through AFM measurements ______65
3.2.3.7 Effluent analysis ______67
3.3 Results ______68
3.3.1 Characterisation of algae and cyanobacteria ______68
3.3.2 Characterisation of functionalised polymers ______69
3.3.3 Polymer-bubble interaction characterisation ______72
3.3.4 Effect of hydrophobically functionalised polymers on cell removal for various
algae______74
3.3.5 PosiDAF effluent ______75
3.3.5.1 Residual polymer detection and quantification ______77
3.4 Discussion ______79
3.5 Summary ______81
Chapter 4
THE ROLE OF ALGAL ORGANIC MATTER IN THE SEPARATION OF ALGAE AND
CYANOBACTERIA ...... 83
4.1 Introduction ______83
4.2 Materials & methods ______85
4.2.1 Chemicals______85
4.2.2 Algae and Cyanobacteria ______85
4.2.3 Algal Organic Matter ______86
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4.2.4 Algal System Characterisation ______86
4.2.4.1 Cell Properties ______86
4.2.4.2 Analysis of Algal Organic Matter (AOM) ______87
4.2.5 Jar Test Procedures ______89
4.2.5.1 PosiDAF jar tests______89
4.2.5.2 Investigation of the influence of AOM on PosiDAF performance ______89
4.2.5.2.1 Examining the impact of “native” AOM of the algal and cyanobacterial
species on PosiDAF performance ______89
4.2.5.2.2 Examining the impact of “foreign” cyanobacterial AOM on algal and
cyanobacterial PosiDAF performance ______90
4.2.5.3 Analysis of flotation performance ______90
4.2.5.3.1 Fourier transform infrared spectroscopy (FT-IR) imaging analysis ___ 90
4.3 Results ______92
4.3.1 Cell System Characterisation ______92
4.3.2 PosiDAF ______98
4.3.3 Investigating link between PosiDAF cell separation and AOM composition __ 100
4.3.3.1 FT-IR analysis of web-like strands from MA564 ______100
4.3.3.2 Cell separation as a function of acidic carbohydrate proportion ______105
4.3.4 PosiDAF Jar Tests with Altered AOM Conditions ______106
4.3.4.1 Examining the influence of “native” AOM of the algal and cyanobacterial
species on PosiDAF performance ______106
4.3.4.2 Examining the influence of “foreign” cyanobacterial AOM on algal and
cyanobacterial PosiDAF performance ______108
4.4 Discussion: The Role of AOM in PosiDAF ______110
4.5 Summary ______112
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Chapter 5
THE IMPACT OF PROTEIN-CARBOHYDRATE CONJUGATE INTERPLAY ON THE
SEPARATION OF ALGAE AND CYANOBACTERIA USING THE POSIDAF PROCESS
...... 114
5.1 Introduction ______114
5.2 Materials & methods ______115
5.2.1 Chemicals______115
5.2.2 Algae and cyanobacteria ______116
5.2.3 Algal organic matter ______116
5.2.3.1 Extraction of algal proteins ______117
5.2.4 Algal System Characterisation ______117
5.2.4.1 Cell Properties and AOM analysis ______117
5.2.4.2 Protein Characterisation ______118
5.2.4.3 Carbohydrate characterisation ______120
5.2.5 Determining PosiDAF jar test procedures ______121
5.2.5.1 Determination of the most effective biopolymers and their dose ______121
5.2.5.2 Investigation of the influence of AOM on biopolymer dosing ______121
5.2.5.3 Determination of optimum biopolymer dose location ______122
5.2.5.4 Investigation of the influence of carbohydrates and proteins derived from
algae ______122
5.3 Results ______123
5.3.1 Cell System Characterisation ______123
5.3.2 Carbohydrate Characterisation ______123
5.3.3 Protein Characterisation ______125
5.3.3.1 Protein profiling through SDS-PAGE ______125
5.3.3.2 Determination of functional characteristics of proteins ______129
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5.3.3.2.1 Detection of carbohydrate binding proteins and their potential impact on
PosiDAF efficiency ______130
5.3.3.3 Protein extraction: implications for PosiDAF experiments ______132
5.3.4 Optimisation of the PosiDAF process through the addition of biopolymers _ 133
5.3.4.1 Determination of the most effective type and dose of biopolymers to
enhance separation in PosiDAF ______133
5.3.4.2 Impact of native AOM on biopolymer addition ______136
5.3.4.3 Determination of ideal location for biopolymer dosing in PosiDAF ____ 137
5.3.4.4 PosiDAF optimisation through the addition of carbohydrates and algal-
derived proteins ______138
5.4 Discussion ______140
5.4.1 Biopolymer chemistry dictating the attachment of AOM to the cells ______141
5.4.2 Charged polymers as interlocutors between biopolymer-cell structures and
bubbles______143
5.5 Summary ______145 Chapter 6
CONCLUSIONS AND FUTURE WORK ...... 147
6.1 Conclusions ______147
6.2 Recommendations for future work ______153 Chapter 7
REFERENCES ...... 155 Chapter 8
APPENDIX ...... 182
Appendix A: Chapter 3 ______182
Appendix B: Chapter 4 ______190
Appendix C: Chapter 5 ______195 ~ xiv ~
List of Figures
Chapter 1
Figure 1-1. A schematic of the (A) conventional DAF and (B) PosiDAF systems. ______4 Chapter 2
Figure 2-1. Different zones around a bubble influencing its interaction with other particles
(Remade from Derjaguin et al. (1993)). ______11
Figure 2-2. Plot of zeta potential ranges of various polymer coated bubbles at pH 6-8.
(Malley 1995a, Kim et al. 2000, Oliveira et al. 2011, Yap et al. 2014b) ______16
Figure 2-3. Charge density vs molecular weight of common polymers used in flotation
(Kam et al. 2001, Cao et al. 2002, Ramos-Tejada et al. 2003, Burdukova et al.
2010, Henderson et al. 2010b, Nunes 2011, Nasser et al. 2013, Yap et al. 2014b) ___ 21
Chapter 3
Figure 3-1. Reaction scheme indicating de-methylation and quaternisation of the de-
methylated PDADMAC. ______60
Figure 3-2. Reaction scheme indicating synthesis of DMAEMA homo polymer and its
subsequent quaternisation with 1- bromodecane. ______61
Figure 3-3. Maximum removal efficiencies for PDADMAC, PDADMAC-BC,
PDADMAC-BCF, PDADMAC-C5, PDMAEMA and PDMAEMA-C10 when
treating CV, MA555, and MA564. For full dataset, refer to Figures A1-A3 in
Appendix A. ______75
Figure 3-4. Comparison of the zeta potential ranges obtained for the treated effluent for
the full dose ranges of PDMAEMA, PDMAEMA-C10, PDADMAC,
PDADMAC-BCF, PDADMAC-BC, PDADMAC-C5 on CV, MA555, and
MA564. ( ), ( ), ( ) represent zeta potential at maximum removal of CV,
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MA555, and MA564, respectively. For full dataset, refer to Figures A1-A3 in
Appendix A. ______76
Figure 3-5. (A) F-EEM obtained while testing PDADMAC-BCF treated effluent of CV.
P1 indicates the region associated with the polymer fluorescence while T1, C1
and C2 indicate the tryptophan-like, chlorophyll a and b regions, respectively.
Initial PDADMAC-BCF concentration – 0.9 mg/L (0.004 meq/L). (B) F-EEM
with the 240-400 nm excitation and 240-400 nm emission region magnified. ______78
Figure 3-6. Proportion of the initial polymer concentration dosed in PosiDAF
experiments remaining in treated effluent on treating CV, MA555 and MA564.
The polymers tested were PDADMAC-BC and PDADMAC-BCF. ______78
Figure 3-7. Schematic depicting the equilibrium between polymer adsorbed on the bubble
and that free in bulk solution and the impact of alkyl and aryl side chains on
hydrophobic association with the bubble; (A) - Flat and tight configuration of
PDMAEMA polymers on the bubble (B) – Loosely bound configuration of
PDADMAC polymers on the bubble. ______81
Chapter 4
Figure 4-1. Comparison of PosiDAF performance with PDADMAC on CV, MH, MA555
and MA564; A – Cell removal efficiency and B – Zeta Potential of effluent Vs
Dose of PDADMAC; Hollow circular and triangular data points obtained from
Yap (2013). ______100
Figure 4-2. Infrared absorbance spectra as a total of all 4096 individual spectra in each
image, with assignment of major peaks numbered according to Appendix B Table
B1. Spectra are presented as absorbance vs wavenumber (cm-1), and have been
baseline corrected and arbitrarily scaled for comparison. ______103
Figure 4-3. Image (A) - Strands from MA564 viewed under a microscope. Images (B),
(C) - 20 x 20 μm2 FTIR images of strand edge with the corresponding cross
section spectra of the strand edge. Images (D), (E) - 20 x 20 μm2 FTIR images of
~ xvi ~
strand centre with the corresponding cross section spectra of the centre of the
strand. Red regions in images (B) to (E) are the relative intensities of the protein
amide I peak at 1630 cm-1. Green regions in (B) and (D) are the relative
intensities of the carbohydrate C-O vibration at 1030 cm-1, and in (C) and (E), are
the relative intensities of the acidic carbohydrate anion COO- vibration at 1330
cm-1. The regions of orange and yellow in (B) to (E) are the overlaps of the
respective chemical regions in the image. For full dataset, see Appendix B Figure
B4. ______104
Figure 4-4. Comparison of maximum cell removal of CV, MH, MA555 and MA564 vs
the acidic carbohydrates detected by Alcian Blue staining. Acidic carbohydrate
concentration represented as percent area per 0.18 mm2 of polycarbonate
membrane. ______105
Figure 4-5. PosiDAF jar test outcomes with PDADMAC for CV, MH, MA555 and
MA564 cells with their respective AOM added to jars at various concentrations;
A – Cell removal efficiency and B – Zeta potential of effluent vs dose of AOM;
Concentrations of PDADMAC for MA555, MA564 were 0.0005 meq/L and for
CV, MH were 0.004 meq/L. Hollow circular and triangular data points obtained
from Yap (2013). ______107
Figure 4-6. Cell separation as a function of AOM dose for Experiment 2 jar tests of A –
MA555; B - CV; C – MH, using MA564 AOM to enhance flotation. Hollow
circular, square and triangular data points obtained from Yap (2013). ______109
Figure 4-7. Mechanism showing the role played by AOM on PosiDAF efficiency ______112
Chapter 5
Figure 5-1. List and concentrations of the carbohydrates detected in CV, MA555 and
MA564. ______125
Figure 5-2. Protein profiling of BSA, HSA, Con A, CIP, 564IP, 555IP conducted through
SDS-PAGE. Dark bands represent the separated proteins. Biorad unstained
~ xvii ~
marker column represented as the standard. Well 1 – BSA, Well 2 – HSA, Well 3
– ConA, Well 4 – CIP, Well 5 – 564IP, Well 6 – 555IP. ______127
Figure 5-3. Venn diagram indicating the functional distribution and the number of
similar and distinct proteins present in CV, MA555 and MA564. Coloured zones
indicate the respective proportions of the functional proteins. White spaces
represent no proteins present. For full protein dataset, please see Appendix Table
A5-1 to A5-6. ______130
Figure 5-4. PosiDAF dose response curve while separating CV using BSA, ConA, HSA,
CV protein, MA555 protein, and MA564 protein. The dashed line indicates the
maximum removal obtained for CV in the absence of protein addition (from
Chapter 3). ______134
Figure 5-5. PosiDAF dose response curve while separating CV using glucose, galactose,
glucosamine, glucuronic acid and galacturonic acid at different carbohydrate
concentrations. Dashed lines indicate the maximum removal obtained for CV in
the absence of carbohydrate addition (Chapter 3). ______135
Figure 5-6. Cell separation obtained in PosiDAF while separating CV with and without
AOM using PDADMAC and BSA dosed with glucuronic acid, galacturonic acid
and glucosamine. ______137
Figure 5-7. Cell removal when CV cells without AOM were supplemented with BSA and
glucose in four different ways to identify the best location to dose these
biopolymers. Dashed lines indicate the maximum removal obtained for CV in the
absence of biopolymer addition (Chapter 3). ______138
Figure 5-8. Cell removal when PosiDAF influent containing cells of CV, MA555, and
MA564 are dosed with MA564 protein, galacturonic acid-fucose, glucuronic
acid-fucose, glucosamine-fucose. Dashed lines indicate the maximum removal
obtained for CV, MA555, and MA564 in the absence of biopolymer addition
(Chapter 3). ______140
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Figure 5-9. Schematic of how the cationic polymers link the biopolymer-cell structures
and the bubbles to result in effective flotation. ______144
Chapter 6
Figure 6-1. Schematic describing: (a) hydrophobic interactions between the HMPs of
PDADMAC and bubble, and (b) lectin-carbohydrate conjugation caused by the
hydrogen bonding between the cells and biopolymers. ______148
Chapter 8
Figure A1. Comparison of PosiDAF performance with PDADMAC, PDADMAC-C5,
PDADMAC -BC, PDADMAC -BCF, PDMAEMA and PDMAEMA-C10 on CV;
A – Cell removal efficiency and B – Zeta Potential of effluent Vs Dose of
PDADMAC, PDADMAC-C5, PDADMAC-BC, PDADMAC-BCF, PDMAEMA
and PDMAEMA-C10; ______182
Figure A2. Comparison of PosiDAF performance with PDADMAC, PDADMAC-C5,
PDADMAC-BC, PDADMAC-BCF, PDMAEMA and PDMAEMA-C10 on
MA555; A – Cell removal efficiency and B – Zeta Potential of effluent Vs Dose
of PDADMAC, PDADMAC-C5, PDADMAC-BC, PDADMAC-BCF,
PDMAEMA and PDMAEMA-C10; ______183
Figure A3. Comparison of PosiDAF performance with PDADMAC, PDADMAC-C5,
PDADMAC-BC, PDADMAC-BCF, PDMAEMA and PDMAEMA-C10 on
MA564; A – Cell removal efficiency and B – Zeta Potential of effluent Vs Dose
of PDADMAC, PDADMAC-C5, PDADMAC-BC, PDADMAC-BCF,
PDMAEMA and PDMAEMA-C10; ______184
Figure A4. Standard Curve of fluorescence intensity vs concentration of PDADMAC-
BCF ______185
Figure A5. Standard Curve of fluorescence intensity vs concentration of PDADMAC-BC __ 185
~ xix ~
Figure A6. A sample standard fluorescence excitation emission matrix (F-EEM) obtained
while testing PDADMAC-BCF in buffer used for preparing the DAF influent.
Polymer peak excited and emitted at approximately 260 nm and 280 nm,
respectively. Polymer concentration: 0.9 mg/L (0.004 meq/L) ______186
Figure B1. LC-OCD and UV results for (A) MA555 (B) MA564 (C) MH (D) CV. ______191
Figure B2. TEP stained with Alcian blue for (A) – MA555, (B) – MA564, (C) – CV, (D)
– MH. ______192
Figure B3. (A) Protein; (B) carbohydrate concentrations of the respective filtered
fractions of AOM from MH, CV, MA555 and MA564. ______193
Figure B4. Images (A) to (D) - 20 x 20 μm2 FTIR images of strand edge with the
corresponding cross section spectra of the strand edge. Images (E) to (H) - 20 x
20 μm2 FTIR images of strand centre with the corresponding cross section spectra
of the centre of the strand. Red regions in images (A) to (H) are the relative
intensities of the protein amide I peak at 1630 cm-1. Green regions in (A), (C),
(E), (G) are the relative intensities of the carbohydrate C-O vibration at 1030 cm-
1, and in (B), (D), (F), (H) are the relative intensities of the acidic carbohydrate
anion COO- vibration at 1330 cm-1. The regions of orange and yellow in (A) to
(H) are the overlaps of the respective chemical regions in the image. ______194
~ xx ~
List of Tables
Chapter 2
Table 2-1. The structure and characteristics of common polymers used in flotation
applications ______23
Table 2-2. Experimental methods used to study interactions between polymers, particles
and bubbles ______32
Table 2-3. Regions where AOM components typically predominate when analysed
through FT-IR. Data compiled from Giordano et al. (2001), Stehfest et al. (2005),
Burgula et al. (2007), Villacorte et al. (2015b), Gonzalez-Torres (2017). ______47
Table 2-4. List of analytical methods used for characterisation of carbohydrates ______50
Table 2-5. List of analytical methods used for characterisation of proteins ______54
Chapter 3
Table 3-1. Physical and charge characteristics of Australian strains of MA (564 and 555)
and CV, and UK strains of MA and CV. ______69
Table 3-2. Characterisation of polyelectrolytes used in PosiDAF jar tests ______71
Table 3-3. Characterisation of polymer coated bubbles in PosiDAF at pH 7 ______74
Chapter 4
Table 4-1A. Physical and charge characteristics of the cultures and different AOM
fractions extracted from MA555, MA564, MH and CV in comparison with
previously tested UK strains of MA (CCAP 1450/3) and CV (CCAP 211/11B). ____ 94
Table 4-1B. Chemical composition of the different AOM fractions extracted from
MA555, MA564, MH and CV in comparison with previously tested UK strains of
MA (CCAP 1450/3) and CV (CCAP-211/11B). All assays were done in
triplicate. ______97
~ xxi ~
Table 4-2. A comparison of the biopolymers, humic substance, building blocks and LMW
neutrals as determined from LC-OCD analysis of AOM0.45 ______98 Chapter 5
Table 5-1. Physiochemical properties of cells and AOM from CV, MA555 and MA564. ___ 123
Table 5-2. Classification of proteins (before and after extraction) from CV, MA555 and
MA564 based on their charge, molecular weight and emPAI. ______128
Chapter 8
Table A1. Polymers trialled: PDADMAC-BC, PDADMAC-BCF. Species trialled on:
Chlorella vulgaris CS – 42/7. ______187
Table A2. Polymers trialled: PDADMAC-BC, PDADMAC-BCF. Species trialled on:
Microcystis aeruginosa CS – 564/01. ______188
Table A3. Polymers trialled: PDADMAC-BC, PDADMAC-BCF. Species trialled on:
Microcystis aeruginosa CS – 555/1. ______189
Table B1. Band assignment- FTIR, adapted from Giordano et al. (2001), Stehfest et al.
(2005), Burgula et al. (2007), Villacorte et al. (2015b), Gonzalez-Torres (2017) ___ 190
Table C1. List of enzymes present in MA555 and their properties before extraction. ______195
Table C2. List of non-enzymes present in MA555 and their properties before extraction. ___ 197
Table C3. List of enzymes present in MA564 and their properties before extraction. ______200
Table C4. List of non-enzymes present in MA564 and their properties before extraction. ___ 202
Table C5. List of enzymes present in CV and their properties before extraction. ______205
Table C6. List of non-enzymes present in CV and their properties before extraction. ______206
Table C7. List of enzymes present in MA555 and their properties after extraction. ______206
Table C8. List of non-enzymes present in MA555 and their properties after extraction. ____ 207
Table C9. List of enzymes present in MA564 and their properties after extraction. ______208
Table C10. List of non-enzymes present in MA564 and their properties after extraction. ___ 210
Table C11. List of enzymes present in CV and their properties after extraction. ______211
Table C12. List of non-enzymes present in CV and their properties after extraction. ______212 ~ xxii ~
List of Abbreviations
Abbreviation Expansion
AFM Atomic force microscopy ANACC Australian National Algae Culture Collection AOM Algal organic matter AOMc Unfiltered AOM after centrifugation AOM1.50 AOM filtered through 1.50 µm membrane AOM0.45 AOM filtered through 0.45 µm membrane ARC Australian Research Council
BSA Bovine serum albumin
CCAP Culture Collection for Algae and Protozoa CDW Chan-Dagastine-White model C-F Coagulation and flocculation CGA Colloidal gas aphrons 3-[(3-Cholamidopropyl)dimethylammonio]-1- CHAPS propanesulfonate ConA Concanavalin A Commonwealth Scientific and Industrial Research CSIRO Organisation CV Chlorella vulgaris CS-42/7
DADMAC Diallyldimethylammonium DAF Dissolved air flotation DLS Dynamic light scattering DMAEMA 2-(N, N-dimethylamino)ethyl methacrylate DOC Dissolved organic carbon
EDL Electrical double layer EEM Excitation emission matrix EPS Extracellular polymeric substances
F-EEM Fluorescence excitation emission matrix FT-IR Fourier transform infrared spectroscopy
HLB Hydrophile-lipophile balance HMP Hydrophobically modified polymer High performance anion exchange chromatography with HPAEC-PAD pulsed amperometric detection HSA Human serum albumin
IAF Induced air flotation
~ xxiii ~
Abbreviation Expansion
Liquid chromatography organic carbon and nitrogen LC-OCND detector LMW Low molecular weight
MA555 Microcystis aeruginosa CS-555/1 MA564 Microcystis aeruginosa CS-564/01 MALDI Matrix-assisted laser desorption/ionization MH Mychonastes homosphaera CS-566/01 Mw Molecular weight
NMR Nuclear magnetic resonance NOM Natural organic matter
PAM Poly(acrylamide) PARAFAC Parallel factor analysis PCD Particle charge detector PDADMAC Poly(N, N-diallyl-N,N-dimethylammonium chloride) PDADMAC-BC PDADMAC quaternised with benzyl chloride PDADMAC-BCF PDADMAC quaternised with 4-fluoro benzyl chloride PDADMAC-C4 PDADMAC quaternised with 1-chlorobutane PDADMAC-C5 PDADMAC quaternised with 1-chloropentane PDADMAC-C10 PDADMAC quaternised with 1-chlorodecane PDMAEMA Poly(N, N-dimethylaminoethyl methacrylate) PDMAEMA-C10 PDMAEMA quaternised with 1-brompdecane PEG Poly(ethylene glycol) Poly(ethylene oxide)–poly(propylene oxide) block PEO-PPO copolymer PNIPAM Poly(N-isopropyl acrylamide) PSS Poly(styrene sulfonate)
Sodium dodecyl sulphate-polyacrylamide gel SDS-PAGE electrophoresis SEC Size exclusion chromatography SUVA specific UV absorbance
TEP Transparent exopolymer particles TFA Trifluoroacetic acid TOC Total organic carbon TPL Three phase line
~ xxiv ~
Chapter 1
INTRODUCTION
1.1 Background
Algae and cyanobacteria are ubiquitous in surface waters; however, if suitable conditions prevail, they can multiply to very high cell densities, challenging separation processes in water treatment systems (Bernhardt 1984). Algae are also an important feedstock for biofuel production (Costa et al. 2011), where the harvesting of cells has been undertaken using separation processes common to the water treatment industry
(Weschler et al. 2013). In both water treatment and algae harvesting contexts, dissolved air flotation (DAF) is often applied to separate the cells, yielding comparatively high solids content and cell separation efficiencies due to the low density of algae (Gehr et al. 1982, Englert et al. 2009, Fang et al. 2010, Coward et al. 2013, Karhu et al. 2014).
However, successful DAF performance relies on pre-treatment by coagulation and flocculation (C-F), typically using metal salts (Edzwald 2010) (Figure 1-1). From the perspective of water treatment, during an algal bloom excessive quantities of coagulant can be required, making the process economically unfavourable (Pivokonsky et al.
2006, Takaara et al. 2007, Sano et al. 2011, Vandamme et al. 2014). When harvesting algal biomass for biofuel production, metal coagulants can interfere with downstream processing, for example, in biofuel conversion (Anthony et al. 2013). Hence, a novel
DAF process that is not reliant on such pre-treatment has been investigated for the purpose of algae separation, termed in some studies, and in this thesis, as PosiDAF
(Malley 1995b, Henderson et al. 2008d, Henderson et al. 2009b, Yap 2013, Yap et al.
2014a).
Chapter 1 Introduction
In PosiDAF, bubble surfaces are coated with cationic polymers to form positively charged bubbles that interact with negatively-charged algal cells, removing the need for pre-treatment (Figure 1-1) (Henderson et al. 2010b). The surface modification of anionic microbubbles, as opposed to particles, has received attention as an alternative to
C-F because of 1) the potential to reduce chemical demand, 2) the absence of metal-salt contamination in the float due to the exclusion of coagulation-flocculation, and 3) an increased solid-liquid ratio. In early PosiDAF investigations the separation effectiveness that was achieved was comparable to that of the traditional process for certain microalgal species (Henderson et al. 2010b), thus demonstrating proof of concept for this new approach. However, challenges were observed including a positively charged effluent which indicated that a significant concentration of polymer was present in the effluent (Henderson et al. 2010b) although a direct measure of polymer concentration in treated effluent was not conducted. To combat this challenge, it was concluded that a review of polymer design was warranted to investigate if polymers could be designed to adhere better at the bubble surface; the polymers used had not been designed to modify bubble surfaces.
In an attempt to improve adherence of the polymer to the bubble, functionalisation of poly(N, N-dimethylaminoethyl methacrylate) (PDMAEMA) with hydrophobic groups was undertaken to anchor the polymer to the microbubble (Yap et al. 2014a). When these hydrophobically modified polymers (HMPs) were applied in PosiDAF on a bench scale, over 95% cell separation was achieved for a strain of Microcystis aeruginosa CS-
564/01. However, a positively charged effluent was still observed, indicating again that a significant proportion of the polymers were not closely attached to the bubble surface
(Yap et al. 2014b). Furthermore, when trialled on a pilot scale plant and contrasted against commercially available poly(N,N-diallyl-N,N-dimethylammonium chloride) ~ 2 ~
Chapter 1 Introduction
(PDADMAC), the latter was found to perform better than the HMPs of PDMAEMA, attributed in part to its enhanced positive charge (Yap 2013). These outcomes indicate that further research into HMP generation using PDADMAC is necessary to optimise
PosiDAF efficiency.
The separation effectiveness observed when using polymers as bubble surface modifiers is much higher than can be modelled using the white water model, which is commonly applied to model conventional DAF performance (Haarhoff et al. 2004, Henderson et al. 2010b), indicating that complex interactions are involved. However, the underlying mechanisms behind polymer-bubble interactions that drive PosiDAF separation remain unidentified. In previous studies, only limited information of the polymer-bubble interaction was reported via bubble charge and conventional surface tension measurements (Yap et al. 2014b) which limited elucidation of the mechanisms involved beyond confirming that HMPs of PDMAEMA were adhering to some degree at the bubble surface. Recently, atomic force microscopy (AFM) has been used to study structural forces between bubbles in polymer solutions such as sodium poly(styrene sulfonate) (Milling 1996, Browne et al. 2015b, Browne et al. 2015a). This suggests that the application of AFM to study polymer-bubble interactions in PosiDAF would help to better understand the PosiDAF process.
An additional challenge previously observed in PosiDAF studies was that when applying PosiDAF across many species of algae and cyanobacteria, the cell separation efficiency varied significantly (Henderson et al. 2010b, Yap et al. 2014b), a challenge that is also observed with conventional separation (Henderson et al. 2008b, Henderson et al. 2010a, Pivokonsky et al. 2016). For example, when trialling the HMP of
PDMAEMA, greater than 95% separation was observed for Microcystis aeruginosa CS-
~ 3 ~
Chapter 1 Introduction
564/1, while only 35% was for achieved for a different strain, M. aeruginosa CS-555/1
(Yap 2013). This variability was attributed to the presence of algal organic matter
(AOM) which is known to have very different concentration and character depending on the species (Henderson et al. 2008b, Henderson et al. 2008c, Henderson et al. 2010a,
Pivokonsky et al. 2014, Pivokonsky et al. 2016). Previously, it was also discovered that
AOM had the potential to enhance the process, whereby exudates from one strain of M. aeruginosa CS-564/01 could be dosed to improve separation of M. aeruginosa strain
CS-555/1 (Yap 2013). It is therefore likely that the mechanisms governing separation in
PosiDAF are significantly influenced by the interactions between AOM, polymer and the bubbles; however these underlying interactions are yet to be evaluated in detail, as proposed mechanisms to date only remain speculative.
Figure 1-1. A schematic of the (A) conventional DAF and (B) PosiDAF systems.
1.2 Research objectives
The overall aim of this project is therefore to elucidate the underlying mechanisms that govern the PosiDAF process, both in terms of polymer adhesion to bubbles and in polymer interaction with the AOM. In order to achieve the aim, the following objectives were set: ~ 4 ~
Chapter 1 Introduction
• To review the existing research on a) the application of polymers used in
flotation processes and their interactions with bubbles and particles, and b) the
influence of AOM on flotation and identify methods to pinpoint and characterise
components in the AOM that impact PosiDAF performance.
• To synthesise and characterise hydrophobically functionalised polymers of
PDADMAC for microbubble surface modification in algae separation using
PosiDAF and observe how these perform in comparison with previously
developed PDMAEMA based polymers and commercial PDADMAC.
• To study the interactions between the polymer and the bubble at the air-water
interface using AFM.
• To examine the impact of varying AOM composition of several strains of algae
and cyanobacteria on PosiDAF performance.
• To identify the specific components of the AOM that affect PosiDAF
performance and determine whether adding these can enhance performance for
strains that are not well separated by PosiDAF.
1.3 Thesis Synopsis
Initially, a literature review (Chapter 2) was conducted in two parts: (a) the application of polymers in flotation processes to better understand how polymer-bubble interactions influence particle separation, and (b) the influence of AOM on flotation processes. In the first part of the Chapter, key polymer characteristics, bubble properties, and their collective impact on flotation were reviewed. The functional groups that differentiate polymers from each other were studied to lend insight into how they impact underlying mechanisms in flotation. Additionally, surface active properties of polymers were analysed to establish similarities and differences in the ways that they interact to lend
~ 5 ~
Chapter 1 Introduction
insight into potential polymer functionalisation. The second part of the review focussed on the influence of AOM on flotation processes. Characteristics like cell size, organic carbon, protein, and carbohydrate concentrations across several strains were compared and their combined effects on particle separation and more broadly, flotation were studied. Additionally, several techniques to characterise AOM and its components were also reviewed to identify potential methods that would help elucidate the mechanisms governing separation in PosiDAF.
In Chapter 3, an array of hydrophobically-modified PDADMAC polymers was developed and the associated polymer-bubble interactions studied in comparison with previously investigated PDMAEMA-based polymers and commercial, unmodified
PDADMAC. Specifically, hydrophobic groups with varying structures were attached to low Mw PDADMAC, to produce a suite of polymers with very high quaternary concentrations. The range of polymer properties were then investigated by tensiometry and charge demand. From these, samples were identified for: (a) atomic force microscopy examination, to lend further insight into the degree of adsorption to the bubble surface and the mechanisms behind polymer-bubble attachment; and (b) jar tests to examine the polymer performance for the separation of algal and cyanobacterial cells.
From these results, the mechanisms behind polymer-bubble interactions in PosiDAF were able to be described, and it was identified that PDADMAC-based polymers had a different interaction at the bubble surface that PDMAEMA-based polymers.
In Chapter 4, an investigation of the role of AOM in enhancing the separation of algae and cyanobacteria by PosiDAF and the interactions of AOM with polymers is presented. Previous work showed that during PosiDAF jar testing, the AOM extracted from M. aeruginosa CS-564/01 could be used to enhance the cell removal of another
~ 6 ~
Chapter 1 Introduction
strain of M. aeruginosa CS-555/1. In this thesis, previous research was extended to two strains of green algae, C. vulgaris CS-42/7 and M. homosphaera CS-566/01.
Furthermore, detailed investigation of the AOM, including its acidic carbohydrate content and Fourier transform infrared spectroscopy analysis of the web-like strands that were previously found to enhance the flotation of M. aeruginosa CS-564/01 was conducted to give further insight into the interactions. Key differences in the AOM were related to the biopolymer concentrations such as carbohydrates and proteins, as well as the associated charge. Particularly, the presence and concentration of acidic carbohydrates and their potential conjugation with the proteins in the strands were theorised to impact PosiDAF performance. Finally, in consideration of all the data, the mechanisms behind the interactions of polymers coated bubbles with the cells and
AOM were established.
In Chapter 5, the impact of the interplay of carbohydrates and proteins on cell separation was studied further. Initially, a complete protein and carbohydrate characterisation was conducted to identify the carbohydrates and proteins that take part in glycosylation. Based on these results, jar tests were conducted on C. vulgaris CS-
42/7 two strains of M. aeruginosa (CS-564/01 and CS-555/1) to determine the impact of specific carbohydrates and protein compositions on separation. It was determined that by dosing specific biopolymers cell separation could be enhanced to close to 100%, but only in the absence of native AOM.
The main findings of this thesis are summarised and discussed in Chapter 6. As a result of the greater depth of understanding of underlying PosiDAF process mechanisms, recommendations to future research directions and improvements to enable practical application of the PosiDAF process are described.
~ 7 ~
Chapter 2
LITERATURE REVIEW
2.1 Review A: Polymer interactions with bubbles and particles in flotation: Lending insight to the PosiDAF process
Polymers have been used for enhanced separation in different flotation processes across various industrial fields for several decades (Bolto 1995, Bolto et al. 1996, Bulatovic
2007a). They aid in the selective treatment of particles (Bulatovic 2007a), by manipulating properties like particle charge (Bolto et al. 2001, Hankins et al. 2006,
Bulatovic 2007d, Rodrigues et al. 2007, Fang et al. 2010, López-Maldonado et al.
2014) and size (aggregation) (2005, Bulatovic 2007b, Nasser et al. 2007, Nasser et al.
2013, Gonzalez-Torres et al. 2014) to facilitate flotation by improving particle attachment to microbubbles (Derjaguin et al. 1993). In addition to altering particle properties, polymers can also influence the bubble charge (Malley 1995a, Uddin et al.
2013, Yap 2013, Yap et al. 2014b) and size (Oliveira et al. 2010a, Oliveira et al. 2012,
Oliveira et al. 2014), thereby creating further scope for their use in flotation applications
(Henderson et al. 2009b, Henderson et al. 2010b). By modifying the properties of bubbles and particles, polymers significantly influence the mechanisms governing particles separation. For instance, polymers facilitate the aggregation of suspended particles in a water treatment process by altering the surface charge (Malley 1995a,
Jameson 1999, Henderson et al. 2008d, Henderson et al. 2009b, Henderson et al.
2010b), in mineral flotation by altering hydrophobic interactions (Liu et al. 1994,
Schreithofer et al. 2011, Yang et al. 2014a) and in a deinking process by a combination
Chapter 2A Literature review (Polymers & bubbles)
of these (Moon et al. 1998, Lamminmäki et al. 2011, Nedjma et al. 2013). As a result, polymers have been applied in flotation processes across various fields.
The two flotation processes in which polymers are used to separate particulate matter include dissolved air flotation (DAF) and induced air flotation (IAF). In both, a range of polymers have been used to enhance flotation where they are referred to as flocculants or collectors and depressants, depending on whether they are applied for water treatment or mineral and deinking applications, respectively. In mineral and deinking, collectors and depressants are known to modify the properties of particles (Klimpel
1988, Pugh 1989, Bulatovic 2007c, Araujo et al. 2008, Huang et al. 2013, Filippov et al. 2014) and, in the case of frothers, encourage the production of a stable float (Pearse
2005, Ata 2012). Due to comparable chemical configurations, many of these polymers can be multifunctional. For example, while amine and quaternary ammonium containing polymers and their derivatives have been used as coagulants in DAF and IAF for the neutralisation of particle and colloidal in water treatment (Galil et al. 1992, Al-
Muzaini et al. 1994, Zlokarnik 1998, Galil et al. 2001, Edzwald 2010, Razali et al.
2011), the same type of polymers have also been used as collectors for ore beneficiation
(Ding et al. 2007, El-Midany et al. 2008, Forbes 2011, Vigdergauz et al. 2012), oily water treatment (Bratskaya et al. 2006), and as depressants in mining industries (Kerr et al. 1994, Bulatovic 2007e). As a result of their multifunctionality, polymers have been widely implemented throughout a range of applications in different fields.
More recently, the changing trends in the fields of water treatment and mineral processing have seen the use of polymers to modify bubble surfaces as opposed to particle surfaces. Specifically, cationic polymers have been used to modify bubble surfaces and these have been proposed to improve bubble-particle interactions as well as
~ 9 ~
Chapter 2A Literature review (Polymers & bubbles)
the robustness of DAF (Malley 1995a, Haarhoff et al. 2004, Henderson et al. 2009b,
Henderson et al. 2010b). Using a modified bubble has the potential to alleviate the process dependency on particle pre-treatment, which is crucial for the success of conventional DAF (Edzwald 2010).
Overall, the purpose of this review is therefore to investigate the application of polymers in flotation to lend insights into the interaction mechanisms that take place during flotation and, in turn, the influence of polymers on bubble-particle interactions.
2.1.1 Flotation principles: Current understanding
A bubble in solution has been described to have three contact zones (Figure 2-1): 1) the bulk solution zone where hydrodynamic forces (>150nm) predominate; 2) the diffusiophoretic zone where electrostatic and hydrophobic (~150 nm) interactions are predominant and influence particle attachment; and, 3) the wetting perimeter where van der Waals (<10nm) and structural forces (<10nm) predominate (Craig et al. 1993,
Derjaguin et al. 1993, Ducker et al. 1994, Ralston et al. 1999). The theory behind this has been discussed in length in prior studies (Derjaguin et al. 1969, Derjaguin et al.
1987, Derjaguin et al. 1993). In short, bubble-particle attachment occurs through the thinning of the hydrophobic wetting perimeter and its subsequent rupture. This phenomenon can be influenced by the electrostatic forces as both bubbles and particles are usually charged in solution. In the absence of chemicals such as coagulants, the bubble surfaces typically have a negative charge in water ranging from -20 mV to -55 mV at pH 7 (Tabor et al. 2012). This is due to the asymmetric dipoles of water molecules that reside at the gas-liquid interface (Engel et al. 1997, Paluch 2000). Once charge interactions are overcome and the particle approaches the wetting perimeter, the formation of a finite contact angle at the gas–liquid–solid contact line called the Three ~ 10 ~
Chapter 2A Literature review (Polymers & bubbles)
Phase Line (TPL) (Figure 2-1) takes place, leading to film drainage (Ralston et al. 1999,
Albijanic et al. 2010). This film drainage is a kinetic phenomenon, caused by the flow being greatest at the very edge of the film in the initial stages of drainage eventually leading to attachment. Sometimes, the film drainage is incomplete owing to a structural force, known as disjoining pressure or hydrodynamic repulsion (Derjaguin 1984). The attachment of a particle to a bubble is successful only when these forces are overcome and the wetting perimeter of the bubble is ruptured (Derjaguin et al. 1993). However, it has also been demonstrated that the availability of long range electrostatic forces can sometimes facilitate ‘contactless’ flotation (Jiang et al. 2010). The premise behind this is that the formation of a TPL and a stable wetting perimeter is not always necessary for the attachment of fine particles, since they can be ‘fixed’ at the bubble surface by long range electrostatic attraction. This theory has been demonstrated by varying the electrolyte concentration and pH of the solution which consequently resulted in
‘attachment’ of alumina particles to N2 bubbles in the absence of film rupture (Jiang et al. 2010).
Figure 2-1. Different zones around a bubble influencing its interaction with other particles
(Remade from Derjaguin et al. (1993)). ~ 11 ~
Chapter 2A Literature review (Polymers & bubbles)
2.1.2 Polymer application for pre-treatment of particles to optimise bubble-particle attachment
The application of polymers for pre-treatment to modify particle properties to optimise flotation across different fields has been reviewed extensively through numerous studies
(Bolto 1995, Yates et al. 2001, Bolto et al. 2007). Briefly, in DAF and IAF, pre- treatment typically involves coagulation of particles by charge neutralisation and flocculation of particles by polymer bridging (Hankins et al. 2006, Nasser et al. 2007,
Fang et al. 2010, Ariffin et al. 2012, Zhang et al. 2014). This is done so that the electrostatic repulsion between the particles is reduced to facilitate aggregation and, subsequently, to reduce particle numbers while increasing both the relative size of particle aggregates and hydrophobic sites, allowing particle collection by bubbles to be non-specific and effective (Colic et al. 2005, Edzwald 2010, Rytwo et al. 2014). It has been observed that the greatest effect on coagulation is through the manipulation of the electrical double layer (EDL) by: (a) altering the pH as EDL compression or expansion is dependent on the ions in solution (Sharp et al. 2005), (b) choice of polymer functional group (Herrera Urbina 2003, Ghimici et al. 2014), and (c) changing the concentration of polymer dosed in the system (Somasundaran 1980). For instance, it was seen that increasing the pH to 10 resulted in over 80% recovery of hematite using nanocellulose whereas hematite recovery was less than 20% at pH 6 for the same polymer (Carlson et al. 2013, Laitinen et al. 2014). Hence, in the context of flotation, coagulation can be greatly optimised prior to flotation by simply manipulating the EDL and ensuring that the polymer gets adsorbed onto the particle.
Floc growth post coagulation is predominantly encouraged via slow mixing, i.e. the flocculation process, which can be further encouraged through the addition of a
~ 12 ~
Chapter 2A Literature review (Polymers & bubbles)
polymeric flocculant to enable polymer bridging. To maximise the projection of polymer chains into the system, strong adsorption due to the electrostatic force is kept at a minimum, by dosing a coagulant prior to- or in combination with the flocculant (Joo et al. 2007, Irfan et al. 2013), or using a charged flocculant (Mishra et al. 2005, Razali et al. 2011). For instance, high Mw non-ionic polyacrylamides (PAMs) have been applied as flocculants with over 95% efficiency in deinking old newsprints after initially treating the pulped paper with alum (Sun et al. 2007). However, for certain synthetic flocculants such as PDADMAC (Razali et al. 2011), and naturally occurring plant based bio-flocculants like Plantago psyllium mucilage (Mishra et al. 2005) and Tamarindus indica mucilage (Mishra et al. 2006), charge neutralisation and bridging have been observed to occur concurrently without the addition of a coagulant due to the charge of the polymer. Recent investigations also suggest that using charged flocculants can result in lower chemical consumption in comparison to dosing a coagulant and flocculant
(Sarika et al. 2005, Chong 2012), generation of low sludge volume because the flocs formed with the strong bridging mechanism are dense and closely packed (Sarika et al.
2005, Lee et al. 2014a) and easy disposal of sludge, leading to reduction of overall treatment cost (Chong 2012). For instance, the preliminary cost analysis of a palm oil mill effluent showed that the total treatment cost while using a coagulant and flocculant was 3.6 times higher than when a single charged polymer was used due to larger volumes of sludge produced in the former process (Chong 2012). Owing to the aforementioned factors, it is therefore important to analyse the feed suspension and choose the right polymer prior to applying polymers for particle modification
(coagulation-flocculation) in flotation applications.
In contrast to conventional pre-treatment where the polymers and particles are oppositely charged, studies have shown that polyelectrolytes with same charge as ~ 13 ~
Chapter 2A Literature review (Polymers & bubbles)
particles, as in the case of anionic particles and anionic polymers, can be flocculated by polymer bridging (Dorrepaal et al. 1979, Caskey et al. 1986, Bolto 1995, Nasser et al.
2007, Abro et al. 2013, Nasser et al. 2013). For example, when several cationic and anionic PAMs with Mw of over 10 million g/mol were applied in flotation of negatively charged kaolinite particles, it was found that use of the anionic PAMs resulted in larger floc structures and better sedimentation rates than when cationic PAMs were applied
(Nasser et al. 2006). Electrostatic repulsion between the negatively charged particles and anionic polymers allow only a limited amount of polymer to adsorb which leads to an expansion of the polymer chain and consequently large and open flocs (Bolto et al.
2007). This type of pre-treatment is not commonly seen in all flotation fields because there has been some evidence that the efficiency of anionic PAMs in flotation can be significantly reduced in the presence of multivalent ions even at concentrations as low as 10-4 M (Henderson et al. 1987). To illustrate, while floating kaolin using 30% anionic
PAMs, flocculation efficiencies reduced to less than 5% due to complex interactions between Al+3, Fe+3, Pb+2, Cu+2, and Zn+2 ions and the carboxylic groups on the PAMs.
There are no specific interactions of these polymers to targeted particle surfaces to ensure selectivity and therefore, more research needs to be done to optimise this mechanism.
2.1.3 An alternative to particle modification: chemically modifying bubble surfaces
Advances in flotation science have demonstrated that the chemical modification of bubbles in flotation processes such as DAF could reduce the process dependency on pre-treatment such as C-F which employs particle modification (Malley 1995a, Edzwald
2010, Bui et al. 2015). At first, chemically altered bubbles were generated using metal
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Chapter 2A Literature review (Polymers & bubbles)
salts, resulting in bubbles with positive zeta potentials (Li et al. 1991, Dockko et al.
2004, Han et al. 2006b). However, it was observed that the adsorption of metal salts onto bubble surfaces were specific to some metals; for example, Na+ (Li et al. 1991) and K+ ions (Han et al. 2006b) did not to adsorb onto bubble surfaces. Surfactant modified bubbles known as colloidal gas aphrons (CGAs) have been generated and used successfully and extensively for the separation of a range of particles, such as dyes (Roy et al. 1992), proteins (Jauregi et al. 1998), and silica particles (Zhang et al. 2017).
However, the CGAs use high concentrations of surfactants as the surfactant stabilised bubbles are surrounded by a layer of water or aqueous solution, which is further encapsulated by a surfactant vesicle (Hashim et al. 2012); this would potentially result in large volumes of chemically concentrated sludge, especially in water treatment applications.
A wide array of polymers has also been trialled for modifying bubbles in the DAF process (Henderson et al. 2009b, Oliveira et al. 2011, Yap et al. 2014b). As the bubble surfaces are negatively charged, cationic polymers are typically used for the purpose of bubble modification as they easily and readily adsorb onto the bubble through electrostatic interactions (Malley 1995a, Henderson et al. 2010b, Oliveira et al. 2012,
Henderson et al. 2015). Several cationic polymers have been trialled successfully to modify bubbles and the resulting bubble zeta potentials can be found in Figure 2-2.
Modified bubbles have also been generated through the addition of anionic, non-ionic, and amphoteric polyacrylamides and the polymer-coated bubble charge when using these polymers was reported to be more negative than an unmodified bubble (Figure 2-
2) (Oliveira et al. 2011). This is because these polymers are known to behave differently with bubbles in comparison to cationic polymers; while cationic polymers strongly adsorb onto bubbles through charge interactions (Malley 1995a, Henderson et al. ~ 15 ~
Chapter 2A Literature review (Polymers & bubbles)
2010b), anionic, amphoteric, and non-ionic polymers adsorb onto the bubbles through hydrogen bonding, and the polymer functional groups project into the solution (Oliveira et al. 2011). The research on using anionic polymers as bubble modifiers is at a nascent stage and has a big scope in mineral processing, especially for separating positively charged particles (Oliveira et al. 2012). However, in the context of bubble modification for water treatment and algae harvesting purposes, cationic polymers are still widely used and preferred as the contaminants are negatively charged (Henderson et al. 2009b,
Henderson et al. 2015).
Figure 2- 2. Plot of zeta potential ranges of various polymer coated bubbles at pH 6-8.
(Malley 1995a, Kim et al. 2000, Oliveira et al. 2011, Yap et al. 2014b) ~ 16 ~
Chapter 2A Literature review (Polymers & bubbles)
The application of surface-modified bubbles in the DAF process has been investigated through several studies; for example, Han et al. (2006a) determined an enhanced removal of negatively charged kaolin particles using Al+3 coated bubbles via electroflotation. Henderson et al. (2008d) dosed surfactants such as cetyl trimethylammonium bromide and observed strong surfactant-bubble interactions even at
1/100th of the CGA concentrations while simultaneously achieving 65% cell separation without pre-treatment by C-F for the cyanobacteria M. aeruginosa. When the same methods were applied for the removal of NOM using polymers such as PDADMAC, the efficiency achieved was greater than 90% and comparable to conventional DAF with pre-treatment by C-F (Malley 1995a). Furthermore, the polymer concentrations used in the modified bubble DAF process have been demonstrated to be 1/10th of those used while dosing surfactants and the solid-liquid ratio was significantly higher than that obtained for conventional DAF process (Henderson et al. 2010b). Therefore, the utilisation of polymers to modify bubbles in the DAF process is gaining traction across multiple fields such as water treatment for NOM removal (Malley 1995a, Shi et al.
2017), mineral processing (Han et al. 2006a, Oliveira et al. 2012), and algal harvesting where it is known as the PosiDAF process (Henderson et al. 2010b, Yap et al. 2014b).
While the application of cationic polymers in modified-bubble DAF has shown promise, their uptake in the industry has been slow due to the poor polymer-bubble interactions resulting in a contamination of the treated effluent with polymer residuals
(Henderson et al. 2010a, Henderson et al. 2015). For instance, Henderson et al. (2010b) observed that PDADMAC treated effluents had highly positive zeta potentials compared to an anionic influent indicating that a high concentration of the dosed polymers were present in the treated effluent. Previously, the application of surfactants in the PosiDAF process as well as in CGAs suggested that the presence of hydrophobic ~ 17 ~
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functionalisation in the polymer backbone could anchor the polymer to the bubble surface and potentially increase robustness of the modified-bubble DAF process. By incorporating hydrophobic alkyl groups on poly(N,N-dimethylaminoethyl methacrylate)
(PDMAEMA) backbone, positively charged bubbles were generated (Figure 2-2) and bench scale tests successfully limited polymer contamination of the treated effluent while maintaining greater than 95% process efficiency (Yap et al. 2014b, Henderson et al. 2015). However, their application in pilot scale resulted in less than 50% cell separation, whereas commercially available PDADMAC increased separation to greater than 90%, attributed partly to PDADMAC’s enhanced charge (Yap 2013). Up to now, the understanding of the factors and mechanisms that result in the working of the modified-bubble DAF process is limited even with tests confined to well-defined laboratory conditions. Therefore, it is essential that factors impacting polymer-particle- bubble interactions and flotation efficiency are studied as they represent a critical gap in knowledge that needs to be addressed for advancing the technology.
2.1.4 Factors impacting polymer-particle-bubble interactions and flotation efficiency
A diverse range of polymers have been used for particle modification across multiple fields (Bolto et al. 1996, Bulatovic 2010); herein, these studies have been catalogued into common factors. These categories for describing the diversity in particle modification are based on polymer molecular weight, charge, functional groups, and hydrophobic tendencies as these can influence the way in which they behave in flotation systems. These categories will be analysed in detail to understand the fundamental similarities in their characteristics.
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2.1.4.1 Polymer molecular weight and charge
Polymer Mw and charge density are important properties that impact polymer-particle and polymer-bubble interactions in flotation. The polymer Mw is usually determined by gel permeation chromatography (Flory 1953) while the charge density is usually ascertained by colloid titration (Kam et al. 2001), or more recently via streaming current detection during colloid titration (Böckenhoff et al. 2001, Lei et al. 2014, Rytwo et al.
2014, Yap et al. 2014b). The charge density of a polymer can be expressed in terms of milliequivalents per gram (meq/g) or through mole percent. For example, while treating suspended organic matter using poly(acrylamide)-co-poly(dimethylaminoethyl acrylate) with a cationic charge of 30 mol%, the charge (2.8 meq/g) was calculated from the formula weight of the monomer units (71 g/mol for acrylamide and 194 g/mol for quaternised DMAEA) (Bolto et al. 2007). The calculation of charge density from mole percent is based on the assumption that the polymer segments are strongly ionic. For the ease of classification, the charge density in this chapter will be referred to in mole% unless otherwise stated. The charge is considered to be low, medium or high if the charge density is 10%, 25-50% and >50%, respectively (Bolto et al. 2007).
In polymer-particle interactions, Mw and charge density have influence on the way particle aggregation takes place. From Table 2-1 and Figure 2-3, it can be seen that polymers with Mw < 1 million g/mol and charge density < 15 mol% facilitate bridging, while those that had charge density > 15% facilitated charge neutralisation. For polymers with Mw > 1 million g/mol and charge > 15%, either charge neutralisation or bridging or a combination of both took place (Figure 2-3). To illustrate, copolymers of poly(acrylamide)-co-poly(dimethylaminoethyl arcylate) (K-PAM-co-DMAEA and I-
PAM-co-DMAEA) that were 10% and 30% charged were shown to result in bridging,
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and charge neutralisation and bridging, respectively, while removing silica particles
(Zhou et al. 2006) (Figure 2-3). Figure 2-3 can also be used to classify polymers based on their functions. For instance, from Figure 2-3, it can be understood that K-PAM-co-
6 DMAEA is a flocculant due to its high Mw (2x10 g/mol) and 10% charge. This is because the linear polymers with high Mw and low charge have greater tendency to extend into the system in the form of loops and tails due to low adsorption on the particle (Bolto et al. 2007). Polymeric collectors and depressants, such as poly(N- isopropyl acrylamide) (PNIPAM) (Burdukova et al. 2010) and carboxymethyl cellulose
(Morris et al. 2002), have low to medium Mw but charge densities that are at least 75 mole% as compared to flocculants (Table 2-1). This is because the primary role of collectors and depressants are to adsorb on a particle to tailor its properties to make it suitable for flotation or depression (Bulatovic 2010).
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Figure 2- 3. Charge density vs molecular weight of common polymers used in flotation
(Kam et al. 2001, Cao et al. 2002, Ramos-Tejada et al. 2003, Burdukova et al. 2010,
Henderson et al. 2010b, Nunes 2011, Nasser et al. 2013, Yap et al. 2014b)
As previously discussed, bubbles in water are negatively charged. Due to this, several cationic polymers have been reported to modify bubbles readily in comparison to fewer anionic, non-ionic, or amphoteric polymers (Figure 2-2). When the zeta potential of polymer-coated bubbles was analysed, it was found that it varied with every polymer trialled (Figure 2-2). For instance, the zeta potential of the bubbles coated with
PDMAEMA derivatives were found to be between +30-75 mV while those of
PDADMAC were found to be +40-58 mV (Figure 2-2). A closer inspection of the studies revealed that the bubble zeta potentials were influenced by the polymer charge density and Mw. For instance, it was observed that the bubble zeta potentials were found to be more positive for cationic polymers with Mw < 100,000 g/mol in comparison to
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polymers with Mw > 100,000 g/mol (Oliveira et al. 2010b, Oliveira et al. 2011, Yap et al. 2014b). Contrastingly, for anionic and non-ionic polyacrylamides, it was demonstrated that bubble zeta potentials were more negative for polymers that had Mw >
1 x 106 g/mol (Oliveira et al. 2010b, Oliveira et al. 2011). In another study, Yap et al.
(2014b) noted that PDADMAC (+ 6.8 meq/g) which had a higher charge density than
PDMAEMA derivatives (+ 1 to + 2.5 meq/g), had lower modified bubble zeta potentials in comparison to some PDMAEMA derivatives (Figure 2-2). In addition to their impact on polymer-bubble interactions, the influence of the polymer charge density and Mw was also visible during flotation of particles using polymer-coated bubbles. For instance, when high Mw PDADMAC (Mw > 300,000 g/mol) was used as the bubble modification chemical, it resulted in heavy flocs that did not allow a stable float layer to be developed, thereby impacting the process efficiency. In another study, when applying
PDADMAC and PDMAEMA derivatives in a pilot PosiDAF plant, the latter resulted in less than 50% cell separation, whereas the former increased separation to greater than
90%, attributed partly to its enhanced positive charge in comparison to the PDMAEMA polymers (Yap 2013). In the context of bubble-modified flotation processes such as
PosiDAF, tuning polymer properties such as charge density and Mw is a key step towards overall process optimisation.
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Chapter 2A Table 2- 1. The structure and characteristics of common polymers used in flotation applications
Polymer Used Functional General Structure Charge Density MW Type Reference(s)
Group mole%, meq/g g/mol
(Bolto 1995, Busscher et al. 1995, PDADMAC Quaternary 45 - 100%, 3.2-6.3 105– 2x106 Collector, Ramesh et al. 1997, Graciaa et al. 2002,
ammonium Flocculant Hankins et al. 2006, Ridaoui et al. 2006, Sun et al. 2007, Sis et al. 2009, Ariffin et
al. 2012, Karhu et al. 2014)
5 6 Quaternised Quaternary 40 - 70%, 0.97-1.7 10 - 10 Collector, (Ramesh et al. 1997, Yap 2013, Yap et al. Literature review(polymers & PDMAEMA ammonium Flocculant 2014b)
~ 23 Poly(acrylamide Amine 5 - 70%, 0.7-9.72 105– 4x106 Collector, (Attia 1977, Vulliez et al. 1983, Bolto ) Flocculant 1995, Chen et al. 2006, Nasser et al. 2006, F. H. Abd El-Rahiem 2010, Franks et al.
2010, Ariffin et al. 2012) 5x103- Poly(ethyleneim Amine 40 - 70% 2x106 Flocculant (Dixon et al. 1974, Mekhamer et al. 2009, ines) Wang et al. 2013, Duan et al. 2014)
(Boddu et al. 2003, Sajomsang et al. 2009, bubbles) Chitosan Amine and 20-50%, 1.2-1.5 105 – Collector, Han et al. 2012, Huang et al. 2013, Hydroxyl 1.5x106 Flocculant Khaldoun Al-Sou’oda 2013, Yang et al.
group 2013, Kurniawati et al. 2014)
Chapter 2A Polymer Used Functional General Structure Charge Density Mw Type Reference(s) Group (mole%, meq/g) (g/mol)
Starch Hydroxyl 20 - 40%, 1.2-2.4 104 – 105 Depressant (Bolto 1995, Jankowski et al. 1999, Song
group et al. 2009, Petzold et al. 2012, Yang et al. 2014b)
Poly(vinylsulfo Sulfonic acid 5 – 75%, 0.5-7.5 105 – 106 Collector (Dorrepaal et al. 1979, Sadowski et al.
nic acid) 1987, Bolto 1995)
5 6 Poly(allylsulfon Sulfonic acid 5 – 75%, 0.5-6.25 10 – 10 Collector (Dorrepaal et al. 1979, Sadowski et al. Literature review(polymers & ic acid) 1987, Bolto 1995)
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Dextrin Hydroxyl 5 - 40%, 0.35-1.81 105 – Depressant (Beaussart et al. 2009a, Beaussart et al. group 7x105 2009b, Beaussart et al. 2012, Mierczynska-Vasilev et al. 2013)
Carboxymethyl Hydroxyl 2.33c 105 – Collector, (Barck et al. 1994, Cao et al. 2002, Wang cellulose group 7.5x105 Depressant et al. 2005, Parolis et al. 2008, Zhivkov 2013)
a: N-acetylchitosan at 1.3-4 mg/ml; b: for 0.3-0.6 and 0.25 DS with cationic charge and hydrophobic groups respectively; c: for DS 1.2; bubbles)
Chapter 2A Literature review (Polymers & bubbles)
2.1.4.2 The impact of polymer functional groups on flotation
Since many natural colloidal particles are negatively charged, cationic polymers are widely used in flotation applications and, in most cases, amine functional groups on the polymer backbone supply the charge. These have been reviewed extensively (Bolto
1995, Bolto et al. 1996, Bulatovic 2010). Over 90% of the synthetic flocculants currently used for flotation applications are PAMs and their derivatives (Pearse 2005).
This is predominantly due to: (a) the high reactivity of monomer as a result of the high kp/kt constant that enables molecular weights in the order of millions (Kurenkov 2002,
Karmakar 2006); (b) the simple linear structures (they are polymerised from acrylamide monomer or acrylic acid which is the simplest known unsaturated carboxylic acid); (c) ease of conversion into quaternary ammonium groups (Neoh et al. 1993, Bolto 1995); and (d) grafting of PAM branches on other polymer backbones or vice versa (Attia
1977, Vulliez et al. 1983, Chen et al. 2006, Xu et al. 2008, Song et al. 2009, Yang et al.
2013, Yang et al. 2014b). As a result, PAMs have been widely studied and implemented in multiple fields such as water treatment (Kurenkov 2002, Yang et al. 2013), mineral flotation (Attia 1977, Ding et al. 2007, Englert et al. 2009) and paper deinking (Miranda et al. 2008, Miranda et al. 2009, Zhang et al. 2011).
PAMs have been found to be compatible with other polymers when dosed together in flotation applications (Lemanowicz et al. 2011a, Lemanowicz et al. 2011b, Ariffin et al.
2012). These are referred to as dual polymer systems in which a combination of a low
Mw-highly charged polymer with a high Mw polymer results in effective coagulation- flocculation. For instance, dual polymer systems are widely used in sludge dewatering
(Lee et al. 2000, Lee et al. 2001, Glover et al. 2004) and mineral processing applications (Fan et al. 2000). The benefits of dual polymer dosing were demonstrated
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by the 95% turbidity reduction and stable alumina flocs when PDADMAC and poly(acrylic acid) were used in conjunction in comparison to less than 30% turbidity reduction and weak alumina flocs when only poly(acrylic acid) was trialled (Fan et al.
2000). However, the focus of the research was on the interactions of polymers with alumina particles rather than direct interactions with bubble surfaces (Fan et al. 2000).
2.1.4.3 The use of branched polymers in flotation
The use of branched polymers has been recently promoted because they combine the efficiency of synthetic polymers and are also environmentally friendly (Pal et al. 2006,
Lee et al. 2014b). However, branched polymers have seen very limited use in flotation applications possibly due to difficulties in solubility as a result of cross-linking during synthesis (Odian 2004). Recently, synthetic polymer grafting to natural polysaccharide polymers to form branched polymers has been explored as a way of combining the best attributes of natural and synthetic polymers for flotation applications (Song et al. 2009).
For instance, grafting PAMs onto amylopectin (Singh et al. 2013) and starch (Song et al. 2009) for the purposes of influent particles modification resulted in greater than 90% removal of suspended solids, chemical oxygen demand and colour in comparison to
70% achieved using non-grafted synthetic PAMs in DAF. In another DAF study, interaction of acid black, reactive and direct reds with hydroxymethylated lignins that were synthetically grafted with dimethylamine, acetone and formaldehyde resulted in almost 100% flotation efficiency (McKague 1974, Fang et al. 2010). This has been attributed to the presence of dangling polymer chains that ‘catch’ the suspended substances (Yang et al. 2013). All the work discussed so far has been in relation to particle modification. Branched polymers have not been used for modifying bubbles in flotation processes; however, the success which has been seen for particle modification
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suggests that branched polymers could be trialled for bubble modification in processes such as PosiDAF, providing a less toxic alternative to existing polymers such as alkyl substituted PDMAEMA derivatives that were used by Yap et al. (2014b).
2.1.4.4 Influence of post-polymerisation modification in polymers on flotation efficiency
Several studies have shown that post-polymerisation modification techniques such as quaternisation (Sajomsang et al. 2009), chelation (Attia 1977, Diallo et al. 1999) and protonation (Neoh et al. 1993, Bolto 1995, Kim et al. 2014) can be carried out to vary polymer properties such as the charge density to improve polymer-particle interactions and consequently, the flotation efficiency. For example, through protonation of amines in chitosan, over 85% removal of Cr (VI) ions (Boddu et al. 2003) and 90% removal of bentonite (Chatterjee et al. 2009) was achieved due to the elevated charge aiding polymer-particle complexation, in comparison to less than 50% removal when applying unprotonated chitosan. Similarly, by quaternising the amines on chitosan with C-12 groups and consequently varying the charge, over 90% separation efficiency was obtained when treating oil-in-water emulsions in comparison to only 30% when unmodified chitosan was used (Bratskaya et al. 2006). Post-polymerisation modification in polymers can also influence the hydrophilic-lipophilic balance (HLB) values which in turn can affect polymer-particle interactions. The HLB is a measure of the polar character of surfactants and though it does not directly apply to polymers, the same principles apply (Perrin et al. 1999). For instance, a reduction in HLB to 9 from 15 in poly(ethylene oxide)–poly(propylene oxide) block copolymer (PEO-PPO) increased the hydrophobicity of the polymer, which consequently raised the flotation efficiency of coal to > 80% from < 50% by facilitating hydrophobic interactions between the polymer
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and particle (Polat et al. 1999). Therefore, post-polymerisation modifications can be used as an effective tool to vary polymer properties to achieve better polymer-particle interactions. However, such modifications also have the potential to bring drawbacks during polymer synthesis; for instance, quaternising PDMAEMA with C-15 hydrophobic groups resulted in swollen gels that could not be applied in flotation processes (Yap et al. 2014b).
Post-polymerisation modifications can also impact polymer-bubble interactions and, consequently, the process efficiency. For instance, by quaternising PDMAEMA with hydrophobic alkyl groups, Yap et al. (2014b) demonstrated elevated polymer adhesion to the bubble surface in PosiDAF; this increased the robustness of the process by reducing the contamination of the treated effluent with polymer residuals while maintaining greater than 95% process efficiency in a bench scale. However, the application of PDMAEMA and its derivatives in pilot scale resulted in less than 50% process efficiency in comparison to greater than 90% achieved by using PDADMAC
(Yap 2013). Polymer development for the purposes of bubble modification has been limited to just PDMAEMA and its derivatives. Hence, alternative polymer backbones such as PDADMAC incorporated with different hydrophobic groups through post- polymerisation modification would be beneficial to the optimisation of the process and is worthy of further investigation.
2.1.4.5 The impact of polymer hydrophobicity on flotation efficiency
Hydrophobic polymers aid in the formation of compact aggregates. As the magnitude of hydrophobic interactions is usually one to two orders greater than other forces in the short range of separation, greater concentrations of hydrophobic groups increase attachment to the particles and improve flotation efficiency (Ralston et al. 1999, Yang
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et al. 2014a). As an example, a PEO-PPO block copolymer with over 60% hydrophobic groups resulted in < 10% fibre loss by facilitating stronger polymer-fibre-bubble hydrophobic interactions in comparison to > 40% fibre loss when the polymer had 30% hydrophobic groups during flotation in the paper deinking process (Moon et al. 1998).
Due to the importance of the hydrophobicity of polymers in flotation, several studies have tried to manipulate hydrophobicity of polymers by (a) varying the percentage of hydrophobic groups (Polat et al. 1999, Yap et al. 2014b), and (b) control polymer solubility during flotation (Burdukova et al. 2010, O’Shea et al. 2011). For instance, in the context of bubble-modification, it was found that polymers modified with a higher proportion of hydrophobic compounds resulted in decreased bubble adherence (Yap et al. 2014b), as determined indirectly via measurement of bubble zeta potentials. Yap et al. (2014) found that bubbles modified with PDMAEMA quaternised with 10% alkyl groups had a statistically more positive zeta potential compared to PDMAEMA quaternised with 50% and 75% alkyl groups (P-value <0.05). However, this needs further investigation as the functionalisation of polymers for the purpose for bubble surface modification has been limited to PDMAEMA and the PosiDAF process only.
In mineral processing applications, polymer-particle adhesion and, subsequently, flotation efficiency have been improved by decreasing polymer solubility. This has been demonstrated by increasing the temperature of poly(N-isopropylacrylamide) (PNIPAM) above its lower critical solution temperature of 35 ± 3 °C (Zhang et al. 1999a,
Burdukova et al. 2010, Franks et al. 2010, Burdukova et al. 2011, O’Shea et al. 2011,
Franks 2013). The increase in temperature causes the hydrogen bonds between water molecules and polymer chains to break and form intra- and inter-molecular hydrogen bonds, which cause the molecule to coil up and expose its hydrophobic core (Zhang et al. 1999a, Burdukova et al. 2010). This renders the polymers hydrophobic, thereby ~ 29 ~
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causing stronger polymer-particle interactions. For example, when dewatering alumina slurries using PNIPAM, it was found that polymer-particle strengths were over 10 times greater at temperatures above 37°C than below it, leading to aggregates that were five times bigger in comparison to those obtained under 37°C (Burdukova et al. 2011). This consequently resulted in greater than 90% separation efficiency (Burdukova et al. 2010,
Burdukova et al. 2011). Hence, manipulation of hydrophobic interactions through polymer solubility control could be used to improve polymer-particle interactions and, therefore, flotation efficiency across multiple fields.
2.1.5 The importance of studying interactions between polymers, particles and bubbles
Analysing flotation performance from the perspective of polymer properties such as hydrophobicity or Mw will only lead to a partial understanding of the outcome due to the importance of other factors such as bubble-particle attachment (Polat et al. 1999,
Beaussart et al. 2009a, Beaussart et al. 2009b, Beaussart et al. 2012). For instance, it has been shown that increased hydrophobicity of hydroxypropyl dextrin in comparison to native dextrin did not necessarily improve the separation of molybdenite from talc; rather, a variation in the particle-bubble attachment efficiency caused by shape of the particles, impacted flotation efficiency (Beaussart et al. 2009a, Beaussart et al. 2012).
While applying polymers for bubble modification, non-ionic polyacrylamide was
6 expected to bridge between kaolin particles and bubbles due to its high Mw (9.6 x 10 g/mol) and increase process efficiency to greater than 90%; however, the separation efficiency obtained was less than 30%, partly attributed to the formation of non- spherical polymer-coated bubbles that were unstable and prone to coalescence (Oliveira et al. 2012). The absence of a clear relationship between the flotation efficiency and
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polymer characteristics in the aforementioned examples indicates that flotation is a product of synergistic interactions between the polymers, bubbles and particles. As the interaction between bubbles and particles is a dynamic process, empirical data such as induction time and thin film drainage or rupture time measurements in conjunction with the polymer properties have served as tools to understand the variations in flotation efficiency (Beaussart et al. 2009b). For example, in the flotation of molybdenite with different dextrins, it was observed that unmodified and moderately hydrophobic dextrin with the least thin film rupture time of 76 ± 17 ms resulted in greater than 80% recovery
(Beaussart et al. 2012). Therefore, it is evident that flotation outcomes should not be attributed to polymer properties alone, but studied along with the interactions between polymers, particles and bubbles. In studies that involve flotation with polymer modified bubbles, this area represents a gap in knowledge and hence, needs to be addressed.
Several studies have applied methods to assist in understanding the fundamental mechanisms behind the interactions between polymer, particles and bubbles (Table 2-2).
These predominantly include microscopic and imaging techniques such as AFM for studying particle-bubble interactions in flotation processes as they can work in versatile environments and require no pre-treatment. For instance, AFM measurements of hydroxypropyl dextrins on molybdenite particles in an aqueous environment revealed that an increase in polymer concentration from 25ppm to 100ppm led to the polymer coils becoming densely packed, thereby decreasing bubble-particle attachment
(Beaussart et al. 2012, Mierczynska-Vasilev et al. 2013) (Table 2-2). In another study,
X-Ray photoelectron spectroscopy demonstrated that a low degree of substitution of hydrophilic groups in carboxymethyl cellulose resulted in over 80% recovery of talc and chalcopyrite (Mierczynska-Vasilev et al. 2013). Other methods such as electrophoretic mobility measurements have demonstrated that the presence of ~ 31 ~
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contaminants such as Ca+2 ions in the suspension increased the unfavourable collector coating on iron particles, leading to poor bubble-particle attachment and low separation efficiency of hematite (Potapova et al. 2012) (Table 2-2). As experimental techniques such as AFM have been proven to lend insights into the mechanisms governing bubble- particle attachment, these have potential to be incorporated to explore the underlying mechanisms governing particle separation when using modified bubble surfaces, such as in PosiDAF.
Table 2- 2. Experimental methods used to study interactions between polymers, particles and bubbles
Experimental Technique Notes Reference(s) Atomic force microscopy Interactions between bubbles, (Beaussart et al. 2009a, polymers and particles studied Beaussart et al. 2012, Tabor et al. 2012, Browne et al. 2015b, Browne et al. 2015a) Single particle flotation Tests done with and without (Beaussart et al. 2009a, experiments polymers Beaussart et al. 2012) ATR-FTIR Spectroscopy Working solution continuously (Potapova et al. 2012) bubbled with argon to minimise
CO2 Thin film rupture imaging Low bubble sizes to prevent (Beaussart et al. 2009a, bouncing and deformation Beaussart et al. 2012) X-Ray photoelectron Done before polymer adsorption (Mierczynska-Vasilev et al. spectroscopy 2010, Mierczynska-Vasilev et al. 2013) Quartz crystal microbalance Used to validate data from AFM (Beaussart et al. 2009a, with dissipation monitoring for determining water content Beaussart et al. 2012) Adsorption isotherm Done by batch depletion method (Cuba-Chiem 2007, Beaussart et al. 2009a, Beaussart et al. 2012) Electrophoretic mobility Zeta potential with and without (Potapova et al. 2012) measurements polymers
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2.1.6 Conclusions and knowledge gaps
In this review, an overview on the use of polymers, both to modify the surface properties of particles and bubbles, and their influence on bubble-particle interaction mechanisms in flotation processes has been provided. The specific conclusions of this review are as follows:
• The types of polymers used in three flotation fields, namely water treatment,
mineral processing and deinking applications, are similar and the mechanisms
by which they interact with particles, and bubbles if examined, are also
comparable.
• Polymer coated bubbles have shown great promise in flotation applications.
Particularly, the PosiDAF process, if optimised, may have application in water
treatment, mineral processing and deinking applications because of its low
chemical demand and environmental footprint.
• Polymer properties such as Mw, charge density and hydrophobicity can impact
polymer-particle and polymer-bubble interactions:
A polymer with a high Mw and low charge density predominantly aids
particle aggregation via bridging rather than charge neutralisation.
Hydrophobicity of a polymer influences polymer-particle and polymer-
bubble adhesion. The greater the hydrophobicity of the polymer, the
stronger is its association with a bubble surface. Similarly, particle floc
strength also increases with the polymer hydrophobicity.
• Analysing flotation efficiency on the basis of polymer properties alone is not
always appropriate because other factors such as bubble-particle attachment play
a major role in the flotation outcome. Hence, experimental techniques such as
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those listed in Table 2-2 should be employed to analyse flotation outcomes in
conjunction with the polymer properties.
From this review, the following gaps in research have been found in relation to the
PosiDAF process:
• Only PDMAEMA and its derivatives have been examined as bubble
modification chemicals; the use of alternative polymer backbones in PosiDAF
requires further investigation.
• The design and application of hydrophobically functionalised polymers in
PosiDAF using non-aliphatic groups is yet to be investigated.
• The impact of post-polymerisation modification on polymer-bubble interactions
has not been effectively quantified.
• Interactions between polymers, bubbles, and particles in PosiDAF have not been
comprehensively analysed.
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2.2 Review B: Algal organic matter: characterisation and influence on flotation
Many processes that use flotation for separation are rich in organic matter. For example, in a drinking water or algae harvesting scenario, the influent water to a dissolved air flotation (DAF) unit contains organic components, termed in water treatment scenarios as dissolved natural organic matter (NOM) (Sillanpää et al. 2015a), which might include humic and fulvic substances (Kosobucki et al. 2014), and/or algal organic matter (AOM) (Bernhardt et al. 1987, Pivokonsky et al. 2006, Henderson et al. 2008c).
The presence of AOM, which will be the focus of Section 2.2, has been shown to directly influence flotation performance by either impacting the particle pretreatment or bubble-particle adhesion. For example, Pivokonsky et al. (2006) and Takaara et al.
(2007) showed that organic matter from M. aeruginosa decreased flotation efficiency by complexing with the coagulant, whereas Bernhardt et al. (1985) demonstrated that low concentrations of AOM can enhance flotation to > 80% by behaving as a flocculant aid for Dictyosphaerium;. More recently, in the PosiDAF process, Yap et al. (2014) noted that AOM from M. aeruginosa had an enhancing effect on cell separation, attributed to large biopolymers acting as flocculants, bridging between bubbles and cells, thus minimising restabilisation of the system despite the positive charge. Therefore, in Part B of the literature review, the influence of AOM on flotation, in addition to the cells themselves, is analysed in depth. Furthermore, techniques to characterise the AOM are evaluated to enable identification of methods that can be applied to gain a better understanding of the underlying mechanisms by which AOM impacts process optimisation in PosiDAF.
Chapter 2B Literature review (AOM& its components)
2.2.1 The influence of algal and cyanobacterial cell characteristics on flotation
Cell characteristics have a large impact on the effectiveness of flotation of algae and cyanobacteria. Particularly, cell morphology, including size, length to width ratios, formation of colonies, and the presence of flagella are all known to affect flotation as they can interfere with cell aggregation (Petrusvenski 1996, Pieterse et al. 1997,
Henderson et al. 2008b, Bui et al. 2015). For instance, the presence of flagella, which are used by some species such as Chlamydomonas reinhardtii for facilitating motility, can impede cell aggregation by facilitating movement of the cells away from flocs
(Petrusvenski 1996, Pieterse et al. 1997). Cyanobacterial cells also have the ability to remain in suspension due to the gas vacuoles present within the cell (John et al. 2002), which results in a higher chemical demand for cell separation, sometimes making the process unsustainable (Mikulec et al. 2015). As an illustration, Yap et al. (2014) noted that the size differences between the UK and Australian strains of M. aeruginosa impacted polymer dose in PosiDAF; as the UK strain was 1.8 times larger than the
Australian strain, it required 1.5 times greater polymer dose to achieve comparable cell separation of 95%.
Cell surface charge is an important property that helps maintain stability of the suspension and, therefore, is crucial in the context of flotation (Henderson et al. 2008a).
The origin of the negative charge on the cell surface is speculated to be due to the dissociation or ionisation of the carboxylic acids in the cell walls, and phosphoric and amine functional groups in the AOM which is frequently attached to the cell surface
(Ives 1959, Konno 1993, Henderson et al. 2008c, Hadjoudja et al. 2010). As cell numbers are usually in millions/ mL, repulsion exists between cells, particularly if they
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come into close contact and, hence, the stability of the matrix is maintained (Konno
1993). The cell surface charge is affected by several parameters including the growth stage of the algal cells and the composition of the associated AOM attached to the cell surface (Bernhardt 1985), thereby influencing the coagulant or polymer dose and impacting flotation. Henderson et al. (2008a) noted that varying composition of AOM between UK strains of M. aeruginosa and C. vulgaris cells resulted in a higher charge density of the of C. vulgaris; this consequently increased the coagulant demand by three times to obtain same flotation efficiencies for both strains. Differences of this scale had not been previously observed and indicate the importance of cell surface charge in comparison to other cell properties.
The charge of algal and cyanobacterial cells is conventionally measured through electrophoretic mobility (zeta potential) (Ives 1959). The zeta potential of algal cells is typically electronegative, ranging from -9 to -40 mV and is dependent on species and pH (Henderson et al. 2008b, Pivokonsky et al. 2016). A reduction of the magnitude of the negative zeta potential via coagulant addition leads to a reduction in the repulsive forces, allowing cells to agglomerate and be separated through flotation.
The use of zeta potential for algae removal by coagulation has been shown to be a promising method in combination with dose and/or pH adjustment, leading to efficient removal of both cells and AOM (Henderson et al. 2008a). In the context of flotation, controlling zeta potential of cell surfaces is critical as bubble surfaces are also negatively charged (Chapter 2A). Therefore, for successful flotation, the repulsive forces between bubbles and cells need to be manipulated carefully for effective bubble- cell attachment to take place (Henderson et al. 2008b, Henderson et al. 2010a).
Sometimes in processes like PosiDAF, it advantageous when the cells have a high
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negative charge as the cell-bubble repulsion is eliminated by using a positively charged bubble (Henderson et al. 2009b). Overall, it has been demonstrated that the impact of cell morphological and physiological characteristics play an important role in the flotation efficiency and need to be given due consideration prior to flotation.
2.2.2 AOM and its components
AOM which is an important component in algal and cyanobacterial suspensions is generated via metabolic excretion or due to autolysis of cells, and can vary between species as well as the growth phase of the cells (Fogg 1983, Villacorte et al. 2015b).
The AOM predominantly comprises biopolymers and low-Mw microbial products
(Ghernaout et al. 2010, Pivokonsky et al. 2014). The biopolymer fraction broadly comprises carbohydrates and proteins. Specifically, more than 60% of the biopolymers comprise hydrophilic substances such as hydroxy acids, low-Mw carboxylic acids, amino acids, amino sugars, peptides, low- Mw alkyl alcohols, aldehydes, and ketones
(Henderson et al. 2008c, Kehr et al. 2015, Pivokonsky et al. 2016). The hydrophobic molecules include high-Mw alkyl amines, alkyl carboxylic fatty acids, aromatic acids, and humic substances (Edzwald 1993, Henderson et al. 2008c, Pivokonsky et al. 2014,
Pivokonsky et al. 2016).
2.2.2.1 Algal and cyanobacterial carbohydrates
The types and concentrations of algal and cyanobacterial polysaccharides (long chain of carbohydrates) and their Mw distributions are highly species-specific. Generally, these polysaccharides typically consist of repeating units built from monosaccharides that result in molecules that are several hundred kDa in size, with the largest molecules reaching Mw of 2 MDa (Vicente‐Garcia et al. 2004). The repeating units are typically made from five to eight monosaccharides, but a few cyanobacteria such as ~ 38 ~
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Synechocystis sp. exhibit a much higher complexity with repeating units comprised of up to 15 monosaccharides (Pereira et al. 2009). No known explanations exist to explain this phenomenon and this is considered a unique feature of a few strains of cyanobacteria, since other algae and cyanobacteria usually possess carbohydrate polymers that contain up to four to six monosaccharide building blocks only
(Templeton et al. 2012b). In terms of composition, algal and cyanobacterial polysaccharides have been observed to comprise various hexoses (fructose, galactose, glucose and mannose), pentoses (arabinose, ribose and xylose) and deoxyhexoses
(fucose and rhamnose), as well as the acidic sugars such as glucuronic and galacturonic acid (Templeton et al. 2012b). Together with commonly occurring sulphate groups, acidic sugars are deemed responsible for the anionic nature of cyanobacterial polysaccharides (Villacorte et al. 2015b).
2.2.2.2 Algal and cyanobacterial proteins
Typically, microalgal and cyanobacterial proteins comprise enzymatic proteins, nucleic acids, amines, glycoproteins and lectins, nitrogenous cell wall materials, and these have been reviewed in detail previously (Becker 2007, Schwenzfeier et al. 2014, Kehr et al.
2015). Most algae and cyanobacteria exhibit high protein concentrations, with the largest values being reported for the Arthrospira sp species (55.8 to 77%) (Barka et al.
2016). However, considerable variability in the protein content for the same species can also exist. For instance, M. aeruginosa was reported to have 11.4% protein content in the Australian strain (Yap 2013) whereas the UK strain was observed to have 40% of protein in its AOM (Henderson et al. 2008c). Reported values may also depend on the analytical methods used in a study and other factors such as growth medium and strains analysed.
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2.2.3 Impact of AOM and its components on flotation efficiency
The flotation of algal and cyanobacterial cells may be hindered or enhanced by the presence of AOM. For instance, at high concentrations of approximately 5 mg/L, AOM is known to increase coagulant demand, and at low concentrations (0.1-2 mg/L), it is known to act as a coagulant aid (Bernhardt et al. 1991b, Vandamme et al. 2010,
Vandamme et al. 2012). This is because the AOM adheres to algal cells via hydrogen or covalent bonding; greater or lower concentrations of AOM would result in greater or lower binding with the cells, resulting in a corresponding increase or decrease in chemical demand. Additionally, AOM-coagulant complexation may take place, which blocks negatively charged functional groups present in the AOM and as a result, prevents coagulation of cells by adsorption or charge neutralisation (Pivokonsky et al.
2006).
AOM and its components have also been observed to enhance flotation of various particles ranging from algal and cyanobacterial cells (Bernhardt et al. 1987, Bernhardt et al. 1991a, Yap 2013) to inorganic particles such as kaolin and quartz (Safarikova et al. 2013). For instance, biopolymers in the AOM can attach to the negatively charged surface of quartz particles by means of hydrogen and covalent bonds thereby enhancing flotation to greater than 80% (Bernhardt 1985, Safarikova et al. 2013). Similarly, Yap
(2013) demonstrated in PosiDAF that dosing the biopolymer rich exudates of one strain of M. aeruginosa CS-564/01 into the jar containing another strain of M. aeruginosa CS-
555/1 resulted in the formation of large web-like networks that enhanced the cell separation to greater than 90% in M. aeruginosa CS-555/1. Furthermore, it was noted that the large web-like structures obtained were similar to fibrils first observed by
Leppard (1995) and later described by (Harel et al. 2012) for M. aeruginosa PCC-7806
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(from the Netherlands). These fibrils were found to be associated to the surface of M. aeruginosa PCC 7806 and were determined to consist of polysaccharides. Protein was also identified in these samples; however, it was not confirmed whether or not the proteins formed a part of the fibril structure (Harel et al. 2012).
In related fields, naturally occurring plant mucilage bioflocculants such as Plantago psyllium mucilage (Mishra et al. 2005) and Tamarindus indica mucilage (Mishra et al.
2006) were observed to result in greater than 80% process efficiency in DAF. These mucilage based bioflocculants were reported to comprise biopolymers, which bridged between bubbles and particles resulting in enhanced separation (Lee et al. 2014a), similar to that observed during the PosiDAF application of M. aeruginosa (CS-564/01)
AOM on M. aeruginosa (CS-555/1). Corroborating the bridging mechanisms observed during the application of mucilage in DAF with the web-like networks observed during separation of M. aeruginosa CS-564/01 by PosiDAF, it is evident that the biopolymers present in the AOM play an important role in the process enhancement in various flotation processes. However, further research is required to analyse the web-like networks as well as understanding how manipulation of the biopolymers present in the
AOM can enhance PosiDAF performance.
As the biopolymers are predominantly known to comprise carbohydrates and proteins, it is important to understand how carbohydrates and proteins either individually or collectively, could impact flotation performance. This has been explained below.
2.2.3.1 Influence of algal and cyanobacterial carbohydrates on flotation efficiency
The algal and cyanobacterial carbohydrates have the ability to influence flotation efficiency; for instance in PosiDAF, Yap (2013) hypothesised that large fractions of carbohydrates in M. aeruginosa CS-564/01 were predominantly composed of ~ 41 ~
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polysaccharides that formed AOM-cell linkages, enhancing flotation to greater than
95%. In addition, several studies have demonstrated the impact of charged polysaccharides on flotation (Mopper et al. 1995, Zhou et al. 1998, Schwenzfeier,et al.
2014, Discart et al. 2013). For instance, the presence of anionic polysaccharides in M. aeruginosa enables their ability to sequester metals such as copper and cadmium (De
Philippis et al. 2011). In another study, Chen et al. (2009) employed ferric salts as a coagulant and concluded that the amount of ferric attached to the algal cells reduced with increasing concentration of anionic polysaccharides dissolved in solution. These types of polysaccharides contribute to transparent exopolymer particles (TEPs), which have been studied in the context of membrane fouling (Mopper et al. 1995, Zhou et al.
1998, Passow 2002, Villacorte et al. 2015a). TEPs are polymeric gel particles produced by microalgae in the natural environment.
Other types of carbohydrates, such as neutral polysaccharides, present in the algae and cyanobacteria may also enhance the flotation efficiency due to the presence of hydroxyl and carboxyl functional groups that could potentially form hydrogen bonding in aqueous solutions (Bernhardt 1985, Cheng et al. 2011, Alam et al. 2014). For example, the hydrogen bonding between the hydroxyl and carboxyl groups on chitosan and the polysaccharides in the algal cell walls enhanced process efficiency to greater than 90%
(Cheng et al. 2011). As a result of their electrical neutrality and the increased concentration of –OH groups resulting in hydrogen bonding in aqueous solutions, polysaccharides are also extremely difficult to remove via flotation processes
(Pivokonský et al. 2009). For instance, low Mw polysaccharides were observed to increase the negative charge of the surface particles resulting in an unpredictable coagulant demand, which consequently resulted in their poor removal during flotation
(Bernhardt et al. 1991a). The impact of the polysaccharide character on conventional ~ 42 ~
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flotation applications is clearly evident. Hence, it is a logical extrapolation to suggest that PosiDAF efficiency would be impacted in a similar manner and this is an important feature to be considered in order to advance technology.
2.2.3.2 Influence of algal and cyanobacterial proteins on flotation efficiency
Proteins in algae and cyanobacteria can increase or decrease flotation efficiency and this depends on the character and contents of their functional groups. Proteins carry positive charges due to the presence of secondary and tertiary amines and negative charges due to carboxylate groups; therefore, these molecules can interact with positively charged polymers and with negatively charged impurities such as kaolin that are present in the water (Safarikova et al. 2013). This can sometimes be problematic as proteins that contain adjacent -OH and –COOH groups have been reported to complex with coagulants (Takaara et al. 2007). This inhibits successful destabilisation of the system and as a consequence, impacts flotation efficiency (Pivokonsky et al. 2006, Takaara et al. 2007, Henderson et al. 2008b). Contrastingly, proteins can also serve as a cationic coagulation aid under certain coagulation conditions at pH values below neutral (pH range for 4-6 for ferric and 5-6.5 for alum) enhancing the removal of other impurities in the raw water such as humic substances (Safarikova et al. 2013, Pivokonsky et al.
2015). In PosiDAF, high Mw proteins in the AOM of algae and cyanobacteria have been hypothesised to aid in co-operative binding with polymers (Henderson et al. 2010b).
While Pivokonsky et al. (2015) determined that proteins interacted with humic substances by hydrophobic, dipole-dipole and electrostatic interactions and has a positive effect on the coagulation process, the mechanism by which proteins influence the algal and cyanobacterial cell separation in PosiDAF is ambiguous. In order to
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provide a better understanding of the contribution of proteins to the PosiDAF efficiency, further research is required.
While there are some studies that systematically examine the impact of algal and cyanobacterial proteins and polysaccharides on the flotation efficiency in conventional flotation systems, there are no published studies relating the impact of these biopolymers to the PosiDAF process. Hence, this remains an area for potential investigation.
2.2.4 Techniques for characterising AOM and its components
The AOM present in water is typically a heterogeneous mixture containing a multitude of different chemical molecules (Henderson et al. 2008b, Villacorte et al. 2015b,
Pivokonsky et al. 2016). The first step in characterising the AOM is therefore to characterise the bulk organic matter (Henderson et al. 2008c, Henderson et al. 2010a,
Stewart et al. 2013, Zhang et al. 2013, Pivokonsky et al. 2014, Villacorte et al. 2015a,
Villacorte et al. 2015b) and then to describe the individual components such as proteins and carbohydrates (Templeton et al. 2012b, Yamada et al. 2012, Villacorte et al.
2015b). There are several analytical techniques which have become increasingly common for characterising the bulk organic matter as well as specific molecular components. Some of these are discussed in the following sections.
2.2.4.1 Size exclusion chromatography
Size exclusion chromatography (SEC) is a fractionation technique which separates the
AOM sample based on its molecular size distribution and, to an extent, its polarity (Her et al. 2002a, Her et al. 2002b, Amy et al. 2004, Villacorte et al. 2015b). It has been used for the investigation of organic matter in algal samples because the size of AOM is
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an important factor to consider in relation to AOM involvement in coagulation and membrane filtration (Ladner et al. 2009, Henderson et al. 2010a). For example,
Henderson et al. (2010a) demonstrated that the low Mw (less than 1 kDa) carbohydrates present in the AOM of A. formosa exhibited a low affinity for the coagulant (alum) such that eight times high dose of the coagulant was required to treat the algae in comparison to C. vulgaris. More recently, in the PosiDAF process, Yap (2013) observed that the cell separation of M. aeruginosa CS-564/01 reduced to less than 70% when AOM filtered through a 0.45 µm membrane was introduced to the jar containing the cyanobacterial cells in comparison to 99% obtained when unfiltered AOM was introduced. As a result, SEC has been widely applied in the characterisation of AOM to simultaneously determine molecular weight distribution, and chemical properties
(Henderson et al. 2008c, Villacorte et al. 2015b).
The latest SEC system includes multiple detectors (organic carbon, nitrogen, and
UV254), and is known as liquid chromatography - organic carbon and nitrogen detection (LC-OCND). In the LC-OCND, size fractions have been classified as follows
(Her et al. 2002a, Amy et al. 2004, Kostanski et al. 2004, Huber et al. 2011a): biopolymers (such as polysaccharides, polypeptides, proteins and amino sugars); humic substances (fulvic and humic acids); building blocks; LMW humic substances and acids; and LMW neutrals (such as alcohols, aldehydes, ketones and amino acids). The
LC-OCND technique can also help understand the character of the molecules in each fraction due to the multiple detectors. A recent study reported the application of LC-
OCND in the quantification of proteins and polysaccharides from the biopolymer fraction for a sample of periphyton and several other cyanobacteria and algae such as M. aeruginosa and C. vulgaris (Stewart et al. 2013, Villacorte et al. 2015b). This technique has been employed to provide insight into the variation of MW distribution of AOM of ~ 45 ~
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M. aeruginosa for sizes < 260,000 kDa (Zhang et al. 2013). As the AOM contains proteins and polysaccharides among other HMW fractions (Henderson et al. 2010a,
Pivokonsky et al. 2016), LC-OCND is likely to be a useful technique to characterise
AOM in the context of PosiDAF process efficiency.
2.2.4.2 Fourier transform infrared spectroscopy (FT-IR)
Fourier transform infrared spectroscopy (FT-IR) is a spectroscopic technique based on the use of infrared radiation. This technique measures absorbance that occurs as a result of molecular vibrations from chemical bonds in a sample of different absorbance regions from 4000-400 cm- 1. It allows multiple chemical species to be analysed simultaneously and images are generated from a single measurement. FT-IR has been successfully used for the characterisation of AOM, providing valuable information on the structure and functional properties of the AOM (Villacorte et al. 2015b, Gonzalez-
Torres et al. 2017a). For instance, Gonzalez-Torres (2017) determined through FT-IR that the flocs of M. aeruginosa were smaller and stronger because they had more concentrated protein regions within the flocs. Other applications of FT-IR in algal systems have been for the purposes of microalgal biochemical composition monitoring
(Stehfest et al. 2005, Pistorius et al. 2009, Meng et al. 2014), strain screening
(Domenighini et al. 2009, Wenning et al. 2013), examining the response of microalgae under different nutritional conditions (Giordano et al. 2001, Wagner et al. 2010), and analysing the species-specific response to metabolic changes due to variations in environmental factors (Wagner et al. 2013). Hence, by investigating the FT-IR spectra, macromolecules within AOM samples can be identified. Several important regions in the AOM have been identified through the FT-IR spectra and classified in Table 2-3.
For instance, the peaks that are usually present in the 3000 - 2800 cm-1 range comprise
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fatty acids whereas those in the 1700 - 1500 cm-1 comprises amide I and amide II peaks, which indicate proteins and peptides present in the AOM (Table 2-3) (Giordano et al.
2001, Stehfest et al. 2005, Burgula et al. 2007). It is therefore proposed that the application of FT-IR to analyse the bio-chemical nature of the web-like networks that appear during the flotation of M. aeruginosa CS-564/01 in PosiDAF has excellent potential.
Table 2- 3. Regions where AOM components typically predominate when analysed through FT-IR. Data compiled from Giordano et al. (2001), Stehfest et al. (2005), Burgula et al. (2007), Villacorte et al. (2015b), Gonzalez-Torres (2017).
Wavenumber (cm-1) Component identified Comments 3000-2800 Fatty acids Indicates those present in the cells and AOM 1700-1500 Amide I and II Indicates proteins and peptides in the AOM. Amide I peak used to analyse the secondary protein structure around 1640 cm-1. 1500-1200 Fatty acids, Usually a conglomeration of peaks and polysaccharides, proteins molecules and phosphates 1200-900 Polysaccharides Includes the peaks of carbohydrates present in the microbial cell walls. 900-700 Unique peaks and sundry Weak peaks those are particular to a species. May be caused by impurities from samples.
2.2.4.3 Techniques for the characterisation of biopolymers in AOM
Algal organic matter can also be characterised based on the proportion of known compounds such as carbohydrates, proteins and lipids (Croue et al. 2000, Henderson et al. 2008c, Chon et al. 2013, Sillanpää et al. 2015b). As discussed in Section 2.2.2.2, the ~ 47 ~
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biopolymers can have a significant impact flotation and, therefore, experimental techniques to characterise them are reviewed in the following sections.
2.2.4.3.1 Carbohydrate characterisation
Characterisation of microalgal carbohydrates is complicated as they comprise neutral, amino and acidic sugars and these compositions could vary with different species and stages of growth (Pieper et al. 2012, Villacorte et al. 2015a, Villacorte et al. 2015b).
Limited information is available about microalgal carbohydrates such as fractions of mono- or polysaccharides, storage or structural carbohydrates, or the concentrations of glycolipids and glycoproteins. It has been determined that the extent of sample preparation for carbohydrate analysis is dependent on the nature of the sample being analysed while the most appropriate method of carbohydrate isolation depends on the carbohydrate type, the sample matrix type and the purpose of analysis (Templeton et al.
2012a). Generally, the total carbohydrate content is calculated by difference, rather than analysed directly as: 100 - (Weight in grams [protein + fat + water + lipids + minerals + fibre] in 100 g of the sample). Alternatively, available carbohydrate can be derived by summing the analysed weights of individual available carbohydrates as: Weight in grams (monosaccharides + disaccharides + oligosaccharides + polysaccharides)
(Chaplin et al. 1994). In either case, available carbohydrate can be expressed as the weight of the carbohydrate or as monosaccharide equivalents. However, analytical methods to study the type and concentration of different types of carbohydrates are more accurate.
In algal biomass samples, carbohydrates have been typically quantified using a phenol– sulphuric acid assays (Table 2-4), where sugars are hydrolysed to furans and measured spectrophotometrically (DuBois et al. 1956). However, it has been demonstrated that
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carbohydrates could have differing responses and their quantification has been unreliable. This is because other organic components such as lipids, proteins, and sundry components found in algae are likely to interfere with the carbohydrate content measured by this method (DuBois et al. 1956, Taylor 1995). Additionally, the assay assumes equal sensitivity and reactivity of all carbohydrates (DuBois et al. 1956).
Therefore, when complex carbohydrate mixtures such as those found in algae are analysed, this technique is likely to under- or overestimate the actual carbohydrate concentrations.
Molecular characterisation of carbohydrates requires advanced techniques such as hydrolysis followed by either liquid or gas chromatography (Mason et al. 1971) (Table
2-4). Gas chromatography of neutral sugars in the form of volatile derivatives results in a good resolution of individual sugars (Mason et al. 1971, Blakeney et al. 1983), where two different chemical derivatisation methods can be used to render sugars volatile for gas chromatography; trimethyl silylation (TMS) (Mason et al. 1971) and alditol acetate derivatisation (AA) (Blakeney et al. 1983). However, these can be hindered even by residual presence of moisture and can yield multiple peaks for a given sugar (Bishop
1964, Mason et al. 1971). Liquid chromatography can greatly reduce sample preparation time when compared to gas chromatography (Table 2-4). However, the analysis can reduce chromatographic resolution, which can be problematic while analysing complex carbohydrate mixtures (Widmer 2011). A recent and promising liquid chromatography system is high-performance anion-exchange chromatography
(HPAEC) with pulsed amperometric detection (PAD) (Cataldi et al. 2000, Widmer
2011). Three major carbohydrate classes, neutral and amino sugars and uronic acids in algal samples, have been simultaneously measured using this technique (Cheng et al.
2003, Templeton et al. 2012a). HPAEC-PAD also allows selective measurement of ~ 49 ~
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electroactive species, such that interfering species pass through the system undetected because they cannot be oxidised or reduced. For example, the detection of monosaccharides such as glucose and fucose did not suffer from interferences by non- carbohydrate compounds in the same solution (Templeton et al. 2012a). This technique would therefore enable a complete and an accurate characterisation of the carbohydrates in algal samples analysed in this study.
Table 2- 4. List of analytical methods used for characterisation of carbohydrates
Technique Comments Reference Anion exchange Can measure total and individual (Cheng et al. 2001, chromatography components such as mono-, di- and Sullivan et al. 2011, polysaccharides Pereira da Costa et al. 2015) Gas Chromatography – Columns, flame ionisation detectors. (Sakugawa et al. 1985, Mass spectrometry Sometimes used in conjunction with Hama et al. 2001, NMR, MS Williams et al. 2009, Ruiz-Matute et al. 2011) High Performance Liq- Sometimes used in conjunction with (Gremm et al. 1997, Kiss uid Chromatography NMR, MS et al. 2011) Liquid Chromatography Columns, post-column solvent (Williams et al. 2009) – Mass Spectroscopy addition Capillary electrophoresis Fluorophores used sometimes. Can (Morell et al. 1998, study monosaccharide composition Goubet et al. 2002, Yao et and structure of polysaccharides; al. 2005, Barton et al. 2006) Phenol- Calibration curve needs to be (DuBois et al. 1956, Sulphuric acid assay prepared sometimes/ Total Zhang et al. 1999b, sugars measured Masuko et al. 2005, Laurens et al. 2012) NMR 13C NMR and 1H NMR used. (Ma et al. 2001, Chen et Powerful tools for determination of al. 2002, Wong et al. molecular structure and concentration. 2002, Williams et al. 2009, Balaria et al. 2013)
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2.2.4.3.2 Protein Characterisation
Algae are known to differ significantly in total protein content, which ranges from reasonably low (3–15% dry weight) in phaeophyceae to high in cyanobacteria (10–47% dry weight) (Henderson et al. 2008c, Schwenzfeier et al. 2011, Kehr et al. 2015,
Pivokonsky et al. 2016). The characterisation of proteins can be complicated in algae due to the presence of phenolic compounds (Steinberg 1984, Duval et al. 1999) and tannins (Amarowicz 2007) as well as the rapid production of lectin-based agglutinins
(Blunden et al. 1990, Bird et al. 1993, Sakamoto et al. 1996, Yamaguchi et al. 1998).
The resilient nature of the algal cell wall (Doucha et al. 2008) and presence of polyanionic cell wall mucilage specifically composed of alginates (Jordan et al. 1991) have also complicated protein characterisation methods. In addition, cyanobacterial cells and cell assemblages may be covered by extracellular polysaccharides (Villacorte et al. 2015a, Villacorte et al. 2015b) and extensive peptidoglycan architecture in the cell wall (Hoiczyk et al. 2000, Kehr et al. 2015). Many of these compounds complicate the analysis of total proteins and the methods are therefore highly specific to species.
Microalgae often contain substantial levels of free amino acids and other N- based organic compounds such as chlorophyll and the techniques that have been used to measure protein content in the AOM have focussed on these compounds (Brown 1991,
Rebolloso-Fuentes et al. 2001, López et al. 2010, Laurens et al. 2012). One of the most routinely used methods to quantify total proteins is the alkaline copper method, commonly known as the Lowry protein assay (Table 2-5) (Lowry et al. 1951, Markwell et al. 1978). The determination of protein by the Lowry assay is carried out by a reaction catalysed by copper, a component of the Folin- phenol reactions followed by spectrophotometry. The chemical reaction detects peptide as tyrosine and tryptophan
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(Legler et al. 1985). As a consequence, the reactivity of Lowry method in comparison to a specific protein is strongly influenced by its amino acid composition; since not all amino acids can oxidate equally, the Folin- phenol method can lead to inaccuracies
(Stoscheck 1990). To combat these challenges, advancements in Lowry assays (Frølund et al. 1995) and several other techniques to measure proteins have also been used such as combustive methods of elemental analysis and protein estimation from total amino acids by HPLC after hydrolysis of the AOM (Table 2-5) (Brown 1991, Volkman et al.
1993). Nevertheless, these approaches can lead to an overestimation due to a lack of specificity towards the various proteins (Barbarino et al. 2005).
To obtain a more reliable measurement of protein, it would be useful to identify the predominant proteins in the cells (Berges et al. 1993). However, this recommendation has no practical value, considering the difficulty of extracting, purifying and characterising the main proteins present in the cells and subsequently using them as protein standards. Recently, protein characterisation post extraction has been suggested as an alternative to the methods discussed previously (Jong et al. 2015). Several procedures have been described for lyophilised AOM but they also require labour intensive steps such as grinding in a pestle and mortar with inert ceramic particles
(López et al. 2010), Potter’s homogenizer (Barbarino et al. 2005), and chemical extraction methods like acetone precipitation (Wang et al. 2003). Furthermore, some commonly studied strains such as Chlorella vulgaris or Nannochloropsis oculata can present general extraction difficulties possibly due to small cell-size or resilient cell walls (Doucha et al. 2008, Chiu et al. 2009).
Mass spectrometry (MS) based methods have been established as an indispensable technology for protein characterisation (Table 2-5) (Zhang et al. 2015). To date, MS has
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Chapter 2B Literature review (AOM& its components)
been applied for the characterisation of cyanobacterial proteins (Simó et al. 2005, Lee et al. 2015) and also for approaches such as confirmation of cyanobacteria through the detection of ribosomal protein mass patterns (Sun et al. 2016). For instance, MS was used to establish a rapid, simple, and accurate method to characterise and differentiate different strains Microcystis aeruginosa, particularly, the toxic M. aeruginosa NIES-
843, for which a complete genome was sequenced (Humbert et al. 2013). Novel MS techniques such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) (Sun et al. 2016) and capillary electrophoresis-mass spectrometry (CE-MS) (Table 2-5) (Zhang et al. 2015) that have good reproducibility and accuracy have also been successfully used for characterising algae and cyanobacteria. Importantly, the use of MS can reduce costs and time required and increases the accuracy and reproducibility of the results (Zhang et al. 2015). Therefore, the application of MS would be a natural fit to the protein characterisation of the strains used in this study. A comparison of several methods used for AOM protein characterisation is presented in Table 2-5.
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Chapter 2B Literature review (AOM& its components)
Table 2- 5. List of analytical methods used for characterisation of proteins
Technique Comments Reference Lowry protein assay Good for estimation of total protein content (Lowry et al. 1951, in samples. Technique is sample dependent Markwell et al. 1978, and relevant modifications can be found in Frølund et al. 1995, the literature. Laurens et al. 2012) Dumas combustion Overestimation possible due to lack of (Rebolloso-Fuentes et method and N- specific proteins. Sample destruction during al. 2001, Laurens et al. content estimation combustion leads to poor estimation 2012) Thin layer Used for protein characterisation; however, it (Peace et al. 2005) chromatography is error prone due to interferences from non- protein components. Gas chromatography Has been used extensively used. Can be (Peace et al. 2005) and capillary combined with mass spectrometry for electrophoresis analysing proteins. FT-IR Can detect specific functional groups on (Arbely et al. 2003, proteins such as Amide I and Amide II. Used Gordon et al. 2004) in conjunction with SDS-PAGE
SDS-PAGE Can determine Mw and concentrations of (Rouxel et al. 2001, proteins present in samples. Patel et al. 2005, Soni et al. 2006) Mass spectrometry Novel MS techniques such as MALDI-TOF (Russell et al. 1997, available. Powerful tool for determination of Reid et al. 2002, Lu et molecular structure and concentration. al. 2003, Hu et al. 2005) NMR 13C NMR and 1H NMR used. Powerful tools (Kim et al. 2005, for determination of molecular structure and Matilainen et al. 2011) concentration.
2.2.5 Conclusions and knowledge gaps
The influence of algal organic matter on flotation and techniques for the characterisation of AOM in water treatment were reviewed in Part B of the Literature Review.
Outcomes of the review are as follows:
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Chapter 2B Literature review (AOM& its components)
• Cell morphological characteristics such as cell size and charge can increase or
decrease coagulant demand and consequently affect flotation.
• Both the character and concentration of AOM have been demonstrated to
influence flotation. Specifically, proteins and carbohydrates can influence the
outcome by interacting with either the coagulants or impurities that are present
in the water.
• There are a number of techniques that can be applied for the molecular
characterisation of the AOM by targeting specific functional groups. For
instance, techniques such as HPAEC-PAD and MS can identify individual
carbohydrates and proteins present in the AOM. Hence, they are promising tools
for the characterisation of proteins and carbohydrates in the AOM.
From this review the following gaps in research have been identified:
• Until now, bulk characterisation of AOM has been conducted and AOM
character has been implicated in PosiDAF performance previously. However,
specific molecular components such as proteins and carbohydrates which may
cause PosiDAF performance variability have not been identified and related.
Therefore, characterisation of these macromolecules is important to better
understand why PosiDAF performance varies for different species and strains.
• The impact of 'foreign’ AOM concentration and character on PosiDAF
efficiency has been studied for two strains of cyanobacteria only. Trialling more
strains of different species of freshwater or marine algae is necessary to validate
the influence of ‘foreign’ AOM on separation.
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Chapter 3
APPLICATION OF HYDROPHOBICALLY MODIFIED
POLYMERS IN POSIDAF: BUBBLE PROPERTIES AND
FLOTATION PERFORMANCE
3.1 Introduction
In previous research, it was identified that increasing the surface activity of cationic water treatment polymers, to encourage adhesion of the polymer to the bubble surface and minimise carryover to downstream processes, had potential (Yap et al. 2014b).
Consequently, an earlier study saw the development of several poly(N,N- dimethylaminoethyl methacrylate) (PDMAEMA) variants by modifying the base polymer to incorporate a range of aliphatic hydrophobic groups (Yap et al. 2014b).
When these hydrophobically modified polymers (HMPs) were applied in PosiDAF on a bench scale, over 95% cell separation was achieved for a strain of Microcystis aeruginosa CS-564/01, while simultaneously maintaining a negatively charged effluent, suggesting that the polymer concentration in the effluent was reduced by comparison to studies that had used unmodified polymers, which had yielded cationic effluents (Yap et al. 2014b). Interestingly, when the HMPs of PDMAEMA were trialled on a pilot scale the separation effectiveness significantly reduced to less than 50%, contrasting dramatically with the performance of commercially available PDADMAC, which still maintained a separation of greater than 95%, attributed in part to its enhanced positive charge (Yap 2013, Henderson et al. 2015). These outcomes indicate that further
Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
research into HMP generation using PDADMAC is necessary and that a better of understanding of polymer-bubble interactions is required.
Prior studies primarily focused on quaternising tertiary amines with aliphatic groups as they are known to create brush type amphiphilic polymers (Ríos et al. 2011, Yap 2013,
Yap et al. 2014b). However, no studies in this field have used polymers quaternised with pendant aromatic moieties. Recently, the functionalisation of PDADMAC with pendant hydrophobic groups to generate polymers with differing charge, hydrophobicity, and structural characteristics was demonstrated (Ríos et al. 2011).
Another study showed that when PDADMAC was modified with aromatic groups, the subsequent hydrophobicity facilitated bridging sheets of clay (Vuillaume et al. 2003). A further gap in previous PosiDAF work has been the absence of a direct measure of polymer concentration in treated effluent. Until now, zeta potential measurements were employed to give an indirect indication of residual polymer. An additional advantage of modifying PDADMAC with aromatic groups is the potential for quantifying polymer concentration in the effluent directly, through fluorescence measurements (Becker et al.
2004). These outcomes demonstrate the potential for tailoring PDADMAC for application as a bubble modifier in PosiDAF and that both aliphatic and aromatic moieties should be investigated.
Earlier work on characterising polymer coated bubbles in PosiDAF was limited to bubble charge measurements and studying the polymer properties such as surface tension and charge, which on analysis, proved insufficient as questions on the extent of polymer adhesion to the microbubble remained unanswered. One of the successful methods employed to study polymer-bubble interactions has been atomic force microscopy (AFM), wherein polymers such as poly(dimethyl siloxane) (Milling et al.
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
1995) and sodium poly(styrene sulfonate) (Milling 1996, Browne et al. 2015b, Browne et al. 2015a) have been trialled extensively. Due to prior successes of AFM delineating the mechanisms behind polymer-bubble interactions, it is likely that analysis using
AFM would improve understanding of PosiDAF mechanisms for this study.
Hence, in the current study, a comparison of the performance of using hydrophobically- modified PDMAEMA and PDADMAC based polymers, modified with aromatic and aliphatic groups for the latter, in the PosiDAF process was undertaken for the separation of one strain of Chlorella vulgaris CS-42/7 and two strains of Microcystis aeruginosa
CS-564/01 and CS-555/1. Importantly, AFM was applied to quantify polymer-bubble interactions and, for polymers modified with aromatic moieties, fluorescence measurements were employed to quantify the polymer concentration in treated effluent.
In combining these measurements, the underlying process mechanisms leading to successful separation using PosiDAF were explored.
3.2 Materials & methods
3.2.1 Materials
3.2.1.1 Chemicals
Unless otherwise stated, all chemicals were obtained from Sigma-Aldrich and used without any further purification. Low MW (100,000 – 200,000 g/mol) poly(N, N- diallyl-N,N-dimethylammonium chloride) (PDADMAC, 20 wt% in H2O) was lyophilised prior to use. Ethanolamine (Unilab, 97%), benzyl chloride (98%), 4- fluoro benzyl chloride (98%), 1-chlorobutane (98%), 1-chloropentane (97%), 1- chlorodecane (98%), 1-bromodecane (98%) were all used as received. 2-(N,N- dimethylamino)ethyl methacrylate (DMAEMA, Aldrich, 98%) was passed through a
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
basic alumina column to remove inhibitor prior to use. 2,2’-Azobisisobutyronitrile
(AIBN, Wako Chemicals, 98%) was re-crystallised twice from methanol.
3.2.1.2 Algae and cyanobacteria
Chlorella vulgaris (strain CS-42/7) and Microcystis aeruginosa (strains CS-555/1 and
CS-564/01) hereafter referred to as CV, MA555 and MA564 in this thesis, were obtained from the Commonwealth Scientific and Industrial Research Organisation
(CSIRO) Australian National Algae Culture Collection (ANACC), Hobart, Australia, and re-cultured in Jaworski (Henderson et al. 2008d) and MLA media (Bolch et al.
1996), respectively. The cultures were subjected to a 16/8 h light/dark cycle, with an associated temperature control of 21 °C/ 15 °C, in 500 L, PG-50 and PG-120 cycling incubators (Labec, Australia) with a photosynthetic photon flux output of approximately
600 μmol/m2s. These cultures were grown in several 250 mL conical flasks in batches of 100 mL. At the end of the exponential growth phase and upon the onset of the stationary growth phase (10-13 d), as determined by cell counting via light microscope
(Leica DM750 Microscope, Switzerland) and a haemocytometer, the cells were harvested for jar testing.
3.2.2 Experimental methods
3.2.2.1 Synthesis of hydrophobically functionalised PDADMAC
The synthesis of hydrophobically functionalised PDADMAC was carried out in a two- step de-methylation and quaternisation of the polymer. The de-methylation of
PDADMAC was carried out in the same manner as de-methylation of chiral [3, 22]- ionenes by generating the corresponding tertiary polyamine (Bazito et al. 2005).
Briefly, 5 g of the lyophilised PDADMAC was reacted with 20 mL of ethanolamine in a
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
round bottom flask equipped with a reflux condenser. The reaction mixture was heated to 165 °C for 4 h after which it was cooled to room temperature and mixed with 80 mL
DI water. The resulting mixture was extracted thrice with 80 mL chloroform and any remaining water was stripped with anhydrous MgSO4. Chloroform was removed under reduced pressure resulting in a waxy-yellow solid which was later characterised by 1H
NMR. The reaction scheme is described in Figure 3-1.
The quaternisation of the selectively de-methylated PDADMAC was carried out in the presence of n-alkyl halides (Ríos et al. 2011) (Figure 3-1). In this study, the polymer and the respective alkyl chloride were mixed in a 1:1.2 molar ratio of the monomer units and heated to 65 °C for 96 - 120 h in the presence of chloroform. The solvent was then extracted under reduced pressure and the solids were dried in a vacuum oven for 24 h. The solids were dissolved in a 50:50 ethanol-water mixture and filtered using a UF membrane (MWCO 1000). The polyelectrolyte was lyophilised and analysed by 1H
NMR.
Figure 3- 1. Reaction scheme indicating de-methylation and quaternisation of the de- methylated PDADMAC.
The following PDADMAC based polymers quaternised with aliphatic and aromatic groups with varying charges were synthesised: 1) PDADMAC quaternised with 1- chlorobutane (PD-C4); 2) 1-chloropentane (PD-C5); 3) 1-chlorodecane (PD-C10); 4) benzyl chloride (PDADMAC-BC); and 5) 4-fluoro benzylchloride (PDADMAC-BCF). ~ 60 ~
Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
PDADMAC was used as the standard to verify the performance of the synthesised polymers with prior studies (Henderson et al. 2010b, Yap et al. 2014b).
3.2.2.2 Free radical polymerisation of DMAEMA and its subsequent quaternisation
Following the same procedure as in the previous study (Yap et al. 2014b), PDMAEMA and the best performing quaternised derivative (50% quaternisation, charge density:
2.86 meq/g, surface tension: 69 mN/m), from that study were synthesised. Briefly,
DMAEMA was polymerised using simple bulk free radical polymerisation (Bazito et al.
2005) (Figure 3-2). 10 g (6.36 × 10-2 mol) of purified DMAEMA was added to a 25 ml reaction vial, followed by 32.8 mg (2 × 10-4 mol) of AIBN to achieve a targeted Mw of
400,000 g/mol. The reaction vial was then purged with nitrogen at 0°C for 30 minutes to remove any dissolved oxygen. The mixture was placed in an oil bath at 65°C for 12 hours and left to react. Post polymerisation, the sample was dissolved in ethanol and dialysed against water in a 25 kDa MWCO membrane to remove any unreacted monomer following which it was lyophilised.
Figure 3-2. Reaction scheme indicating synthesis of DMAEMA homo polymer and its subsequent quaternisation with 1- bromodecane.
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
Quaternisation of the homopolymer was carried out with 1-bromodecane at room temperature (Bazito et al. 2005) (Figure 3-2). Briefly, 300 mg of the homopolymer was first dissolved in 3 mL of methanol, followed by the addition of 211 mg of 1- bromodecane to the reaction mixture. The solution was stirred vigorously for 36 hours, before being dried by vacuum oven at 40°C. The product was then dialysed against methanol in a 25 kDa MWCO membrane to remove any excess of 1-bromodecane and dried in a vacuum oven for 24 hours. Later, the quaternised polymer was dissolved in water and lyophilised prior to use. This polymer will hereafter be known as H33 to maintain consistency with prior PosiDAF work (Yap et al. 2014b).
3.2.2.3 Jar testing
3.2.2.3.1 Synthetic DAF medium
To prepare the synthetic DAF influent water, cultured cells were diluted to 6×105 -
5 8×10 cells/mL with DI water buffered with 0.5 mM NaHCO3 and brought to an ionic strength of 1.8 mM using NaCl. This was then adjusted to pH 7 using 1M HCl and 1M
NaOH to maintain consistency and ensure comparability with previous studies
(Henderson et al. 2008b, Henderson et al. 2008d, Henderson et al. 2009b, Henderson et al. 2010b, Henderson et al. 2010a, Yap 2013, Yap et al. 2014b).
3.2.2.3.2 PosiDAF jar tests
A DAF Batch Tester, Model DBT6 (EC Engineering, Alberta, Canada), was used for the PosiDAF jar testing. The saturator influent and recycle flow consisted of DI water buffered with 0.5 mM NaHCO3 and made up to an ionic strength of 1.8 mM with NaCl and corrected to pH 7. Industrial grade air was then used to pressurise the saturator to
450-470 kPa which was subsequently shaken until the liquid inside was completely
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
saturated. The jar tests were conducted in batches of 1 L with synthetic DAF influent water. Flotation was conducted for 10 min prior to sampling of the treated effluent without any coagulation-flocculation. It should be noted that a recycle ratio of 20% was selected for all PosiDAF jar tests to ensure that a high bubble to particle ratio was maintained because conventional coagulation-flocculation of cells was not undertaken.
A schematic of the PosiDAF system is described in Figure 1-1 (B).
The polymer stock solutions were prepared and stirred at 200 rpm for 1 h before use.
Aliquots of buffered solution were dosed with various polymer concentrations and added to the saturator prior to pressurisation. The performance of the polymers functionalised with hydrophobic moieties was ultimately compared against unmodified commercially available PDADMAC.
3.2.3 Characterisation and Analytical Methods
3.2.3.1 Cell characterisation
The cells were characterised by size using a Mastersizer 2000 (Malvern, Australia) and zeta potential using a Zetasizer Nano NZ (Malvern, Australia). These cell samples were analysed at the onset of the stationary phase during their growth cycles (10-13 days) and immediately prior to undertaking the jar tests.
3.2.3.2 NMR characterisation
1H NMR spectra were recorded using a Bruker ACF300 (300 MHz) (Bruker, Germany) spectrometer. The de-methylated polymer was analysed by dissolving it in deuterated chloroform (CDCl3) whereas the quaternised polyelectrolytes were analysed by employing deuterium oxide (D2O) as solvent. The percent quaternisation was
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
determined directly via the ratio of the signal areas from the experimental spectra of the modified polymer with that of PDADMAC and PDMAEMA.
3.2.3.3 Zeta potential and hydrodynamic size measurements
The synthesised polymers were characterised through zeta potential and hydrodynamic size measurements using a Zetasizer Nano ZS (Malvern, Australia), using 175° backscatter to measure Brownian motion via scattering intensity fluctuations. The dispersant parameters used for the analyses were RI: 1.330, Abs: 0.1 and diluent (water) viscosity: 0.88 cP. All samples were measured by scanning 15 times using automatic measurement settings. Assays were done in triplicate.
3.2.3.4 Surface tension measurements of polymer in water
To quantify the hydrophobic nature of the polymers used in this study, the surface activity was measured by assessing the surface tension. A NIMA Surface Tensiometer equipped with a Du-Nuoy ring was used to measure the surface tension of the polymer samples at room temperature. The solutions were first prepared at a concentration of 1 mg/mL in a buffer consisting of 1.8 mM NaCl and 0.5 mM NaHCO3, adjusted to pH 7.
Polymer stock solutions are typically known to take several hours to come to equilibrium which could result in large errors during physical measurements (Riess
2003). As a result these solutions were stirred at 200 rpm at room temperature for exactly 24 h before the measurements were made.
3.2.3.5 Charge density analysis
The charge density was assessed using a particle charge detector (PCD-04 Travel;
Mütek BTG, Eclépens, Switzerland). The PCD-04 measures the charge density of a sample by titrating it with an oppositely charged polyelectrolyte standard. To determine
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
the charge polarity, streaming current detection is employed in which counter ions are segregated from their respective colloids via a reciprocating piston, resulting in a measurable current. Solutions of low Mw PDADMAC and sodium poly(ethylene sulphonate) (PES-Na) were used as titrants for anionic and cationic systems, respectively, at a concentration of 0.001 N. Charge density was calculated following:
Charge Density = (Equation 3.1) 𝐶𝐶 ∗ 𝑉𝑉 𝑀𝑀 In which C is the titrant concentration (0.001 N), V is the volume of titrant used (L) and
M is the mass of the polymer in solution (g).
3.2.3.6 Bubble characterisation through AFM measurements
Direct force measurements were carried out between two bubbles in solutions of
PDADMAC-BCF, PDADMAC, PDMAEMA-C10 and PDMAEMA. From these measurements and the use of the Chan-Dagastine-White (CDW) (Chan et al. 2001,
Tabor et al. 2012) model, the surface potential of the microbubble interface could be ascertained. An Asylum MFP-3D atomic force microscope (Asylum Research, Santa
Barbara) was used to conduct the measurements and cantilever spring constants were determined with the use of the Hutter and Bechhoefer method (Hutter et al. 1993). AFM measurements were undertaken in collaboration with the Particle Fluids Processing
Center (PFPC) at the University of Melbourne.
Prior to use, the glassware was soaked in a 10% Ajax detergent solution for 1 h and then a 10% nitric acid solution for 1 h. Milli-Q water (resistivity of 18 MΩ cm at 25 °C) was used to thoroughly rinse the glassware between each solution. Custom cantilevers with
a gold disc with the approximate dimensions of 480 μm × 50 μm × 2 μm(Vakarelski and having et al. 2010). The ~ 65 ~ a diameter of 45 μm located at the end were used Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
cantilevers were soaked in a solution of 1-decanethiol in ethanol (Mir et al. 1995,
Vakarelski et al. 2010), making the gold disc hydrophobic, to allow a bubble to be attached to the gold disc. To ensure a suitable signal from the laser was received by the photodetector within the AFM, a layer of approximately 5 nm of chromium and then an approximate 10 nm layer of gold was sputter coated (Emitech K575X) onto the back of the cantilevers. The measured spring constants for the cantilevers used within these measurements were between 0.083 ± 0.01 N/m and 0.22 ± 0.02 N/m.
All measurements were conducted in the fluid cell designed for the AFM (Asylum
Research) which has replaceable round glass surfaces. Before each measurement the hydrophobicity of the glass surfaces was increased by boiling them in n-propanol for 4 h (Biggs et al. 1994). Bubbles were generated ultrasonically (Undatim Ultrasonics D- reactor) onto the glass surfaces with a power of 25 W and a frequency of 515 kHz
(Vakarelski et al. 2008, Balasuriya et al. 2012). This procedure was conducted within the desired solution and the generated bubbles had radii between 40 and 80 micrometres. Once bubbles were generated, the fluid cell was placed within the AFM and a prepared cantilever was lowered into solution. With the use of an optical microscope (Nikon Ti-2000), the bubbles were sighted on the glass surface. The cantilever was lowered onto a suitable bubble and upon raising the cantilever the bubble was affixed to the gold disc of the cantilever. Another bubble on the surface was located and aligned axisymmetrically. This was achieved first with the optical microscope and subsequently with the piezo actuators of the AFM. Once the bubbles were aligned, direct force measurements were then performed. All collision velocities used within this work were below 300 nm/s to ensure that hydrodynamic fluid flow would not influence the forces of interaction between the colliding bubbles (Manor et al. 2008).
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
Upon completion of the direct force measurements, the CDW model was used to quantitatively describe the interaction forces between colliding bubbles. This model has been used and tested in previous studies (Tabor et al. 2011, Browne et al. 2015b,
Browne et al. 2015a) and accounts for interfacial deformation of bubbles during their collisions. This allows a model AFM force curve to be constructed based on fundamental surface force descriptions from theory, which is then compared to the experimentally gained force curve. The model requires the bubble radii, contact angles and interfacial tension to be experimentally measured as inputs into the calculation. This allows the surface potential, the only remaining free parameter within the calculation, of the bubble interface to be determined. In addition, at higher applied force between bubble or drop pairs, a simplified analytic formula derived from the CDW model has been developed that allows one to extract surface or interfacial tension from these types of force measurements between bubble or drop pairs. This method has routinely shown parity between the surface or interfacial tension values extracted from the AFM measurements and independently measured values via pendant drop at the same solution conditions (Tabor et al. 2012).
3.2.3.7 Effluent analysis
Analysis of the treated water after the jar tests included measurement of algae cell concentration achieved by cell counting using a haemocytometer and Leica DM500 light microscope (Leica Microsystems Ltd, Switzerland) and measurement of the zeta potential of treated water using a Zetasizer Nano ZS (Malvern, Australia).
The residual polymer detection was carried out through fluorescence spectroscopy where fluorescence excitation emission matrices (F-EEMS) were obtained and analysed using a 1 cm path length quartz cuvette (Starna, Australia) and a Horiba Aqualog
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
Fluroescence Spectrophotometer. The F-EEMS were only obtained on effluent samples that were treated with PDADMAC-BC and PDADMAC-BCF due to the presence of benzene rings in the polymers that fluoresced at emission and excitation wavelengths of approximately 280 nm and 260 nm, respectively (Figure S6) (Birks et al. 1965, Schwarz et al. 1976). Fluorescence intensities were measured in triplicate at excitation and emission wavelengths each ranging from 200–700 nm in 2 nm increments. The photomultiplier tube (PMT) voltage was set at 800 V and excitation and emission slit widths of 2 nm were utilised. Raman scans of MilliQ water in a sealed cell (Varian,
Australia) were obtained at an excitation wavelength of 348 nm over the emission range of 380–410 nm for the calculation of the area of the Raman peak. The area of the
Raman peak was used to normalise fluorescence intensity of all spectra, which were expressed in Raman units (RU). A linear calibration curve was obtained by making a plot of varying concentrations of PDADMAC-BC and PDADMAC-BCF against maximum intensity, as shown in Appendix A, Figures A4, A5.
3.3 Results
3.3.1 Characterisation of algae and cyanobacteria
All the cells had similar spherical, unicellular morphology when grown in the laboratory and were comparable with previous studies (Henderson et al. 2010b, Yap 2013). The physical diameter of the spherical cells of CV was 1.7 times larger than both MA555 and MA564 (Table 3-1). Each of the three strains examined were found to have widely differing charge; for example, the MA555 had a zeta potential of -17.0 ± 1.0 mV while that of MA564 and CV were -31.1 ± 1.0 mV and -27.6 ± 4.3 mV, respectively.
Similarly, the charge density also varied with CV having a charge that was almost 12 and 10 times lower (-0.96 ± 0.6 x10-7 meq/cell) than that of MA564 (-15.8 ± 0.8 x10-7
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
meq/cell) and MA555 (-9.3 ± 0.6 x10-7 meq/cell), respectively. Although the cell diameters and charge characteristics for both M. aeruginosa cultures were similar to those described by (Yap et al. 2014b), it was seen that the characteristics obtained for
CV were different from those described by (Henderson et al. 2008b), illustrating the importance of undertaking such detailed characterisation studies.
Table 3- 1. Physical and charge characteristics of Australian strains of MA (564 and 555) and CV, and UK strains of MA and CV.
Properties Diameter Average cell Zeta Potential Charge concentration at the Density onset of stationary phase Units µm × 107 cells/ mL mV × 10-7 meq/ cell MA555 3.0 ± 0.7 1.79 ± 0.08 -17.0 ± 1.0 -9.3 ± 0.6 MA564 3.1 ± 0.6 2.01 ± 0.12 -32.8 ± 0.6 -15.8 ± 0.8 CV 5.2 ± 0.6 1 ± 0.3 -27.6 † 4.3 -0.96 ± 0.6 MA UK strain †‡ 5.4 ± 0.8 † - -19.8 ± 1.5 † -0.02 ‡ CV UK strain †‡ 4 ± 1.1 † - -32.3 ± 0.6 † -0.85 ‡ ‘-’ indicates no data available
† Data obtained or calculated from (Henderson et al. 2008c)
‡ Data obtained or calculated from (Henderson et al. 2010b)
3.3.2 Characterisation of functionalised polymers
All polymers were cationic at pH 7 with the charge and zeta potentials varying from
1.21 to 6.55 meq/g and +30 mV to +65 mV, respectively (Table 3-2). It was observed that the charge densities of the hydrophobically modified PDADMAC polymers were less positive (4.1-4.3 meq/g) than that of commercially available PDADMAC (6.46 meq/g). In contrast, PDMAEMA functionalised with 1-bromodecane was found to have
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
more than double the charge density (2.76 meq/g) in comparison to the unquaternised
PDMAEMA (1.21 meq/g). Variations in the charge densities after modifications to the polymer backbone have been observed in previous studies (Yap 2013, Kim et al. 2014) where factors such as alkylation and backbone protonation of the un-reacted repeat units were shown to influence the actual charge density of the polymers.
The modified polymers had higher surface tensions in comparison to their respective native scaffolds when measured using the Du-Nuoy ring method (Table 3-2). It was also seen that the surface tensions of all the PDADMAC polymers were greater than 65 mN/m when compared to the PDMAEMA polymers which were 44 - 69 mN/m in this study and in prior PosiDAF studies (Yap et al. 2014b). Additionally, PDADMAC polymers quaternised with aromatics displayed higher surface tensions (>71 mN/m) in comparison to the aliphatics which were in the range of 64-69 mN/m. The high surface tensions observed for PDADMAC polymers despite the inclusion of hydrophobic aromatic groups, are possibly due to the high concentrations of quaternary amine groups; the quaternisation completion was over 90% for PDADMAC polymers (Table
3-2) whereas in previous PDMAEMA studies it was 0-75% (Yap 2013). Similar observations were made in other studies where an increase in the surface tension was observed with an increased degree of quaternisation regardless of the side chain length
(Vamvakaki et al. 2001, Ni et al. 2006). The high surface tension values demonstrate tendencies towards lower surface activity, indicating an overall affinity for the water phase and thus a shift in the hydrophile-lipophile balance (HLB). Therefore, characterising the surface activity of polymers on the microbubble surface rather than conventional determination of surface tension through Du-Nuoy ring method will provide a more appropriate characterisation of the hydrophobic nature of the polymers.
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Chapter 3 Table 3- 2. Characterisation of polyelectrolytes used in PosiDAF jar tests
Polymer PDADMAC PDADMAC–C5 PDADMAC–C10 PDADMAC–BC PDADMAC–BCF PDMAEMA PDMAEMA-C10
Structure Polymer design, polymer design, Polymer
Quaternisation 100 92.2 93.8 90.9 0 52 ~
71 Completion (%) ~
Charge Density 6.46 ± 0.2 4.33 ± 0.21 4.1 ± 0.2 4.17 ± 0.08 1.21 2.76 - bubble & PosiDAF studies PosiDAF & bubble (meq/g)
Zeta Potential 55.6 ± 7.06 37.8 ± 8.2 52.4 ± 2.69 55.3 ± 6.3 49.1 ± 3.5 59.8 ± 1.2 solution (mV)
Surface Tension 69 ± 1 64.1 ± 0.5 73.5 ± 0.1 71.9 ± 0.5 44 ± 0.3 69 ± 0.1 (mN/m) Not measured as polymer did not go into into go not did as polymer Not measured
Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
3.3.3 Polymer-bubble interaction characterisation
With the exception of PDMAEMA-C10, the surface tensions of all polymers at the microbubble air-liquid interface were almost equivalent to that of Milli-Q water until the concentrations were increased to at least 0.5 mg/L (Table 3-3). The low surface tension (44 mN/m) observed for PDMAEMA-C10 at 0.3 mg/L indicates that this polymer adsorbed to the air-water interface more readily than the other polymers, possibly due to the migration of the hydrophobic alkyl groups to the air-water interface.
Furthermore, an increase in the concentration of the same polymer resulted in a consequent increase in the surface activity (Table 3-3), which can be directly attributed to a greater surface density of polymer molecules at the interface.
While no coalescence was seen for bubbles coated with the PDMAEMA polymers, it was interesting to observe PDADMAC and PDADMAC-BCF coated bubbles coalescing at polymer concentrations of 2 mg/L (Table 3-3). However, it was also observed that when the concentration of PDADMAC-BCF was further increased to 5 mg/L, no coalescence was observed and the surface tension was seen to rapidly increase to 68 mN/m. The high surface tension of PDADMAC-BCF coated bubbles at 5 mg/L indicates little polymer adsorption on the bubble surface which was most likely due to an equilibrium existing between the polymer adsorbed at the gas-liquid interface and that free in bulk solution as hypothesised in a previous study (Henderson et al. 2008d).
Overall, the surface tension measurements from the AFM studies were found to contrast with those carried out by the Du-Nuoy ring method. For example, PDMAEMA-C10 had the highest surface tension as measured using a Du-Nuoy ring surface tensiometer and thus may have been expected to have the least attachment; however, in contrast, it was shown to readily adsorb on the bubble at a concentration of 0.3 mg/L. Furthermore,
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
PDMAEMA which had the lowest surface tension (44 mN/m as detected by Du-Nuoy ring) was found to have poor polymer adsorption at 0.3 mg/L. These results show that conventional surface tension measurements of polymer solutions by Du-Nuoy ring method do not elucidate polymer-bubble interactions when compared to measurements of surface activity of the polymers at the microbubble through advanced physical measurements such as AFM.
From Table 3-3, it is also seen that the bubble surface potentials as measured via AFM became more positive with increasing polymer dose and as the AFM surface tensions decreased. The surface potential ranged between -65 ± 15 mV at minimum, similar in magnitude to a that of bubble in the absence of surface active species (Tabor et al.
2012), to +65 ± 15 mV at maximum polymer concentrations in line with the increasing adsorption of polymer onto the bubble surface with increasing polymer concentrations.
However, large uncertainties in the surface potentials were observed for all the polymers and at all concentrations (Table 3-3). Similar uncertainties have been observed for surface potentials of bubbles coated with PDMAEMA and its variants (Yap et al.
2014b), polyacrylamides (Oliveira et al. 2011) and non-polymers such as aluminium hydroxides (Han et al. 2006b) due to factors such as pH fluctuations (Oliveira et al.
2011) and polymer hydrolysis (Holmberg et al. 2003, Bolto et al. 2007, Oliveira et al.
2011).
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
Table 3- 3. Characterisation of polymer coated bubbles in PosiDAF at pH 7
Polymer used to Concentration Surface Potential Surface Tension modify bubble (mg/L) (mV) calculated from AFM force curve (mN/m) PDMAEMA 0.3 -65 ± 15 70 0.5 +55 ± 25 58 2.0 +50 ± 30 51 PDMAEMA-C10 0.3 +65 ± 15 44 0.5 +60 ± 20 42 2.0 +65 ± 15 40 PDADMAC-BCF 0.3 -65 ± 15 71 0.5 +55 ± 25 58 2.0 Coalesce – bridging N/A 5.0 +45 ± 5 68 PDADMAC 0.3 -65 ± 15 71 0.5 -65 ± 15 71 2.0 Coalesce – bridging 44
3.3.4 Effect of hydrophobically functionalised polymers on cell removal for various algae
Results from jar tests demonstrated that maximum cell removals of CV, MA555 and
MA564 were 69% ± 3%, 38% ± 4%, 93% ± 4% , respectively (Figure 3-3, Appendix A
Figures A1-A3). The effective separation of MA564 is in line with previous observations made for MA564 and UK strain of M. aeruginosa (Henderson et al.
2010b, Yap et al. 2014b) where separation of 96% - 99% was observed. A similar decrease in separation effectiveness for a UK strain of C. vulgaris (Henderson et al.
2010b) was also observed in comparison with Australian strain of CV.
Micro-floc networks started to appear simultaneously as the cell removal of CV decreased at high doses of polymers. Interestingly, these micro-floc networks that ~ 74 ~
Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
formed during the 10 minute flotation period were clearly visible to the naked eye and possibly the result of coagulation-flocculation with the free polymer in water caused by turbulence in the system. (Henderson et al. 2010b) suggested that at high doses, free polymers in the solution interact with the algal organic matter (AOM) thereby forming large suprastructures that cause steric repulsion with the high concentrations of polymer at the bubble surface, thereby impacting removal efficiency.
CV MA555 MA564 100
80
60
40 Cell removal % removal Cell
20
0
Figure 3- 3. Maximum removal efficiencies for PDADMAC, PDADMAC-BC, PDADMAC-
BCF, PDADMAC-C5, PDMAEMA and PDMAEMA-C10 when treating CV, MA555, and
MA564. For full dataset, refer to Figures A1-A3 in Appendix A.
3.3.5 PosiDAF effluent
The initial zeta potential of the system with no added polymer was found to be −27.8 ±
4.5 mV for CV, −12 ± 6.1 mV for MA555, -31.1 ± 5.1 mV for MA564 and, in each
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
experiment, this did not change until at least 0.0005 meq/L of polymer had been added
(Appendix A Figures A1-A3). From Figure 3, it was observed that for all the cultures, a considerable change in the zeta potential, as well as a charge reversal took place. This was apparent for all polymers except PDADMAC-BCF and PDMAEMA-C10.
Specifically, for PDADMAC-BCF and PDMAEMA-C10, the zeta potential seemed to remain stable between -22 to -11 mV and -19 to -6 mV, respectively. From Figure 3-4, it is also apparent that for the same polymers, the zeta potential at maximum cell removal was found to be lower than the other polymers trialled. This supports observations made by (Yap et al. 2014b) who noted that polymers which have a strong hydrophobic character similar to surfactants have the best bubble adhesion as detected by stable zeta potentials and vice versa.
Figure 3- 4. Comparison of the zeta potential ranges obtained for the treated effluent for the full dose ranges of PDMAEMA, PDMAEMA-C10, PDADMAC, PDADMAC-BCF,
PDADMAC-BC, PDADMAC-C5 on CV, MA555, and MA564. ( ), ( ), ( ) represent zeta potential at maximum removal of CV, MA555, and MA564, respectively. For full dataset, refer to Figures A1-A3 in Appendix A. ~ 76 ~
Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
3.3.5.1 Residual polymer detection and quantification
When the treated effluent was tested for the presence of polymers (Figure 3-5), a polymer peak (P1) was observed to fluoresce at 280 nm (emission) and 260 nm
(excitation), tryptophan like (T1) and chlorophyll a and b like (C1 and C2) peaks were emitted at 340 nm and 680 nm, respectively, and in line with results from prior studies
(Birks et al. 1965, Schwarz et al. 1976, Henderson et al. 2009a). It was also observed that the proportion of fluorinated polymer present in the treated effluent was nearly four to six times lower (~12% - 25%) than that of the non-fluorinate polymer which was found to have residual concentrations up to 85% of the initial polymer dose (Figure 3-
6). The polymer residuals also slightly increased as the dose increased; this was particularly apparent for PDADMAC-BCF while treating MA564 and CV. This observation along with the decrease in cell removal at higher concentrations for CV
(Appendix A Figure A2), suggests that steric interactions between the polymer-AOM aggregates with polymer on the bubbles contribute to the fluctuations in cell removal observed in PosiDAF as hypothesised previously (Henderson et al. 2010b). The results obtained while evaluating PDADMAC-BCF concentrations in the treated water also corroborate well with the charge measurements observed in Figure 3-4 wherein the fluorinated polymer was found to have highly stable negative zeta potential. This observation therefore, clearly indicates a lower concentration of the polymer in the treated effluent and lower chemical contamination ahead of further pre-treatment.
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
Figure 3- 5. (A) F-EEM obtained while testing PDADMAC-BCF treated effluent of CV. P1 indicates the region associated with the polymer fluorescence while T1, C1 and C2 indicate the tryptophan-like, chlorophyll a and b regions, respectively. Initial PDADMAC-BCF concentration – 0.9 mg/L (0.004 meq/L). (B) F-EEM with the 240-400 nm excitation and
240-400 nm emission region magnified.
PDADMAC-BC in CV PDADMAC-BC in MA555 PDADMAC-BC in MA564 PDADMAC-BCF in CV PDADMAC-BCF in MA555 PDADMAC-BCF in MA564 90
70
50
effluent 30
10 0 0.002 0.004 0.006 0.008 0.01 0.012 % Residual Polymer in treated Initial Polymer Concentration (meq/L)
Figure 3- 6. Proportion of the initial polymer concentration dosed in PosiDAF experiments remaining in treated effluent on treating CV, MA555 and MA564. The polymers tested were PDADMAC-BC and PDADMAC-BCF.
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Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
3.4 Discussion
It was previously hypothesised that the incorporation of hydrophobic groups on the polymer backbone might lead to enhanced attachment of cationic HMPs to microbubble surfaces relative to those without any modification, and thus reduced polymer in the effluent. In this study, highly stable anionic zeta potentials of treated water were observed, indicating a low effluent polymer concentration, but only with two HMPs, namely PDMAEMA-C10 and PDADMAC-BCF (Figure 3-4). The presence of alkyl side chains in PDMAEMA-C10 has been shown to facilitate a stronger association to the bubble surface by providing sites for hydrophobic interactions (Yap et al. 2014b).
This phenomenon forces the polymers to adopt a ‘flat and tight’ conformation on the bubble surface and due to this, their interactions with the cells and the AOM were impacted (Figure 3-7). On the contrary, the aryl side chains of PDADMAC-BC and
PDADMAC-BCF are known to stack on top of each other due to π-π interactions as seen in graphene oxide based flocculants (Hyung et al. 2007, Hartono et al. 2009, Zhao et al. 2011, Chen et al. 2012, Yang et al. 2012). This stacking is speculated to force the polymer chains to project away from the bubble and prevent hydrophobic association of the HMP with the bubble, thereby causing poor polymer-bubble adhesion. This was evidenced in the case of PDADMAC-BC treated effluent where over 70% of the initial polymer dose and a highly positive zeta potential was detected. However, for
PDADMAC-BCF, the effluent analyses suggest another mechanism is at play to counter-balance the stacking effect. It is likely that this is due to the presence of fluorine molecules within the polymer, which are known to re-orient itself at the air-water interface and adsorb ahead of the three phase contact line on hydrophobic interfaces
(Mukerjee et al. 1981, Kovalchuk et al. 2014). While the π-π stacking due to the aryl groups causes the chains to project away from the bubble, the presence of fluorine ~ 79 ~
Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
molecules help the chains to anchor on the bubble surface, resulting in a balance between polymer association to the interface and chain projection away from the bubble
(Figure 3-7). This configuration would imply that more steric interactions would be encountered and consequently less polymer and charge would occupy the same area on a microbubble surface resulting in low surface potential (Napper 1983, Henderson et al.
2010b). The AFM data from Table 3-3 shows that this phenomenon is particularly evident for PDADMAC-BCF wherein the surface tensions increased to 68 mN/m after an initial decrease to 58 mN/m. However, it is to be noted that the counter balancing effect of the fluorine in PDADMAC-BCF was not apparent at lower concentrations as poor polymer-bubble adhesion was seen at 0.3 mg/L (Table 3-3). Therefore, this indicates that the concentrations of polymers dosed have a direct impact on the polymer configuration on the bubble surface and could be critical to the outcome of the PosiDAF process.
Apart from the polymer configurations, the concentrations of the polymers dosed were also seen to have a profound impact on polymer adsorption on the bubble. For example, the rapid decrease in cell removal from 70% to 35% for CV along with the zeta potential of the treated effluent at higher concentrations of the HMPs, indicates inconsistencies in polymer adsorption onto the bubbles (Appendix A Figure A2). This is supported by the fact that at concentrations of 2 mg/L or higher, the adsorption of
PDADMAC polymers on bubbles was vacillating (Table 3-3). In a previous study, the adsorption of various surfactants such as dodecyl-, cetyl-, and octadecyltrimethylammonium bromide on the microbubble surface were hypothesised to be impacted due to equilibrium shifts between surfactants adsorbed at the gas-liquid interface and those free in bulk solution (Henderson et al. 2008d). Similarly, it is suggested that equilibrium shifts between polymers adsorbed at the gas-liquid interface ~ 80 ~
Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
and those free in bulk solution to drive polymer adsorption to and from bubbles (Figure
3-7). Consequently, such shifts during DAF experiments as a result of rapid change in concentrations from t = 0 min to t = 10 mins are theorised to cause variations in polymer orientation on the bubble surface thereby eventually impacting cell removal.
Figure 3-7. Schematic depicting the equilibrium between polymer adsorbed on the bubble and that free in bulk solution and the impact of alkyl and aryl side chains on hydrophobic association with the bubble; (A) - Flat and tight configuration of PDMAEMA polymers on the bubble (B) – Loosely bound configuration of PDADMAC polymers on the bubble.
3.5 Summary
The specific conclusions drawn from this work are as follows:
• Hydrophobic modification of PDADMAC with various pendant groups for
bubble surface modification in PosiDAF was successful and the results indicate ~ 81 ~
Chapter 3 Polymer design, polymer-bubble & PosiDAF studies
that all the polymers trialled achieved increased or comparable cell removal
when compared to prior studies that utilised commercially available PDADMAC
and hydrophobically modified PDMAEMA.
• Stable negative zeta potentials between -22 mV and -6 mV were observed in
PosiDAF treated effluent across the entire dose range and for all species when
PDADMAC-BCF and PDMAEMA-C10 were used as the bubble modification
chemicals. Furthermore, the zeta potential of these two polymer treated effluents
at maximum cell removal was found to be lower than the other polymers
trialled. This indicates that very low amounts of the polymer remained in the
treated water, thereby confirming better adhesion of the polymer to the bubble
surface.
• Aromatically substituted polymers (PDADMAC-BC and PDADMAC-BCF)
were quantifiable for residual polymers in the treated effluent through F-EEM
analysis. It was observed that the treated effluent concentration of PDADMAC-
BCF was four to six times less than that when using PDADMAC-BC.
• The adsorption conformation of the polymer on the bubble surface alters
dependent on both the polymer backbone and the type of hydrophobic moiety
incorporated. PDMAEMA and its derivative were found to adsorb very tightly
to the bubble surface unlike PDADMAC derivatives which tend to project away
into the surrounding matrix as evidenced by the AFM results.
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Chapter 4
THE ROLE OF ALGAL ORGANIC MATTER IN THE
SEPARATION OF ALGAE AND CYANOBACTERIA
4.1 Introduction
It has been shown that PosiDAF, an alternative to conventional DAF that does not use coagulation-flocculation, is gaining traction because of the reduced chemical demand, absence of metal-salt contamination and the resultant increase in solid-liquid ratio
(Henderson et al. 2009b, Henderson et al. 2010b, Yap et al. 2014a). While greater than
95% cell separations have been achieved for certain algae and cyanobacteria
(Henderson et al. 2010b, Yap et al. 2014a), the separation efficiency was observed to vary depending on the species, attributed to changes in the associated algal organic matter (AOM) composition.
Typically, the AOM comprises hydrophilic polysaccharides, hydrophobic proteins and other low molecular weight soluble microbial products (Henderson et al. 2008c,
Pivokonsky et al. 2014, Pivokonsky et al. 2016). In Chapter 2B, it was observed that in a number of studies, AOM was shown to both hinder and enhance conventional coagulation and flocculation of algae (Bernhardt et al. 1985, Pivokonsky et al. 2006,
Takaara et al. 2007, Henderson et al. 2010b). For example, Bernhardt et al. (1991b) demonstrated that low concentrations of AOM can behave as a flocculant aid for
Dictyosphaerium; however, higher concentrations inhibited flocculation. More recently,
Pivokonsky et al. (2006) and Takaara et al. (2007) showed that the proteins present in the AOM from M. aeruginosa could decrease coagulation efficiency by preferentially
Chapter 4 Influence of AOM & its components
complexing with coagulants. It was therefore apparent that studying PosiDAF efficiency from the standpoint of variability in AOM composition was worthy of further investigation.
In a previous study by Yap (2013), it was determined that dosing exudates from M. aeruginosa (CS – 564/01) to cyanobacterial suspensions had an enhancing effect on cell removal. Furthermore, in the same study, it was observed that the enhancement of cell removal was caused by the formation of large fibrous web-like ‘strands’. A preliminary analysis of the AOM showed that it contained high concentrations of proteins and carbohydrates (Yap 2013). However, no further investigation into the AOM properties or the web-like ‘strands’ was conducted in that study and therefore, the link between
AOM composition and PosiDAF efficiency remains inconclusive.
Hence, the aim of this chapter was to improve the understanding of how AOM influences cell separation in the PosiDAF process. To achieve this, the separation effectiveness when performing PosiDAF jar tests using two freshwater green algae, C. vulgaris CS-42/7 and M. homosphaera CS-566/01, were contrasted against the results obtained by Yap (2013) when conducting the same experiments for M. aeruginosa strains CS-564/01 and CS-555/1; these four strains are known to be of comparable morphology but different AOM composition. This included experiments examining the influence of dosing extracted AOM stepwise into the jars. In addition, for the first time,
AOM properties and the composition of the fibrous ‘strands’ formed during jar test experiments were analysed through advanced characterisation techniques, including transparent exopolymer particle (TEP) analysis using Alcian Blue staining (Villacorte et al. 2015a) and Fourier transform infrared spectroscopy (FT-IR) imaging (Gonzalez-
Torres et al. 2017b). These experiments enabled the PosiDAF performance to be related
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Chapter 4 Influence of AOM & its components
to the AOM character, including the composition of observed fibrous networks. Overall, this chapter establishes that the presence of AOM is critical for separation by PosiDAF and that AOM character dictates the effectiveness of the PosiDAF process.
4.2 Materials & methods
4.2.1 Chemicals
Low molecular weight PDADMAC (Sigma Aldrich, Australia) was used for PosiDAF jar testing. To prepare the background sample matrix, sodium chloride (Ajax Finechem,
Australia) and sodium bicarbonate (Ajax Finechem, Australia) were made into 1.0 and
0.1 M solutions with Milli-Q water for jar testing. To adjust the pH, 32% hydrochloric acid (HCl) solution (Ajax Finechem, Australia) and sodium hydroxide (NaOH) pellets
(Ajax Finechem, Australia) were both prepared as 1.0 M HCl and 1.0 M NaOH solutions using Milli-Q water. Alcian blue 8GX powder (Sigma-Aldrich, Australia) was dissolved in 3% acetic acid solution prior to use as a stain for charged acidic polysaccharides (also referred to as TEP). A solution of 8% paraformaldehyde (PFA)
(ProSci Tech, Australia) was used as a fixation agent to prevent possible biochemical changes that could occur while drying samples for FT-IR analysis following Gonzalez-
Torres et al. (2017).
4.2.2 Algae and Cyanobacteria
C. vulgaris CS-4/7, M. homosphaera CS-556/01 and two strains of M. aeruginosa, CS-
555/1 and CS-564/01, known hereafter as CV, MH, MA555 and MA564, respectively, were obtained from the Commonwealth Scientific and Industrial Research
Organisation’s (CSIRO) Australian National Algae Culture Collection (ANACC)
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Chapter 4 Influence of AOM & its components
(Hobart, Australia) and recultured in growth media according to the procedures outlined in Chapter 3, Section 3.2.1.2.
4.2.3 Algal Organic Matter
The AOM was separated from the cells to produce three AOM solutions, based on the procedure outlined by Yap (2013). Firstly, cells were separated from AOM by centrifugation at 10,000 x g for 15 minutes with an Allegra® X-15R Centrifuge
(Beckman Coulter, Australia) (Henderson et al. 2008c). To remove any additional tightly-bound AOM, each aliquot of pelleted cells was resuspended in their respective media without micronutrients by vortexing for 20 seconds, followed by another cycle of centrifugation. The supernatants from the centrifuge cycles were collected and combined to give AOMC. An aliquot of this supernatant (AOMC) was then filtered using
934-AH glass microfiber filters (1.50 µm nominal pore size, Whatman, USA) to remove cell debris to give AOM1.50. The size of AOM molecules has previously been implicated in process performance (Henderson et al. 2010a) and, hence, a further aliquot of the
AOM1.50 solution was then filtered using a 0.45 µm polyethersulphone (PES) syringe filter, resulting in AOM0.45, thus enabling investigation of the impact of AOM size on
PosiDAF performance.
4.2.4 Algal System Characterisation
4.2.4.1 Cell Properties
A haemocytometer and light microscope (DM500, Leica Microsystems Ltd,
Switzerland) were used for cell counting. The cell size measurements were made using a Mastersizer 2000 (Malvern, UK). Cultures were adjusted to pH 7 with 1 M HCl for charge analysis which was undertaken using two techniques: zeta potential
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Chapter 4 Influence of AOM & its components
measurements were conducted with a Zetasizer Nano ZS (Malvern, UK) and charge demand analyses with a PCD-04 Particle Charge Detector (Mütek BTG, Switzerland).
Each analytical procedure was conducted in triplicate.
4.2.4.2 Analysis of Algal Organic Matter (AOM)
The AOM extracted from all four species was analysed as described in this section.
Each of these analyses, with the exception of the acidic carbohydrate (frequently referred to as TEP) analysis, was previously conducted for MA564 and MA555 by Yap
(2013); however, they were repeated to ensure consistency in AOM composition with previous work. As it was determined that there were not significant differences between
AOM results obtained in the current work and in previous work (Yap 2013), in this
Chapter, unless otherwise stated, the data obtained for MA555 and MA564 was averaged with those data that were previously obtained.
The three AOM solutions (AOMC, AOM1.50 and AOM0.45) from all four species were analysed with a TOC–Vsch Analyser (Shimadzu, Australia) to determine their concentration as total organic carbon (TOC). Protein and carbohydrate assays in AOMC,
AOM1.50 and AOM0.45 samples were conducted using the modified Lowry method
(Frølund et al. 1995) and phenol-sulphuric acid method (Zhang et al. 1999b), respectively. Bovine serum albumin (BSA) and D-glucose were used as calibration standards and the resultant UV absorbance was analysed at λ = 750 nm and λ = 480 nm, respectively. Each measurement was conducted in triplicate.
The AOM0.45 samples were further analysed using size exclusion liquid chromatography with organic carbon and UV254 (LC-OCD Model 8) (DOC Labor, Germany). A
Toyopearl TSK HW-50S column, that has a particle size of 30 mm, length of 250 mm and internal diameter of 20 mm, was used. The mobile phase employed was phosphate ~ 87 ~
Chapter 4 Influence of AOM & its components
buffer at pH 6.37 (2.5 g/L KH2PO4 and 1.5 g/L Na2(HPO4)2·H2O) at a flow rate of 1.1 mL/min. An injection volume of 1 mL was used to analyse the samples. From this analysis, the apparent molecular weight of molecules within the AOM0.45 with respect to
DOC and UV absorbing compounds was analysed (Huber et al. 2011b). Note that it was not possible to analyse AOMC or AOM1.50 as the chromatographic column is protected by an in-line 0.45 µm filter.
Charge measurements were conducted on the three AOM samples from all species after correcting them to pH 7 with 1 M HCl. Zeta potential measurements were conducted with a Zetasizer Nano ZS (Malvern, UK) and charge demand analysis with a PCD-04
Particle Charge Detector (Mütek BTG, Switzerland). Each measurement was conducted in triplicate.
The detection and quantification of the acidic carbohydrates, in the AOM was conducted for all four species for the first time in PosiDAF studies. Alcian Blue staining was performed on cells collected during the onset of the stationary phase of cell growth, based on the procedure outlined in previous studies (Passow et al. 1995, Villacorte et al.
2015a, Villacorte et al. 2015b). Firstly, the pH of Alcian Blue solution was reduced to
2.5 by the addition of 1M HCl, to ensure that both sulphated and carboxylated polysaccharides were detectable during the staining process (Passow et al. 1995). All four cultures were then filtered using 0.45 μm polycarbonate membranes and stained with Alcian Blue. As a result, the acidic carbohydrate staining was performed on
AOM0.45 samples only and not on AOMC or AOM1.45. Images of the stained acidic carbohydrates were obtained through a light microscope (DM2500, Leica Microsystems
Ltd, Switzerland) and processed with Image J software. The acidic carbohydrates were quantified from the image by selecting random areas of 0.18 mm2.
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Chapter 4 Influence of AOM & its components
4.2.5 Jar Test Procedures
4.2.5.1 PosiDAF jar tests
PosiDAF jar testing of CV and MH was carried out using PDADMAC as described in
Chapter 3, Section 3.2.2.3 (Figure 1-1(B)). The results from these jar tests were then compared and contrasted against those obtained previously for MA555 and MA564 by
Yap (2013).
4.2.5.2 Investigation of the influence of AOM on PosiDAF performance
PosiDAF jar tests as described in section 4.2.5.1 were repeated for CV and MH by fixing the PDADMAC dose at the point where maximum cell removal was observed for the respective species. However, in these experiments, the AOM concentration was varied by first separating AOM from the cells (according to the method outlined in section 4.2.3) and resuspending cells in buffer solution to the appropriate concentration, and then reintroducing AOM into the cell systems with mixing at 50 rpm for 60 seconds to the desired AOM concentration. The results obtained for these experiments were compared against those obtained by Yap (2013) for MA555 and MA564.
4.2.5.2.1 Examining the impact of “native” AOM of the algal and cyanobacterial species on PosiDAF performance
Strains of CV and MH with the AOM removed were used to prepare jar test samples of
8 × 105 cells/mL. Jar tests were first performed in the absence of AOM. Subsequently, the AOMC obtained from the strain under investigation was reintroduced to prepare cell solutions with AOM concentrations ranging from 0.25 to 2.0 mg/L. The results from these experiments were then compared to those obtained for the same tests conducted on MA555 and MA564 by Yap (2013).
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Chapter 4 Influence of AOM & its components
4.2.5.2.2 Examining the impact of “foreign” cyanobacterial AOM on algal and cyanobacterial PosiDAF performance
The rationale behind this experiment was to determine whether the separation of algal cells could be improved upon by the addition of “foreign” AOM, particularly from a species that was well-separated. Previously, Yap (2013) had observed that dosing AOM from MA564, which was previously observed to be the most effectively separated strain, into the jars containing MA555 cells improved cell removal from < 35% to >
90%. In this study, CV and MH were used to provide cell cultures for the jar test; and again, extracted MA564 AOM was introduced. The AOMC, AOM1.50, and AOM0.45 from MA564 were introduced to prepare concentrations ranging from 0.25 to 2.6 mg/L.
The tests were repeated using AOM1.50 and AOM0.45 over the same dosing range to determine whether AOM size impacted treatability. The results from these experiments were then compared to those obtained for MA555 by Yap (2013).
4.2.5.3 Analysis of flotation performance
Analysis of jar test performance was similar to the methods described in section 3.2.3.7.
This included cell counting using a haemocytometer or Sedgewick Rafter to determine residual cyanobacteria cell concentration (Henderson et al. 2010b) and a zeta potential analysis performed using a Zetasizer Nano ZS (Malvern, UK). All measurements were undertaken in triplicate.
4.2.5.3.1 Fourier transform infrared spectroscopy (FT-IR) imaging analysis
The fibrous ‘strands’ from the AOM of MA564, that were previously described by Yap
(2013) as flotation enhancers, were generated once again in this study so that they could be analysed by FT-IR imaging. The strands were extracted from the jars during
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Chapter 4 Influence of AOM & its components
PosiDAF experiments using a plastic pipette, re-suspended in the fixation agent and diluted using double concentrated MLA stock solution, following the procedure outlined by Gonzalez-Torres et al. (2017a). The fixed samples were then refrigerated (4
˚C) for 24 hours and then transferred to a stainless steel slide to dry in a desiccator for
24 hours prior to FT-IR imaging analysis to prevent water absorption. The stainless steel slide has the advantage that it has a low cost and is reusable and reflects all infrared light without contributing to the infrared spectrum, unlike a standard glass microscope slide.
Once the samples were dried in the desiccator, the FT-IR imaging measurements were performed using a PerkinElmer Spotlight 400 FT-IR microscope (USA). FT-IR images were collected over the 4000-750 cm-1 using reflectance mode. The spectrum at each point was created by co-adding 32 scans and recorded using the spectroscopic software
Spectrum Image (version R1.8.2 PerkinElmer, USA). Background spectra were collected from a region free of sample on the reflective slide. The resulting spectra were atmospherically corrected to eliminate the effects of moisture and carbon dioxide in the atmosphere. FT-IR images were obtained as the FT-IR peak height is proportional to the quantity of chemical present on the sample, mapping peak height intensity across the sample produces a chemical image for each type of chemical region. Images were selected from the edges and centre of the sample deposited on each slide and the peak intensities were normalised to enable a meaningful comparison of colours between each image.
The FT-IR image files obtained were processed using the spectroscopic software
Spectrum (version 10.5.1 PerkinElmer, USA). An interactive baseline correction was applied to each individual spectrum to specify the base points (2935, 2915, 1640, 1620,
~ 91 ~
Chapter 4 Influence of AOM & its components
1550, 1530, 1350, 1330, 1260, 1240, 1110, 1090, 1050, 1030 cm-1) and to produce the spectra. Chemimaps were created from the FT-IR image files in specific ranges (2935-
2915 assigned to lipids, 1640-1620 assigned to amide I, 1550-1530 assigned to amide II
/ COO-, 1350-1330 assigned to carboxylate anion, 1110-1090 assigned to COC carbohydrates, 1050-1030 cm-1 assigned to C-OH carbohydrates). The chemimaps created for the peak range between 1640-1620 cm-1 (proteins), 1350-1330 cm-1 (acidic carbohydrates) and 1050-1030 cm-1 (carbohydrates) were overlaid to obtain the FT-IR images using the Spectrum software. Proteins, acidic carbohydrates and carbohydrates were selected as their synergistic interactions have previously been implicated to have an influence on cell separation (Mopper et al. 1995, Henderson et al. 2010a, Villacorte et al. 2015b, Pivokonsky et al. 2016). The spectra were also extracted and plotted by
Microsoft Excel (Version 2013) to obtain the cross section profiles.
4.3 Results
4.3.1 Cell System Characterisation
The CV, MH, MA564 and MA555 cells had similar spherical, unicellular morphology when grown in the laboratory. The physical diameter of the spherical cells of CV was found to be bigger (5±0.6 µm) than those of MH (2.8 ±0.5 µm), and the previously tested MA555 (3.0±0.7 µm) and MA564 (3.1±0.6 µm) ((Yap 2013),Table 4-1A). The sizes of MA555 and MA564 were almost 40% lower than that observed for M. aeruginosa (CCAP 1450/3) that was used in a previous PosiDAF study (Henderson et al. 2010b) while that of CV was almost 1.5 times the size of the strain used by
Henderson et al. (2010b).
The cultures were also found to have widely differing charge. For instance, MH had a highly negative zeta potential (approx. -35 mV) and charge (approx. -15x10-7 meq/cell); ~ 92 ~
Chapter 4 Influence of AOM & its components
in comparison, CV had a less negative zeta potential (-24.6 ± 4.3 mV) and charge density (-2.8x10-7 meq/cell) (Table 4-1A). Furthermore, the charge and zeta potential of
MA564 was comparable to that of MH whereas the charge and zeta potential observed in MA555 was the lowest among all the strains tested in this study (Table 4-1A, Yap
(2013)). Similar trends were observed in the zeta potential of the AOM fractions of the cultures (Table 4-1A). Interestingly, the charge density and zeta potential of the AOM fractions were found to become less negative for every fraction with the most negative values for AOMC and the least negative for AOM0.45. The less negative charge observed for the AOM0.45 fractions when compared to the other fractions in all the species points to the presence and high concentration of low molecular weight charged compounds.
Collectively, the disparities in charge and size of the algal cell suspension indicate that the relative dose per cell for all the species could fluctuate for the same polyelectrolyte concentration.
~ 93 ~
Table 4-1 A. Physical and charge characteristics of the cultures and different AOM fractions extracted from MA555, MA564, MH and CV in Chapter
comparison with previously tested UK strains of MA (CCAP 1450/3) and CV (CCAP 211/11B). 4
Properties Diameter Average cell Zeta Potential Charge Density
concentration at the onset of stationary phase
Culture (AOMC) (AOM1.50) (AOM0.45) Culture (AOMC) (AOM1.50) (AOM0.45) Units µm × 107 cells/mL mV mV mV mV × 10-7 × 10-7 meq × 10-7 × 10-7 meq/cell /cell meq/cell meq/cell
CV 5.2 ± 1.3 ± -24.6 † -21.3 ± -17.1 ± -15.7 ± - 2.8 ± -1.9 ± -1.75 ± -1.2 ± 0.6 0.3 4.3 4.3 2.4 1.8 0.6 0.22 1.5 0.02 2.8 ± 0.9 ± -35.1 ± -28.9 ± -25.8 ± -21 ± -14.8 ± - 8.4 ± - 7.6 ± - 6.96 ± ~ MH
94 0.5 0.2 5.6 2.9 2.3 3.4 0.6 0.8 0.3 0.87 ~
*MA555 3.0 ± 1.79 ± -17.0 ± -8 ± -9 ± -8.3 ± -9.3 ± -5.6 ± -4.8 ± -4.3 ±
0.7 0.08 1.0 1.6 3.6 1.9 0.6 1.3 0.2 1.0 components & its AOM of Influence *MA564 3.1 ± 2.01 ± -32.8 ± -26.2 ± -22.7 ± -20.2 ± -14.1 ± -7.9 ± -6.1 ± -5.8 ± 0.6 0.12 0.6 5.1 6.6 1.8 0.8 1.8 0.5 3.0 a MA (UK) 5.4 ± - -19.8 ± - - -21.5 ‡ -0.02 ‡ - - -0.01 ‡ 0.8 † 1.5 † a CV (UK) 4 ± - -32.3 ± - - -18.9 ‡ -0.85 ‡ - - -0.72 ‡ 1.1 † 0.6 † ‘-’ indicates no data available, * – Indicates the averaged data determined from this study and Yap (2013) a - Indicates all data obtained or calculated from Henderson et al. (2010b) and Henderson et al. (2008c).
Chapter 4 Influence of AOM & its components
AOMC extracted from strain MH, CV and MA555 had a third of the TOC concentration of what was observed in MA564. Conversely, the DOC of the extract (AOM0.45) was found to be slightly more concentrated for MA555 at 9.6 × 10-10 mg/cell than that observed for MA564, MH and CV (Table 4-1B). When the biopolymer composition was analysed, it was noticed that all fractions of MA564 AOM had the highest total protein and carbohydrate concentration, and at least twice as much protein:DOC and thrice as much carbohydrate:DOC ratios than the AOM from other strains (Table 4-1B).
However, the protein:DOC and carbohydrate:DOC ratios between the AOM0.45 and
AOM1.50 fractions from every strain were very similar. The carbohydrate:DOC ratio of
MA555 (0.38 mg/mg) was also similar to that of the UK strain of MA; however, the protein:DOC ratio of the UK strain was found to be much greater than that for the strains used in this current paper at 0.64 mg/mg (Table 4-1B). With AOMC, it was revealed that the protein:TOC and carbohydrate:TOC ratios were much lower than the
AOM0.45 and AOM1.50 fractions. For example, in Table 4-1B, it is seen that MA564 had a protein:TOC ratio of 0.11 ± 0.02 mg/mg while that of the AOM1.50 was 0.26 ± 0.06 mg/mg. This may due to the fact that cell debris was not effectively removed by centrifugation in AOMC, increasing observed TOC concentrations. Collectively, it can be said that the overall concentrations of biopolymers were found to be present in the greatest quantity in the AOM from MA564.
An LC-OCD analysis of AOM0.45 from the strains used in this study revealed some differences in the size fractions of the AOM from the different strains (Table 2,
Appendix B Figure B1). Specifically, MA564 had a larger fraction of biopolymers, humic substances and building blocks at 18.5%, 14.7% and 45%, respectively, in comparison to MA555, MH and CV. In contrast, the LMW neutrals fractions were the lowest for MA564 (21%) compared to MA555 (31%), MH (45%) and CV (23%), ~ 95 ~
Chapter 4 Influence of AOM & its components
respectively (Table 2, Appendix B Figure B1). These results demonstrate a tendency for higher molecular weight components to exist in the MA564 AOM.
When Alcian Blue was used to stain acidic carbohydrates (Appendix B Figure B2), aggregates of these molecules were visible under a microscope for all the species indicating that they were produced extracellularly during the onset of the stationary phase and not just via release due to the death of algal cells or environmental stress as observed previously (Passow 2002, Villacorte et al. 2015a, Villacorte et al. 2015b).
However, acidic carbohydrate concentrations varied depending on the species, with
MA564 producing more than five times more acidic carbohydrates at a surface coverage of 52% of polycarbonate membrane than MH (Table 4-1B). This is further supported by the fact that the AOM from MA564 had at least three times or higher carbohydrate concentrations when compared to the other species at the same time period (Table 4-1B,
Appendix B Figure B2 (B)). However, the presence of acidic carbohydrates was not reflected when comparing the carbohydrate concentrations of AOM0.45 and AOM1.50 fractions of the algal cultures. For instance, the carbohydrate concentrations of AOM1.50 and AOM0.45 were 8 mg/L and 7.5 mg/L, respectively (Appendix B Figure B3). With the acidic carbohydrates likely to be filtered out by the 0.45 µm membrane, it is expected that the carbohydrate concentrations of the AOM0.45 fractions would become lower than the AOM1.50 fractions; however, comparable values were observed for all the species (Appendix B Figure B3).
~ 96 ~
Table 4- 1B. Chemical composition of the different AOM fractions extracted from MA555, MA564, MH and CV in comparison with previously Chapter
tested UK strains of MA (CCAP 1450/3) and CV (CCAP-211/11B). All assays were done in triplicate. 4
Properties TOC/DOC Carbohydrate : Organic carbon1 Protein : Organic carbon1 Acidic carbohydrate concentration
(AOMC) (AOM1.50) (AOM0.45) (AOMC) (AOM1.50) (AOM0.45) (AOMC) (AOM1.50) (AOM0.45)
Units x10-10 x10-10 mg/ x10-10 mg/ mg/mg mg/mg mg/mg mg/mg mg/mg mg/mg % mg/cell cell cell
CV 15 ± 8.6 ± 9.1 ± 0.36 ± 0.41 ± 0.39 ± 0.05 ± 0.1 ± 0.12 ± 17.1 ± 2.1 0.5 0.1 0.11 0.15 0.2 0.02 0.04 0.08 4.5
MH 16.2 ± 6.7 ± 6.9 ± 0.21 ± 0.28 ± 0.29 ± 0.09 ± 0.13 ± 0.15 ± 8.6 ± ~
97 5.6 1.1 3.3 0.19 0.21 0.18 0.05 0.09 0.08 3.4 ~
MA555 *11.1 ± * 9.6 ± * 9.6 ± *0.24 ± *0.36 ± * 0.38 ± *0.1 ± *Below * Below 12.4 ± components & its AOM of Influence Detection Detection 0.8 0.4 0.1 0.02 0.11 0.03 0.03 1.1
MA564 * 35.6 ± * 10.2 ± * 8.5 ± *0.94 ± *1.34 ± * 1.36 ± *0.11 ± *0.26 ± *0.28 ± 52.7 ± 2.1 0.9 0.5 0.12 0.18 0.04 0.02 0.06 0.16 6.1 a MA (UK) - - 10 ‡ - - 0.38 ‡ - - 0.64‡ - a CV (UK) - - 10.3 ‡ - - 1.1 ‡ - - 0.4 ‡ - ‘-’ indicates no data available, * – Indicates the averaged data determined from this study and Yap (2013), a – Data obtained or calculated from (Henderson et al. 2008c). 1 – TOC for AOMc and AOM1.5, and DOC for AOM0.45.
Chapter 4 Influence of AOM & its components
Table 4- 2. A comparison of the biopolymers, humic substance, building blocks and LMW neutrals as determined from LC-OCD analysis of AOM0.45
Humic Building LMW Strain Biopolymers Substances Blocks Neutrals CV 19.3% 10.2% 19.1% 23.7% MH 5.4% 11.4% 38.5% 44.7% * MA555 12.7% 13.0% 22.9% 31.2% * MA564 18.5% 14.7% 44.6% 21.3% * Indicates the averaged data determined from this study and Yap (2013)
4.3.2 PosiDAF
When applying PosiDAF using PDADMAC, the zeta potential of the treated effluents became positive at doses above 0.008 meq/L for CV and MH (Figure 4-1). In comparison with data from Yap (2013), the zeta potential for MA555 and MA564 became positive at doses of 0.001 meq/L and 0.002 meq/L, respectively (Figure 4-1).
Despite belonging to the same species, a key difference in the effluent zeta potential between CV and C. vulgaris CCAP 211/11B was that the dose required to neutralise charge of CV with PDADMAC was was twice as high as that observed for C. vulgaris
CCAP 211/11B (Henderson et al. 2010b). However, Yap (2013) noted that for MA555, the dose required to neutralise charge with PDADMAC was one sixth of the concentration observed for MA564 (Figure 4-1).
The PosiDAF cell separation varied for the different species and strains: CV and MH reached a maximum of 62±3% and 35±2% at a polymer dose of 0.004 meq/L (Figure 4-
1), while Yap (2013) noted that MA564 and MA555 reached a maximum cell separation of 95±3% and 31±2%, respectively, at a polymer dose of 0.001 meq/L
(Figure 4-1). Above the optimal polymer dose, cell separation decreased for all the strains except MA564. A similar decrease in separation effectiveness to that obtained ~ 98 ~
Chapter 4 Influence of AOM & its components
for CV was observed for a UK strain of C. vulgaris CCAP 211/11B (Henderson et al.
2010b). Furthermore, the effective separation of MA564 is also in line with previous observations made for M. aeruginosa CS-564/01 and CCAP-1450/3, where separations of 96% - 99% were observed (Henderson et al. 2010b, Yap et al. 2014a). Given the previously described similarities in morphological characteristics, the discrepancies in cell removal and effluent zeta potential are attributed to AOM influences, interactions of polymers and other high molecular weight substances.
~ 99 ~
Chapter 4 Influence of AOM & its components
MA555 CV MA564 MH
100
80
60
40 Cell Removal %
20
0 20 B 10
0
-10
-20
-30 (mV) Zeta Potential
-40
-50 0 0.002 0.004 0.006 0.008 0.01 Dose of Polymer (meq/L)
Figure 4- 1. Comparison of PosiDAF performance with PDADMAC on CV, MH, MA555 and MA564; A – Cell removal efficiency and B – Zeta Potential of effluent Vs Dose of
PDADMAC; Hollow circular and triangular data points obtained from Yap (2013).
4.3.3 Investigating link between PosiDAF cell separation and AOM composition
4.3.3.1 FT-IR analysis of web-like strands from MA564
Web-like strands were observed to enhance flotation during the jar tests of MA564 in this study (Chapter 3) and also by Yap (2013). These strands were generated once again through PosiDAF jar tests so that they could be analysed by FT-IR. On examination of ~ 100 ~
Chapter 4 Influence of AOM & its components
the FT-IR spectra of these strands (Figure 4-2), nine regions with peaks that have been previously reported to be those of macromolecules were found (Giordano et al. 2001,
Mecozzi et al. 2001, Sigee et al. 2002, Lee et al. 2006, Murdock et al. 2009, Chon et al.
2013, Sudharsan et al. 2015) (Appendix B, Table B1). A broad peak around 3300 cm-1 in region (1) was attributed to the presence of hydrogen-bonded OH (1) caused by the hydroxyl groups in carbohydrates as well as some residual moisture present after drying. The C-H stretch caused by the lipids present in the strands was visible in all the samples (2). Peaks in region 3 were attributed to C=O stretching from aldehydes, ketones and protonated carboxylic acids. The peaks had a poor signal in this region indicative of poor concentration of the aforementioned compounds. Broad peaks in (4) and (5) were indicative of the amide binding protein, amide I (4) and amide II (5). A strong carboxylate anion peak was observed at 1340 cm-1 (6). A single peak in the
- region (7) was attributed to the sulphated esters (R-O-SO3 ) present in the samples.
However, the signal was not strong and may reflect the limited presence of the sulphated compounds. A group of overlapping peaks (8) and (9) around 1150-1050 cm-1 were observed which were the result of a contribution from different types of polysaccharides that have been detected previously in algal species (Villacorte et al.
2015b). These are speculated to be glycogen molecules that serve as energy storage in the cells (Bertocchi et al. 1990, Murdock et al. 2009, Hadjoudja et al. 2010).
FT-IR images of the spatial distribution of proteins, acidic carbohydrates and carbohydrates for the strand edge and strand centre were obtained as they provided information on the extent of aggregation or homogeneity of these macromolecules across the samples (Figure 4-3 (B) to (E)). The analysis of the FT-IR images of the strand edge (Figure 4-3B, 3C) revealed the presence of regions attributed to proteins
(red, amide I peak at 1630 cm-1), carbohydrates in Figure 4-3B (green, C-O vibration at ~ 101 ~
Chapter 4 Influence of AOM & its components
1030 cm-1) and acidic carbohydrates in Figure 4-3C (green, COO- vibration at 1330 cm-
1). The sample in Figure 4-3C showed a more homogeneous distribution of proteins and acidic carbohydrates (yellowish-orange hue) in comparison to the same sample in
Figure 4-3B which had a more localised distribution and distinctive differences between proteins and carbohydrates. By analysing the corresponding cross-section images of the strand edge (Figure 4-3B, 4-3C), it was observed that changes in the red protein line correlate well with most changes in the green acidic carbohydrate line in Figure 4-3C, whereas in Figure 4-3B, the cross-section image shows poor correlation between the red protein line and green carbohydrate line. When the FT-IR images of the strand centre
(Figure 4-3D, 4-3E) were analysed, it was seen that both acidic carbohydrates and carbohydrates had moderate homogeneity in their distribution with the proteins. This phenomenon was also validated by their corresponding cross-section images (Figures 4-
3D, 4-3E). Overall, it was observed that the yellowish-orange regions of protein-acidic carbohydrate homogeneity dominated more towards the strand edges when compared to the strand centre where both the acidic carbohydrates and carbohydrates had moderate influences. The homo- and heterogeneous distribution of proteins with acidic carbohydrates and carbohydrates, respectively, along with the correlations observed between these regions in the cross section images may be caused by the conjugation of glycoproteins and carbohydrates that has been previously observed in algal systems
(Kehr et al. 2015).
~ 102 ~
Chapter 4 Influence of AOM & its components
Figure 4- 2. Infrared absorbance spectra as a total of all 4096 individual spectra in each image, with assignment of major peaks numbered according to Appendix B Table B1.
Spectra are presented as absorbance vs wavenumber (cm-1), and have been baseline corrected and arbitrarily scaled for comparison.
~ 103 ~
Chapter 4 Influence of AOM & its components
(A)
Figure 4- 3. Image (A) - Strands from MA564 viewed under a microscope. Images (B), (C)
- 20 x 20 μm2 FTIR images of strand edge with the corresponding cross section spectra of the strand edge. Images (D), (E) - 20 x 20 μm2 FTIR images of strand centre with the corresponding cross section spectra of the centre of the strand. Red regions in images (B) to (E) are the relative intensities of the protein amide I peak at 1630 cm-1. Green regions in
(B) and (D) are the relative intensities of the carbohydrate C-O vibration at 1030 cm-1, and in (C) and (E), are the relative intensities of the acidic carbohydrate anion COO- vibration at 1330 cm-1. The regions of orange and yellow in (B) to (E) are the overlaps of the respective chemical regions in the image. For full dataset, see Appendix B Figure B4.
~ 104 ~
Chapter 4 Influence of AOM & its components
4.3.3.2 Cell separation as a function of acidic carbohydrate proportion
On investigating the link between maximum cell removal and the acidic carbohydrate concentration of the algal species, it was observed that the species which had the greatest proportion of acidic carbohydrates per mm2 of polycarbonate membrane (Table
4-1B, Appendix B Figure B2) had the best cell removal. For instance, it is seen from
Figure 4-4 that MA564 which had the highest proportion of acidic carbohydrates had the best cell removal among the strains tested. Moreover, MH and MA555 which had the lowest amount of acidic carbohydrates, had poor cell separation (Figure 4-4). While only four species were tested to study the impact of acidic carbohydrates, the indications are that the role of these molecules in PosiDAF separation would appear to warrant further investigation. This was examined in more detail by altering AOM concentration and composition.
100 y = 1.4915x + 23.436 Pearson's coefficient - 0.96±0.01 MA564 80 R² = 0.9235
CV 60
40 MH MA555 20 Maximum Cell Removal (%) Removal Cell Maximum
0 0 10 20 30 40 50 60 Acidic Carbohydrates (%)
Figure 4- 4. Comparison of maximum cell removal of CV, MH, MA555 and MA564 vs the acidic carbohydrates detected by Alcian Blue staining. Acidic carbohydrate concentration represented as percent area per 0.18 mm2 of polycarbonate membrane.
~ 105 ~
Chapter 4 Influence of AOM & its components
4.3.4 PosiDAF Jar Tests with Altered AOM Conditions
4.3.4.1 Examining the influence of “native” AOM of the algal and cyanobacterial species on PosiDAF performance
When the AOM was completely removed from the cell system, it was found that negligible cell separation was achieved for all the strains (Figure 4-5). On addition of
AOMC back into cell solution, cell separation increased, although to different extents. In jars with CV and MH cells, cell separation reached a maximum value of only 46 ± 3% at a CV AOMC dose of 0.6 mg/L and 28 ± 2% at MH AOMC dose of 0.4 mg/L while the maximum cell separation achieved for MA555 cells was 18±4% at a MA555 AOMC dose of 0.55 mg/L (Figure 4). In contrast, for MA564 cell system, 95±3% cell separation was observed at a MA564 AOMC concentration of 0.91 mg/L (Figure 4).
This was comparable with that observed in PosiDAF tests without extracting the AOM
(Chapter 3, (Yap et al. 2014a)) and confirms that the presence of AOM is critical for separation using PosiDAF, as previously hypothesised by Henderson et al. (2010b).
All the strains demonstrated similar dose response curves with respect to zeta potential.
The PosiDAF effluent zeta potential was negative prior to the addition of AOM, with charge reversal occurring at low doses of AOM (Figure 4-5). The initial negative zeta potential seen in Figure 4-5, i.e. in the absence of AOM but with 0.004 meq/L of
PDADMAC for CV and MH, and 0.0005 meq/L of PDADMAC for MA564 and
MA555, infers that less cationic PDADMAC remains in the DAF effluent in the absence of AOM. Furthermore, as AOM for all the strains has a negative charge (Table
4-1A), it would be expected that its addition would result in a PosiDAF effluent with greater negative charge; however, the opposite occurred.
~ 106 ~
Chapter 4 Influence of AOM & its components
CV MA564 MA555 MH
100
80
60
40 Cell Removal %
20
0
20 B 10
0
-10 (mV) Zeta Potential -20
-30 0 0.2 0.4 0.6 0.8 1 1.2 Dose of AOM quoted as mg of TOC/L
Figure 4- 5. PosiDAF jar test outcomes with PDADMAC for CV, MH, MA555 and MA564 cells with their respective AOM added to jars at various concentrations; A – Cell removal efficiency and B – Zeta potential of effluent vs dose of AOM; Concentrations of
PDADMAC for MA555, MA564 were 0.0005 meq/L and for CV, MH were 0.004 meq/L.
Hollow circular and triangular data points obtained from Yap (2013).
~ 107 ~
Chapter 4 Influence of AOM & its components
4.3.4.2 Examining the influence of “foreign” cyanobacterial AOM on algal and cyanobacterial PosiDAF performance
When centrifuged and unfiltered MA564 AOMC was added to the jar tests containing samples of CV and MH, cell separations greater than 90% were obtained at a dose of
2.0 mg/L; these results were similar to that obtained by Yap (2013) while dosing
MA564 AOMC into the jar containing MA555 cells (Figure 4-6 A, B, C). This shows that the AOM extracted from one strain can be used to enhance the cell separation of another strain irrespective of whether the cells floated belong to algal or cyanobacterial species.
Dosing of AOM1.50 and AOM0.45 from MA564 to jar tests containing samples of
MA555, CV and MH resulted in lower cell separations in comparison with AOMC. For example, maximum cell separations over 75% for AOM1.50 and 60% for AOM0.45 were obtained at a dose of 2.0 mg/L for the three strains (Figure 4-6 A, B, C). The 35% drop in cell removal when using AOM0.45 confirms previous observations that high molecular weight biopolymer material was crucial in enhancing the PosiDAF process (Henderson et al. 2010b).
~ 108 ~
Chapter 4 Influence of AOM & its components
100
B 75
50 Cell Removal Removal Cell %
25
100
C 75
50 Cell Removal Removal Cell %
25 0 0.5 1 1.5 2 2.5 3 Dose of AOM quoted as mg of TOC/L Figure 4- 6. Cell separation as a function of AOM dose for Experiment 2 jar tests of A –
MA555; B - CV; C – MH, using MA564 AOM to enhance flotation. Hollow circular, square and triangular data points obtained from Yap (2013).
~ 109 ~
Chapter 4 Influence of AOM & its components
4.4 Discussion: The Role of AOM in PosiDAF
In consideration of all available data, it is suggested that the AOM and its composition play a significant role in PosiDAF separation effectiveness. In this study, it was observed that cell removals across all strains were poor when PosiDAF was operated in the absence of AOM (Figure 4-5). However, in the presence of AOM, only MA564 had greater than 95% cell separation (Figure 4-1). This therefore infers that the AOM in
MA555, MH and CV has either a low cell adherence or low polymer binding capability, or both, in comparison to the AOM found in MA564. In the case of MA564, the AOM appeared to provide a physical connection between the polymer-coated bubbles and cells by adhering to or complexing with polymers and attaching to the cell surface
(Figure 4-7). Therefore, understanding the mechanisms behind the AOM’s capability to forge a link between the cells and the bubbles is critical to the overall success of
PosiDAF.
Comparisons of the AOM from all the species revealed the major differences to be associated with charge and variations in the composition of the AOM size fractions
(Table 4-1A, 4-1B). Interestingly, while MA564 and MH had comparable charge, cell size and morphology, their cell removals and float characteristics in PosiDAF were found to be vastly different (Figure 4-1). Therefore, when correlating the cell removal and AOM characterisation results of all the species, it is proposed that two factors namely: 1) composition of the AOM, and 2) changes to influent conditions such as particle (cell) size due to the formation of web-like strands that link several cells, contribute predominantly to the enhanced cell removal in PosiDAF.
The composition of the AOM plays a crucial role in the AOM-cell attachment and formation of large web-like networks that enhance flotation. As both the cells and AOM
~ 110 ~
Chapter 4 Influence of AOM & its components
are negatively charged, the presence of glycoproteins and carbohydrates in the AOM
(Henderson et al. 2008c) and cell walls (Lee 2008), and their subsequent conjugation interactions due to the presence of multiple binding sites on a glycoprotein surface (Lee et al. 1995), are deemed critical for the AOM-cell interactions – a necessity for successful PosiDAF performance, which was seen only in MA564. Conjugation is the reversible interaction caused by Van der Waals forces, hydrogen bonding or non-polar interactions that occurs between the protein and carbohydrate even in the absence of enzymes (Weis et al. 1996, Berg et al. 1999). This would serve as a cogent explanation as to why cell removal increased to greater than 90% despite the addition of negatively charged MA564 AOM to the negatively charged cell suspensions in Experiment 2 jar tests (Figure 4-6), and why cell removal increased only for MA564 AOM and not the
AOM from other strains in Experiment 1 jar tests (Figure 4-5). In addition, the conjugations between glycoproteins, acidic carbohydrates and carbohydrates may also be responsible for the formation of large web-like networks that enhanced flotation of
MA564 (Figures 3B to 3E). Such networks have been previously seen in the biomedical field, wherein enhanced binding and bridging interactions were observed (Pfaff et al.
2011), especially when studying receptor-ligand recognition and inhibition for cell uptake (Kiessling et al. 2000). Therefore, it would be a logical extrapolation to suggest that this interplay between the aforementioned biomolecules has the potential to contribute to the AOM-cell attachments and formation of large web-like structures.
From the development of DAF models such as the white water model performance equation (Haarhoff et al. 2004), it is now understood that modifications to the influent conditions such as increasing the particle size can result in elevated particle separations.
As a consequence, in PosiDAF, the presence and interaction of the aforementioned conjugated suprastructures with polymers is critical for successful flotation. This is ~ 111 ~
Chapter 4 Influence of AOM & its components
supported by the fact that when AOM from MA564 was introduced into the jars containing cells of MA555, MH and CV, the introduced AOM created several networks which increased the apparent size of the particles being separated and consequently increased cell removal to greater than 90% (Figure 4-6 A, B, C). Furthermore, the cell removal did not decrease at doses beyond the optimum level and remained greater than
90% consistently, thereby, supporting the theory that these large polymer-AOM networks enhanced flotation in PosiDAF.
Figure 4- 7. Mechanism showing the role played by AOM on PosiDAF efficiency
4.5 Summary
The specific conclusions drawn from this work are as follows:
• PosiDAF operated in the absence of AOM resulted in poor cell separation and
AOM is therefore considered a necessary system component.
• A combination of AOM concentration and character dictated the degree of cell
separation. AOM from MA564 which had the highest MW and acidic
carbohydrate content than the other strains improved PosiDAF performance.
• Web-like strands that enhanced flotation of MA564 were analysed. It was
suggested that conjugation between acidic carbohydrates and proteins could be
~ 112 ~
Chapter 4 Influence of AOM & its components
dominant at the strand edge whereas the centre of strand may comprise
conjugates of proteins with acidic carbohydrates as well as carbohydrates.
• The interplay between proteins, carbohydrates and acidic carbohydrates with the
cells and cationic polymers is speculated to facilitate bubble attachment and
flotation. Therefore, a complete protein and carbohydrate characterisation is
recommended to identify the nature and concentration of proteins and
carbohydrates that have the greatest impact on cell removal.
~ 113 ~
Chapter 5
THE IMPACT OF PROTEIN-CARBOHYDRATE
CONJUGATE INTERPLAY ON THE SEPARATION OF
ALGAE AND CYANOBACTERIA USING THE POSIDAF
PROCESS
5.1 Introduction
Prior research showed that the impact of differing concentration and characteristics of
AOM between algal and cyanobacterial species had the potential to generate large variations in cell separation efficiencies using PosiDAF for different species
(Henderson et al. 2010b). It was then shown in the work of (Yap 2013), further explored in Chapter 4, that dosing AOM exudates from cells that were well separated using PosiDAF to algal and cyanobacterial suspensions that were not well separated, enhanced cell separation. Further analysis conducted in Chapter 4 demonstrated that the carbohydrates, particularly acidic carbohydrates present in the AOM, may conjugate with the proteins to form, on interaction with cells and synthetic polyelectrolytes, large web-like structures that enhance flotation (Chapter 4, Section 4.4). Therefore, it was proposed that a complete carbohydrate and protein analysis would provide critical information required to determine the specific macromolecules that enhance flotation.
Traditionally, carbohydrates and proteins in algal biomass samples are quantified using phenol–sulfuric acid and Lowry assay protocols (Frølund et al. 1995, Zhang et al.
1999b). However, these methods measure total proteins and carbohydrates and do not give insight into the type and concentration of the individual components. For instance,
Chapter 5 Protein-carbohydrate chemistry
carbohydrates that are typically bound to the protein through covalent bonds with asparagine, threonine, or serine, are not detected through conventional total protein and total carbohydrate assays (Hall 2007). Therefore, advanced characterisation techniques such as chromatography and spectrometry (Chapter 2B) must be applied to identify the type and concentrations of individual proteins and carbohydrates.
Hence, in this Chapter, the aforementioned methods were applied to characterise the specific proteins and carbohydrates present in the algal and cyanobacterial species tested in Chapter 4. The rationale behind this is to understand which specific carbohydrates and proteins must be present to enhance flotation and thus examine the potential to develop a more robust PosiDAF process in which the desired cell separation can be obtained independent of species. Overall, the aims of this chapter were:
1) to characterise the proteins and carbohydrates in the AOM from three species namely
C. vulgaris CS-42/7, and two strains of M. aeruginosa CS-555/1 and CS-564/01, based on their nature, concentration and size.
2) to apply the information from the in depth biomolecular characterisation to optimise the PosiDAF process by conducting jar tests with the addition of select proteins and carbohydrates that are known to be present in the species that give high separation on
PosiDAF.
5.2 Materials & methods
5.2.1 Chemicals
Unless otherwise stated, all chemicals were obtained from Sigma-Aldrich and used without any further purification. Bovine serum albumin (BSA) (97%), concanavalin A
(Con A) (97%), D-(+)-glucose, D-(+)-galactose, D-(+)-fucose, D-glucuronic acid ~ 115 ~
Chapter 5 Protein-carbohydrate chemistry
(97%), D-galacturonic acid (97%), D-glucosamine, human serum albumin (HSA)
(99%), Bradford’s reagent, TrisHCl, 3-[(3-cholamidopropyl)dimethylammonium]-1- propanesulfonate (CHAPS) hydrate (98%), urea (98%), thio-urea ACS reagent (99%), sodium dodecyl sulfate (SDS), 1,4–dithiothreitol (DTT) (97%), sodium deoxycholate
(97%), trifluoroacetic acid (TFA) (99%), iodoacetamide (97%), acetone (98%), sodium bicarbonate (99.5%) and sodium chloride ACS reagent (99%) were used as received.
Low Mw PDADMAC was used as the bubble modification chemical in PosiDAF jar testing.
5.2.2 Algae and cyanobacteria
Chlorella vulgaris CS-42/7 and two strains of Microcystis aeruginosa, CS-555/1 and
CS-564/01, known hereafter as CV, MA555 and MA564, respectively, were obtained from the Commonwealth Scientific and Industrial Research Organisation’s (CSIRO)
Australian National Algae Culture Collection (ANACC) (Hobart, Australia) and re- cultured, monitored and harvested as per the procedures outlined in Chapter 3, Section
3.2.2.
5.2.3 Algal organic matter
The algal organic matter (AOM) was separated from the cells to produce a single AOM fraction (AOM1.50) based on the methods described in Chapter 4, Section 4.3.2.1.
Briefly, the cells were separated from AOM by centrifugation at 10,000 g for 15 minutes with an Allegra® X-15R Centrifuge (Beckman Coulter, Australia) (Henderson et al. 2008c) and then filtered using 934-AH glass microfiber filters (1.50 µm nominal pore size, Whatman, USA) to remove cell debris to give AOM1.50. The resulting
AOM1.50 fractions from each species were then lyophilised prior to any further analysis.
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5.2.3.1 Extraction of algal proteins
The proteins from CV, MA564, and MA555 were extracted by first dissolving the respective lyophilised AOM samples in 7 M urea, 3M thiourea and 3% CHAPS buffer and left overnight to precipitate with ice cold acetone. The precipitated proteins were then removed via centrifugation at 12,000 g for 15 minutes at 4˚C with an Allegra® X-
15R Centrifuge (Beckman Coulter, Australia), air dried and re-suspended in 50 µL of 50 mM TrisHCl buffer maintained at pH 8.
The protein purity analysis was carried out through Bradford assay. Briefly, aliquots of the protein samples dissolved in the buffer were added to the tray with Bradford’s reagent. The purity results were obtained by comparing against a standard curve of BSA after analysing in a spectrophotometer Model 918 (GBC, Australia). The extracted proteins were then resuspended in Milli-Q water and lyophilised prior to any further use or analyses. All assays were conducted in triplicate.
5.2.4 Algal System Characterisation
5.2.4.1 Cell Properties and AOM analysis
Cell counting, cell size, charge measurements, and AOM analyses were undertaken exactly as described in Chapter 4, Section 4.3.2.3. Briefly, cell counting was done via light microscopy (Leica DM750 Microscope, Switzerland) and a haemocytometer, cell size measurements via Mastersizer 2000 (Malvern, Australia), and charge measurements via particle charge detector (PCD-04 Travel; Mütek BTG, Eclépens,
Switzerland) and Zetasizer Nano ZS (Malvern, Australia). AOM1.50 was first analysed with a TOC–Vsch Analyser (Shimadzu, Australia) to determine its concentration as total organic carbon (TOC). Protein and carbohydrate assays were conducted using the
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modified Lowry method (Frølund et al. 1995) and phenol-sulphuric acid method (Zhang et al. 1999b), respectively. Each measurement was conducted in triplicate.
5.2.4.2 Protein Characterisation
The protein profiling was carried out using the sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) method. The resolving and the stacking gels were initially prepared by mixing different proportions of chemicals such as SDS, acrylamide and 0.5M Tris-HCl buffer and then polymerised by slowly injecting the gel solution into short and spacer glass plates and allowing them to set for 45 – 120 min. The polymerised gel was later assembled and cast into the electrophoresis tank. The samples were then heated to 95 ˚C, vortexed, cooled and loaded. Once the electrophoresis was finished, the gels were placed in a container and stained for 2 h. Later, the gel was destained and scanned for the image. All assays were done in triplicate.
For further analysis through mass spectrometry (MS), two sets of samples: (a) the lyophilised AOM samples, and (b) samples where the proteins were extracted based on the procedure outlined in Section 5.2.3.1, were weighed out and dissolved in a buffer containing 2% sodium deoxycholate and 50 mM Tris-HCl pH 8.5. Vortexing and sonication were used to assist the sample solubilisation. After centrifugation, 50 µL of solution was reduced with 5 mM DTT for 30 min at 37 ˚C followed by alkylation with
10 mM iodoacetamide. Trypsin was then added and the solution was incubated overnight at 37 °C. The digested peptides were desalted via a C18 stage tip before mass spectrometry examination.
The digested peptides were then separated by nano-LC using an Ultimate 3000 HPLC and autosampler system (Dionex, Amsterdam, Netherlands). Samples were concentrated and desalted onto a micro C18 pre-column (300 µm x 5 mm, Dionex) with H2O:CH3CN ~ 118 ~
Chapter 5 Protein-carbohydrate chemistry
(98:2, 0.05 % TFA) at 15 µL/min. After a 4 min wash, the pre-column was switched
(Valco 10 port valve, Dionex) into line with a fritless nano column (75µ x ~10cm) containing C18 media (1.9 µ, 120Å, Dr Maisch, Ammerbuch-Entringen Germany) manufactured according to Gatlin. The peptides were then eluted using a linear gradient of H2O:CH3CN (98:2, 0.1 % formic acid) to H2O:CH3CN (64:36, 0.1 % formic acid) at
200 nL/min over 30 min. High voltage (2000 V) was applied to low volume tee
(Upchurch Scientific) and the column tip positioned ~ 0.5 cm from the heated capillary
(T=275 °C) of an Orbitrap Velos (Thermo Electron, Bremen, Germany) mass spectrometer. Positive ions were generated by electrospray and the Orbitrap operated in data dependent acquisition mode (DDA).
A survey scan in the range m/z 350-1750 was acquired in the Orbitrap (Resolution =
30,000 at m/z 400, with an accumulation target value of 1,000,000 ions) with lockmass enabled. Up to the 10 most abundant ions (>4,000 counts) with charge states > +2 were sequentially isolated and fragmented within the linear ion trap using collision induced dissociation with an activation q = 0.25 and activation time of 30 ms at a target value of
30,000 ions. M/z ratios selected for MS/ MS were dynamically excluded for 30 seconds. All MS/MS spectra were searched against NCBI database using MASCOT
(version 2.3) with the following search criteria: enzyme specificity was trypsin; precursor and product ion tolerances were at 4 ppm and ± 0.4 Da, respectively; variable modification of methionine oxidation; and one missed cleavage was allowed. The ions score significance threshold was set to 0.5 and each protein was provided with a probability based Mowse (Molecular Weight Search) score (Pappin et al. 1993). All protein characterisation experiments were conducted in triplicate.
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Chapter 5 Protein-carbohydrate chemistry
5.2.4.3 Carbohydrate characterisation
A complete monosaccharide analysis was performed at Macquarie University through ion-exchange chromatography because presently, analysis of polysaccharide carbohydrates in AOM is not very efficient or accurate when compared to the analysis of monosaccharides which has a very high accuracy. 2.0-2.5 mg of the respective lyophilised AOM sample was weighed in triplicate and then subjected to strong acidic conditions to promote acid hydrolysis. For neutral and acidic monosaccharide analysis, the sample was hydrolysed in 2 M TFA at 100 °C for 4 h, while for amino monosaccharide analysis, the sample was hydrolysed in 4 M HCl at 100 °C for 6 h. At the end of the reaction, the supernatant was taken from the sample and was lyophilised.
The residue was then reconstituted using an internal standard (for neutral and amino monosaccharide analysis: 100 µM 2-deoxy-D-glucose solution; for acidic monosaccharide analysis: 100 µM ketodeoxynonulosonic acid).
The carbohydrate analysis was carried out on a high performance anion-exchange chromatograph system with pulsed amperometric detection (HPAEC-PAD) fitted with a
BioLC amino trap guard column (4 x 50 mm) connected to a CarboPac PA10 column (4 x 250 mm) held at 25 °C. The sample was injected into the HPAEC-PAD and analysed using basic solvents, at a flow rate of 0.5 mL/min. The analytes detected were quantified with internal calibration. The sample was analysed in triplicate and results are expressed as an average. Dilution factors have been included in calculating the results. Results are expressed as microgram of monosaccharide per gram of sample.
The internal standards for the monosaccharides are as follows: neutral - fucose, arabinose, rhamnose, galactose, glucose, xylose, and mannose; amino - galactosamine and glucosamine; acidic - galacturonic acid and glucuronic acid.
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Chapter 5 Protein-carbohydrate chemistry
5.2.5 Determining PosiDAF jar test procedures
Using the procedures employed for standard PosiDAF jar testing (Chapter 3, Section
3.2.3, Figure 1-1(B)) and a fixed PDADMAC dose of 0.004 meq/L for CV and 0.001 meq/L for MA564 and MA555 (dose at which maximum cell removal was achieved in
Section 3.3.4), synthetic algal solutions were prepared as influent feed water to explore the impact of carbohydrates and proteins on PosiDAF cell separation effectiveness. All assays in this section were conducted in triplicate.
5.2.5.1 Determination of the most effective biopolymers and their dose
A CV suspension (including cells and AOM), at a cell concentration of 8 x 105 cells/ mL, was prepared as the PosiDAF influent. To determine the most effective proteins and the dose of these to be applied, a set of 1) external proteins: BSA, Con A, HSA, and
2) algal derived proteins: CV protein, MA555 protein, and MA564 protein, were trialled by adding these at varying concentrations, between 5–25 nM, into the jar. The optimum carbohydrate dose and most effective carbohydrates were determined by varying the doses of glucose, galactose, glucosamine, glucuronic acid and galacturonic acid, between 0–500 nM, to the jar. The cell separation efficiency was used to assess the best performing proteins and carbohydrates, and their respective concentrations. The proteins and carbohydrates were dosed into the jar along with the algae and stirred at 50 rpm for 1 min.
5.2.5.2 Investigation of the influence of AOM on biopolymer dosing
Based on the results from Section 5.2.5.1, the best performing carbohydrates and external protein, and associated doses, were selected to examine the influence of the presence of native AOM on the biopolymer dosing. The synthetic DAF influents used
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Chapter 5 Protein-carbohydrate chemistry
for this experiment were made of CV at a cell concentration of 8 x 105 cells/ mL in two ways: 1) cells with AOM; and, 2) cells without AOM (refer to Section 4.2.3). The cell separation efficiency obtained for both sets of tests determined whether subsequent experiments were conducted by having the native AOM in the jar or post-AOM extraction.
5.2.5.3 Determination of optimum biopolymer dose location
The PosiDAF influent in this section was prepared based on the results of Section
5.2.5.2, at a cell concentration of 8 x 105 cells/ mL. The ideal location for dosing the protein and carbohydrate into the system was identified by conducting four tests with
BSA and glucose as follows:
Test 1 – BSA and glucose dosed into the jar and stirred at 50 rpm for 1 min.
Test 2 – BSA and glucose dosed into the saturator and jar, respectively, and stirred at 50 rpm for 1 min.
Test 3 – BSA and glucose dosed into the jar and saturator, respectively, and stirred at 50 rpm for 1 min.
Test 4 – BSA and glucose dosed into the saturator and stirred at 50 rpm for 1 min.
The location where the maximum cell separation was obtained in these aforementioned jar tests was used in the subsequent experiments.
5.2.5.4 Investigation of the influence of carbohydrates and proteins derived from algae
CV, MA555, and MA564 were the species tested in this set of PosiDAF experiments.
Sections 5.2.5.2 and 5.2.5.3 provided the dose location and influent conditions,
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Chapter 5 Protein-carbohydrate chemistry
respectively; the best performing carbohydrates and algal derived protein were selected based on the experiments conducted in Section 5.2.5.1. Additionally, a neutral carbohydrate (selected based on carbohydrate characterisation of the algae) was also trialled in 1:7 ratio with the other carbohydrates as both acidic and neutral carbohydrates were shown to influence PosiDAF cell removal (Chapter 4).
5.3 Results
5.3.1 Cell System Characterisation
The CV, MA564 and MA555 cells had similar spherical, unicellular morphology when grown in the laboratory. The physio-chemical properties of the algae and cyanobacteria were similar to that observed in Chapter 4, Section 4.3.1.
Table 5- 1. Physiochemical properties of cells and AOM from CV, MA555 and MA564.
Properties Units CV MA555 MA564
Size µm 5.1 ± 0.55 2.9 ± 0.4 3.15 ± 0.45 TOC x10-10 mg/cell 8.6 ± 0.5 9.6 ± 0.4 10.2 ± 0.9 DOC x10-10 mg/cell 9.1 ± 0.1 9.6 ± 0.1 8.5 ± 0.5 Carbohydrate mg/L 3.9 ± 0.7 2.8 ± 0.4 7.9 ± 0.2 Protein mg/L 1.3 ± 0.3 0.4 ± 0.2 2.2 ± 0.4
5.3.2 Carbohydrate Characterisation
The carbohydrate characterisation constituted a monosaccharide analysis as polysaccharides are essentially monosaccharides that are linked to each other through glycosidic bonds. Through the analysis, seven different neutral carbohydrates were identified in the AOM from CV, MA555 and MA564, although the associated individual concentrations varied greatly (Figure 5-1). For example, it was found that the
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Chapter 5 Protein-carbohydrate chemistry
concentrations of glucose in MA555 were nearly 10 and 60 times higher than that found in MA564 and CV, respectively (Figure 5-1). However, the concentration of arabinose in CV was found to be 25 times greater than the amount found in both the Microcystis aeruginosa strains (Figure 5-1). Of the seven neutral carbohydrates that were detected, six were common to all three strains with fucose being the only neutral carbohydrate that was not observed in CV (Figure 5-1). Fucose has been previously detected in the
US strain of Chlorella vulgaris through ion-exchange chromatography (Templeton et al.
2012b), indicating that carbohydrate production in algae can be dependent on the region from where they are isolated.
In MA555 and MA564, two acidic carbohydrates, namely glucuronic acid and galacturonic acid were observed (Figure 5-1). The presence of acidic carbohydrates in
MA555 and MA564 is in agreement with the results obtained via transparent exopolymer particle analysis (TEP) analysis (Chapter 4, Section 4.3.3.1), and through prior research on different strains of M. aeruginosa (Nakagawa et al. 1987, Strycek et al. 1992, Villacorte et al. 2015b). In contrast, no acidic carbohydrates were detected in
CV in this study (Figure 5-1), although acidic carbohydrates were previously detected in
Chlorella vulgaris through ion-exchange chromatography (Discart et al. 2013) and
Alcian Blue staining (Chapter 4, Section 4.3.3.1). The concentrations of neutral and acidic carbohydrates varied greatly between the two Microcystis aeruginosa strains tested. For instance, it can be seen from Figure 5-1 that the acidic carbohydrates are at least twice as concentrated in MA564 when compared to MA555. However, MA555 had a higher concentration of all the neutral carbohydrates in comparison to MA564
(Figure 5-1). The high concentrations of the charged uronic acids observed in MA564
(Figure 5-1) could explain why MA564 had the most negative charge density in comparison to the other species (Chapter 4, Table 4-1). ~ 124 ~
Chapter 5 Protein-carbohydrate chemistry
Glucosamine was detected in CV but was absent in both strains of M. aeruginosa tested
(Figure 5-1). Amino carbohydrates such as glucosamine have been previously identified in C. vulgaris (Templeton et al. 2012b) as well as in cell walls of M. aeruginosa (Martin et al. 1989); however, whether glucosamine was present in the organic matter secreted by M. aeruginosa was unknown. Also, from this study, it was seen that concentrations of glucosamine in CV was much less than that found in the US strain of C. vulgaris
UTEX-395 (Templeton et al. 2012b) at 0.5 µg/mg of lyophilised AOM in comparison to 5.2 µg/mg of lyophilised AOM, respectively.
100 ) CV MA555 MA564
μg/mg 10
1
0.1 Log Concentration ( Log Concentration 0.01
Figure 5- 1. List and concentrations of the carbohydrates detected in CV, MA555 and
MA564.
5.3.3 Protein Characterisation
5.3.3.1 Protein profiling through SDS-PAGE
The protein profiles obtained via SDS-PAGE differed greatly between the non- algal/cyanobacterial and algal/cyanobacterial sources (Figure 5-2). For example, it can be seen that the commercially available proteins had distinct concentrated bands
(indicative of increased concentration) in the range of 50-70 kDa for BSA and HSA, ~ 125 ~
Chapter 5 Protein-carbohydrate chemistry
and 8-23 kDa for Con A. However, the protein profiles for algal proteins presented concentrated clusters of bands at much lower molecular weights predominantly around
5-25 kDa (Figure 5-2). Furthermore, the algal proteins also had more protein bands covering the entire size range of 5-250 kDa in comparison to the non-algal proteins which had more narrow size ranges.
The protein profiles generated in this study varied significantly between the algal species (Figure 5-2). For instance, it was seen that the molecular weights of the most intense bands were around 5-25 kDa for MA555, 8-15 kDa and 20-25 kDa for MA564, and around 10-15 kDa for CV. This was further supported by the good correlation observed when the intense algal and cyanobacterial protein bands from Figure 5-2 were compared with the protein concentrations through exponentially modified protein abundance index (emPAI) values for all the species (Table 5-2). An emPAI value is a relative quantification of the specific protein in a mixture of proteins and is calculated based on the peptide matches in a database search result (Ishihama et al. 2005). In this study, the emPAI values of MA555 for molecular weights under 50 kDa were the highest among all the species tested and this corresponded well with the intense clustering of protein bands observed below 50 kDa for the same strain (Figure 5-2).
MA564 and CV had the greatest (> 80%) and lowest (< 45%) number of proteins below
50 kDa, respectively (Table 5-2). This is in contrast to prior research studies which showed that M. aeruginosa predominantly comprised proteins that had molecular weights greater than 100 kDa (Pivokonsky et al. 2006, Henderson et al. 2008c,
Pivokonsky et al. 2014). Such differences might be caused due to the variation in strains of M. aeruginosa, which are likely to have differing compositions as highlighted in this study via the contrast of MA555 and MA564. This demonstrates the importance of identifying species up to the strain level. There were also similarities between all the ~ 126 ~
Chapter 5 Protein-carbohydrate chemistry
algal strains in that many protein bands above 75 kDa range were visible, as well as some apparent clusters in the range of 50 kDa (Figure 5-3). Similar observations of bands in M. aeruginosa proteins (US and Chinese strains) at positions of approximately
15-17 kDa, 50 kDa, 150 kDa and greater than 250 kDa have been previously reported
(Tonietto et al. 2012, Yamada et al. 2012, Wei et al. 2016). As proteins comprise a significant proportion of the AOM, it is suggested that the size of AOM molecules could impact process performance in PosiDAF as previously proposed in Chapter 4 and by Henderson et al. (2010a).
Figure 5- 2. Protein profiling of BSA, HSA, Con A, CIP, 564IP, 555IP conducted through
SDS-PAGE. Dark bands represent the separated proteins. Biorad unstained marker column represented as the standard. Well 1 – BSA, Well 2 – HSA, Well 3 – ConA, Well 4 –
CIP, Well 5 – 564IP, Well 6 – 555IP. ~ 127 ~
Table 5- 2. Classification of proteins (before and after extraction) from CV, MA555 and MA564 based on their charge, molecular weight and emPAI. Chapter 5 Protein 5 Species Charge Property < 10 kDa 10 - 25 kDa 25 - 50 kDa 50 - 75 kDa 75 - 100 kDa >100 kDa (meq/g)
CV -0.8 ± 0.6 No of proteins 3 ± 2 4 ± 1 14 ± 2 15 ± 1 3 ± 1 5 ± 2 % proteins 5.2 ± 1 10.4 ± 3 31.5 ± 2 31.5 ± 2 5.2 ± 1 16.2 ± 0.5 Before
extraction emPAI 0.46 ± 0.01 0.29 ± 0.04 0.121 ± 0.026 0.084 ± 0.023 0.05 ± 0.01 0.05 ± 0.001
-0.6 ± 0.04 No of proteins 5 ± 2 4 ± 2 4 ± 1 2 ± 1 1 ± 1 1 ± 1 % proteins 29.6 ± 10 23.1 ± 10 23.1 ± 5 10.5 ± 5 5 ± 5 5 ± 5
After emPAI 0.96 ± 0.15 0.55 ± 0.3 0.15 ± 0.13 0.94 ± 0.011 0.02 ± 0.001 0.01 ± 0.001 extraction
-1.4 ± 0.8 No of proteins 10 ± 1 65 ± 2 58 ± 3 35 ± 2 10 ± 2 16 ± 4 ~ 128 MA555 % proteins 6.1 ± 1 34.4 ± 2 30.5 ± 2 19.8 ± 1 5 ± 0.1 8.8 ± 1 Before
extraction emPAI 1.01 ± 0.22 2.52 ± 0.71 0.76 ± 0.23 0.68 ± 0.36 0.12 ± 0.04 0.19 ± 0.1
-0.9 ± 0.01 No of proteins 4 ± 4 12 ± 10 14 ± 11 8 ± 7 1 ± 1 4 ± 4 % proteins 9.1 ± 9.1 28.3 ± 24 31.1 ± 23 17.8 ± 14.9 2.1 ± 2.1 9.1 ± 9.1 After -
emPAI 0.85 ± 0.12 1.4 ± 0.19 1.05 ± 0.14 0.65 ± 0.05 0.37 ± 0.06 0.11 ± 0.1 chemistry carbohydrate extraction
MA564 -1.01 ± 0.6 No of proteins 21 ± 1 70 ± 5 49 ± 4 27 ± 2 5 ± 1 2 ± 1 % proteins 10.9 ± 2 39.7 ± 2 29.1 ± 2 15 ± 2 2 ± 0.3 3.1 ± 0.01 Before
extraction emPAI 0.95 ± 0.23 0.99 ± 0.36 0.27 ± 0.08 0.42 ± 0.09 0.09 ± 0.01 0.085 ± 0.005
-0.6 ± 0.02 No of proteins 16 ± 10 37 ± 15 22 ± 21 22 ± 11 3 ± 3 3 ± 3 % proteins 15.9 ± 11.2 33.6 ± 13 20.1 ± 20 23.1 ± 12 1.8 ± 0.3 1.8 ± 0.01 After
extraction emPAI 0.72 ± 0.23 0.48 ± 0.36 0.31 ± 0.08 0.63 ± 0.09 0.15 ± 0.01 0.093 ± 0.005
Chapter 5 Protein-carbohydrate chemistry
5.3.3.2 Determination of functional characteristics of proteins
When the AOM from all three strains was analysed through mass spectrometry, over
260 distinct algal proteins were identified and these comprised enzymatic and non- enzymatic proteins in almost equal proportions (Appendix Tables A5-1 to A5-6). Of these, no protein was found to be commonly expressed between all three species although some proteins were found to be common to two species of algae (Figure 5-3).
For instance, there were 77 proteins that were commonly expressed between MA555 and MA564, and one protein that was commonly expressed between MA564 and CV.
The total number of proteins identified for each strain varied, with the most found in
MA555 (180 proteins) and the least in CV which had 37 proteins (Figure 5-3). This reiterates that such differences are due to the variations in strains of algae and cyanobacteria. As an illustration, several proteins which were not previously detected in
US strains of M. aeruginosa were found in Chinese and the PCC strains (Mayumi et al.
2006, Alexova et al. 2011, Qi et al. 2015).
Nine different functional categories were identified from the list of proteins for all strains, with the highest percentage of proteins involved in photosynthesis and respiration, and lowest fractions being the uncharacterised and hypothetical proteins
(Figure 5-3). Of interest from the functional distribution of proteins in Figure 5-3 is the set of proteins involved in the biosynthesis process; some of these are involved in the O- linked and N-linked glycosylation, and some take part in the synthesis of lectins.
Glycosylation is the covalent addition of a glycan (carbohydrate) residue to the amide of an asparagine inside the endoplasmic reticulum - termed an N-linkage (Aebi 2013), or the oxygen of a serine or a threonine protein in the Golgi apparatus - termed an O- linkage (Lodish et al. 1995, Berg et al. 2002). Both of these linkages are catalysed by
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Chapter 5 Protein-carbohydrate chemistry
enzymes to form non-reversible protein-carbohydrate globules known as glycoproteins
(Berg et al. 2002), whereas lectins are those proteins which can reversibly bind to specific or multiple carbohydrates (conjugation) through hydrogen bonding (Berg et al.
1999), and non-polar interactions (Weis et al. 1996) even in the absence of enzymes. By identifying these proteins, potential sites of the protein-carbohydrate conjugation in the respective AOM could be detected which could further help understand the variability in the PosiDAF cell separation efficiency across species.
Figure 5- 3. Venn diagram indicating the functional distribution and the number of similar and distinct proteins present in CV, MA555 and MA564. Coloured zones indicate the respective proportions of the functional proteins. White spaces represent no proteins present. For full protein dataset, please see Appendix Table A5-1 to A5-6.
5.3.3.2.1 Detection of carbohydrate binding proteins and their potential impact on
PosiDAF efficiency
From Appendix Tables 5-1 to 5-6, no proteins that are known to be involved with N- linked glycosylation were identified in any of the three strains. However, 10 serine and ~ 130 ~
Chapter 5 Protein-carbohydrate chemistry
threonine residues that are known to aid O-linked glycosylation (Berg et al. 2002) were found in MA555 (Appendix Table 5-1) and MA564 (Appendix Table 5-3), but not in
CV (Appendix Table 5-2). It is proposed that these may form a part of the ‘microcystin- related protein C’ (MrpC) which was found to be glycosylated through O-linkages in
M. aeruginosa PCC 7806 (Zilliges et al. 2008, Zilliges et al. 2011). Furthermore, the same studies showed that MrpC covered the whole cell surface of M. aeruginosa cells and also formed connections between individual cells. Therefore, it is suggested that the serine and threonine proteins could form similar linkages between the cells and potentially aid cell removal.
No specific lectins, such as MAL or microvirin that have been previously observed in algae and cyanobacteria (Sakamoto et al. 1996, Yamaguchi et al. 1998, Kehr et al.
2006, Kehr et al. 2015), were found via mass spectrometry in the three strains used in this study (Appendix Table 5-1, 5-3, 5-5). However, the enzyme methionine sulfoxide reductase was found to be present in the M. aeruginosa strains (Appendix Table 5-1, 5-
3). It is suggested that this enzyme could potentially reduce the di-sulfide bonds in the methionine group in microvirin because the excision of the methionine group is a common post-translational modification that occurs in bacterial lectins (Kort et al. 1975,
Giglione et al. 2001). Given the extreme difficulty in isolating and identifying lectins in algae and cyanobacteria (Kehr et al. 2015), it can only be hypothesised that microvirin was not detected due to the loss of this methionine group from its structure. However, the existence of other types of lectins in the species cannot be completely ruled out.
This is because one key characteristic of lectins is their ability to bind to sugar residues on cell surface which leads to linking of cells (agglutination) (Singh et al. 2017); previously, cyanobacterial species such as M. aeruginosa have been observed to agglutinate (Carmichael et al. 1981, Yamaguchi et al. 1998, Yamaguchi et al. 2000). ~ 131 ~
Chapter 5 Protein-carbohydrate chemistry
Hence, owing to the limited research on lectins in cyanobacteria (Kehr et al. 2015), the presence and concentrations of other lectins is unknown.
Presently, it is understood that the disparities in the availability of proteins reflects the natural variability between the species. From an algal harvesting perspective, the absence of detectable lectins or proteins that assist in N- or O- linkages in the algae under consideration may impact PosiDAF efficiency by inhibiting the conjugation of proteins and carbohydrates.
5.3.3.3 Protein extraction: implications for PosiDAF experiments
When the proteins were extracted from the AOM and then analysed through mass spectrometry, the number of proteins detected were much lower than what was found prior to extraction. For instance, the numbers of proteins identified in MA564 before and after protein extraction were 180 and 75-130, respectively (Table 5-2). These numbers were found to be even lower for MA555 and CV. Especially CV, which had nearly 55% proteins greater than 50 kDa prior to the extraction, lost more than half the number of proteins after extraction (Table 5-2). However, the percentage of proteins in particular Mw ranges before and after extraction for both the M. aeruginosa strains remained largely unaffected. For example, MA564 before and after protein extraction still had a comparable percentage of proteins below 50 kDa (Table 5-2).
The presence of serine and threonine based proteins, and possibly microvirin and other lectins in MA555 and MA564 was also affected by the protein extraction process. Out of the seven serine and three threonine proteins detected prior to extraction, only two to four serine and one or two threonine proteins were detected after extraction (Appendix
Table A5-1, A5-3, A5-7, and A5-9). The enzyme methionine sulfoxide reductase was also not detected; however, it is not conclusively known whether microvirin exists in ~ 132 ~
Chapter 5 Protein-carbohydrate chemistry
the AOM and hence, the impact of extraction on microvirin or any other lectin cannot be verified. Over several extraction experiments, it was observed that the detection of a number of these aforestated proteins fluctuated and was largely inconsistent. From the context of PosiDAF efficiency, it is expected that the loss of serine and threonine residues, and potentially lectins could cause problems during carbohydrate conjugation, and consequently, the cell removal. The loss of proteins during the extraction procedure could either be due to the assay’s sensitivity to the ionic strength of the solvents resulting in sequestration of proteins (Crowell et al. 2013), or due to the assay’s sensitivity to low concentrations of protein (Walsh 2002).
5.3.4 Optimisation of the PosiDAF process through the addition of biopolymers
5.3.4.1 Determination of the most effective type and dose of biopolymers to enhance separation in PosiDAF
When applying PosiDAF using PDADMAC and the proteins (both non- algal/cyanobacterial and algal/cyanobacterial derived) individually in the jar containing
CV cells with AOM, the cell separation gradually increased with the protein dose and reached maximum values at a protein concentration of 10 nM. Beyond that point, the cell separation consistently decreased (Figure 5-4). In all cases, the addition of the proteins lowered the cell separation below that experienced without additional protein addition (Chapter 3, Section 3.3.4); the only exception was after the addition of the extracted MA564 protein which provided the optimal performance, increasing cell removal from 61% to 69%. Among the non-algal proteins trialled, the cell separation obtained while dosing BSA was close to what was achieved without dosing any protein
(Figure 5-4, Chapter 3 Section 3.3.4), possibly because BSA has been reported to be a suitable model for the algal proteins (Pivokonsky et al. 2015). The reduction in cell
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Chapter 5 Protein-carbohydrate chemistry
removal after the addition of proteins can be attributed to the loss of polymer efficiency, possibly due to: (a) the strong electrostatic bonding between the protein and
PDADMAC similar to that observed for coagulant and proteins (Pivokonsky et al.
2006, Takaara et al. 2007), or (b) the protein’s conjugation with the free carbohydrates as well as the carbohydrates attached to the glycoproteins present in the AOM resulting in a limited interaction with the cells.
BSA Con A HSA
70 CV protein MA564 protein MA555 protein
50
30 Cell Separation %
10 0 5 10 15 20 25 Protein concentration (nM)
Figure 5- 4. PosiDAF dose response curve while separating CV using BSA, ConA, HSA,
CV protein, MA555 protein, and MA564 protein. The dashed line indicates the maximum removal obtained for CV in the absence of protein addition (from Chapter 3).
On addition of carbohydrates to the jar containing CV cells with AOM, the cell separations achieved were below what was experienced without additional carbohydrate addition (Chapter 3, Section 3.3.4). In all cases, the cell separations were less than 35%
(Figure 5-5); however, the trends obtained were very similar to that of Figure 5-4.
Specifically, the cell separations increased from less than 5% up to 35% as the concentration of carbohydrates increased to 200 nM, but decreased from then onwards
(Figure 5-5). The uronic acids, and in particular galacturonic acid, performed better with ~ 134 ~
Chapter 5 Protein-carbohydrate chemistry
35% cell separation in comparison to the other carbohydrates. The reduction in cell removal on the addition of carbohydrates could perhaps be due to the conjugation of the dosed carbohydrates with the proteins present in the AOM, which restricts the attachment of proteins in the AOM to the cells. This implies that lectins were indeed present in the AOM and more importantly, the carbohydrates that strongly conjugated with the lectins in AOM caused low cell separations and vice versa. A corollary to this hypothesis is that the addition of proteins and carbohydrates in the presence of native
AOM would result in low cell removal in most cases as observed in Figures 5-4 and 5-
5. This is investigated further in the following section by dosing biopolymers in the presence and absence of native AOM.
Glucose Galactose Glucosamine
Glucuronic acid Galacturonic acid 75
60
45
30
15 Cell Separation % 0 0 100 200 300 400 500 Carbohydrate concentration (nM)
Figure 5- 5. PosiDAF dose response curve while separating CV using glucose, galactose, glucosamine, glucuronic acid and galacturonic acid at different carbohydrate concentrations. Dashed lines indicate the maximum removal obtained for CV in the absence of carbohydrate addition (Chapter 3).
Based on the results from Figures 5-4, and 5-5, the following were established: the most effective algal and non-algal proteins in terms of PosiDAF cell removal were MA564 protein and BSA, respectively. The carbohydrates that have the potential to improve cell
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Chapter 5 Protein-carbohydrate chemistry
separation were uronic acids and glucosamine. The concentrations of proteins and carbohydrates for subsequent experiments were fixed at 10 nM and 200 nM, respectively.
5.3.4.2 Impact of native AOM on biopolymer addition
When trialling the biopolymers on CV both with and without AOM, it was observed that when the AOM was removed the impact of biopolymer addition was enhanced when compared to its impact prior to AOM removal (Figure 5-6). As an example, the cell removals obtained for BSA-galacturonic acid before and after AOM extraction were 49% and 62%, respectively (Figure 5-6). Furthermore, the maximum cell removal achieved post AOM extraction for BSA-galacturonic acid combination was comparable to the cell removal achieved for CV in Chapter 3, Section 3.3.4.1 which was 61%. It is therefore evident that the BSA-galacturonic acid combination interacted with the CV cells in a manner that was similar to that of the native CV AOM (Chapter 3). Based on these results, further experiments on PosiDAF optimisation were conducted on cells post-AOM extraction only.
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Chapter 5 Protein-carbohydrate chemistry
Figure 5- 6. Cell separation obtained in PosiDAF while separating CV with and without
AOM using PDADMAC and BSA dosed with glucuronic acid, galacturonic acid and glucosamine.
5.3.4.3 Determination of ideal location for biopolymer dosing in PosiDAF
When PosiDAF experiments were conducted on CV cells without AOM to determine the ideal location for dosing the biopolymers, the cell separation reduced to almost half of what was achieved prior to the addition of these biopolymers (Figure 5-7). The reduction in cell removal when dosing BSA and glucose is explained by the ability of glucose to couple multiple carbohydrate residues to BSA (Ledesma-Osuna et al. 2008) resulting in poor interactions of the biopolymers with the algal cells. While the cell separation reduced post the addition of biopolymers, it was observed that when the biopolymers were dosed together or separately into the saturator and interacted first with the polymer, the cell removal obtained was less than 5% (Figure 5-7). However, when the biopolymers were dosed together into the jar and interacted first with the cells, the cell removal increased to 35% (Figure 5-7). This is attributed to the strong
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Chapter 5 Protein-carbohydrate chemistry
electrostatic interactions between PDADMAC and the proteins or carbohydrates which have previously been demonstrated to diminish PDADMAC’s surface properties (Wen et al. 1997, Mattison et al. 1998, Chen et al. 2011). Hence, subsequent experiments were conducted by dosing the biopolymers into the jar.
100
80 60 40
Cell Separation % 20 0 BSA and glucose in jar BSA in saturator and BSA in jar and glucose BSA and glucose in glucose in jar in saturator saturator
Figure 5- 7. Cell removal when CV cells without AOM were supplemented with BSA and glucose in four different ways to identify the best location to dose these biopolymers.
Dashed lines indicate the maximum removal obtained for CV in the absence of biopolymer addition (Chapter 3).
5.3.4.4 PosiDAF optimisation through the addition of carbohydrates and algal- derived proteins
When PosiDAF experiments were conducted for all strains in the absence of AOM, the cells systems were supplemented with: MA564 proteins, as they were the most effective algal protein in terms of PosiDAF cell removal (Figure 5-4); uronic acids; and, glucosamine, as these had the potential to improve cell separation (Section 5.3.4.1,
Figure 5-5). In addition, fucose was dosed in a 1:7 ratio with the other carbohydrates because: (a) lectins identified in cyanobacteria and particularly, M. aeruginosa have been typically mannose specific and not fucose specific (Kehr et al. 2015), (b) algal
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Chapter 5 Protein-carbohydrate chemistry
acidic carbohydrates which impacted PosiDAF efficiency have been observed to contain substantial amounts of fucose sugars (Zhou et al. 1998, Villacorte et al. 2015b), and (c) fucose had 1/7th the concentration of uronic acids in MA564 (Figure 5-1), which was the best performing strain based on Chapter 3. Hence, the collective aim of this experiment was to dose proteins extracted from the best performing strain (Chapter 3) along with the carbohydrates that had the greatest impact on PosiDAF efficiency (Chapter 4) and poor cyanobacterial lectin binding affinity (fucose).
When the biopolymers were trialled in the PosiDAF process, it was observed that maximum cell removals of 81% for CV, 83% for MA555 and 90% for MA564 were obtained (Figure 5-8). Furthermore, for CV and MA555, the achieved cell removals when dosing galacturonic acid-fucose, glucuronic acid-fucose, and glucosamine-fucose were comparable to, or higher than, those obtained without supplementing the system with proteins or carbohydrates (Figure 5-8; Chapter 3, Section 3.3.4.1). With the addition of acidic carbohydrates and fucose along with MA564 proteins, the cell removal was expected to be greater than 95% for all strains. However, over several jar test repetitions, large standard deviations of 10-20% were observed bringing down the median cell removal to 80-90% for all the strains. This could be attributed to either the presence of residual AOM in the system or the loss of key carbohydrate binding proteins during protein extraction as noted in Table 5-2.
Two interesting observations were made during these experiments: (a) web-like networks that were smaller and fewer in number than those seen in Chapter 4 appeared during the flotation of all three strains when the biopolymers were dosed, and (b) the cell separations were within a range of 10% irrespective of the species tested or the carbohydrate combinations trialled (Figure 5-8). With only the cells present in the
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Chapter 5 Protein-carbohydrate chemistry
system and no AOM to influence separation, it is suggested that during the PosiDAF process, the dosed biopolymers interact with the glycoproteins and carbohydrates present on the cell surface and form networks eventually resulting in high cell separations.
Figure 5- 8. Cell removal when PosiDAF influent containing cells of CV, MA555, and
MA564 are dosed with MA564 protein, galacturonic acid-fucose, glucuronic acid-fucose, glucosamine-fucose. Dashed lines indicate the maximum removal obtained for CV,
MA555, and MA564 in the absence of biopolymer addition (Chapter 3).
5.4 Discussion
It has been proposed in Chapter 4 that for the success of PosiDAF, the AOM must be strongly associated with the cell surface as well as the polymer-coated bubbles. In this
Chapter, it is conclusively proven that the PosiDAF process efficiency can be manipulated through a combination of factors that occur in two stages: 1) protein- carbohydrate interplay causing biopolymer-cell attachment, and 2) PDADMAC-
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Chapter 5 Protein-carbohydrate chemistry
biopolymers-cells linkages. It is proposed that neither of these aspects is predominant; rather, the synergistic action of these factors contributes to the overall success of
PosiDAF. This is explained as follows:
5.4.1 Biopolymer chemistry dictating the attachment of AOM to the cells
In this Chapter, it is proposed that for effective AOM-cell attachment, the proteins and carbohydrates in the AOM must have a strong association with the proteins and carbohydrates on the cell surface. However, the nature of interactions between the proteins and carbohydrates in the AOM and cell surfaces has not been definitively established in any previous study. Typically, protein-carbohydrate interactions can occur in two ways: (a) glycosylation – which is the irreversible covalent addition of carbohydrate to a protein in the presence of glycosyltransferases (Lodish et al. 1995,
Berg et al. 2002), and (b) conjugation – which is a reversible interaction between the protein and carbohydrate (Weis et al. 1996, Berg et al. 1999). From the protein characterisation results (Section 5.3.2), it can be said that there is a good probability for both types of interactions to take place due to the availability of the respective moieties.
However, the results from the PosiDAF experiments indicate that one type of protein- carbohydrate interaction has a more dominant role on the cell separation efficiency – conjugation. To illustrate, in the experiments conducted in section 5.3.4.1, it was noted that the cell separation was found to vary from less than 5% to 35% for the same species when different carbohydrates were dosed (Figure 5-5). This implies that there was a significant variability in the specificity of the dosed carbohydrates to the proteins present in the AOM – a characteristic of protein-carbohydrate conjugation (Singh et al.
2017). Furthermore, in Section 5.3.4.2, when the AOM was removed and the experiments were conducted by dosing proteins and carbohydrates that were non-
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Chapter 5 Protein-carbohydrate chemistry
specific to the proteins, the cell separation increased (Figure 5-6). Hence, it is possible that in strains that result in poor cell separation, the AOM consists of proteins and carbohydrates that are specific to the proteins; when these conjugate, they result in poor attachment to the proteins and carbohydrates on the cell surface and consequently poor cell separation.
The importance of protein-carbohydrate interactions between the cell surfaces and
AOM is further demonstrated through two more examples from this chapter. The observations of high standard deviations when conducting PosiDAF jar tests with
MA564 protein (Figure 5-8), and the comparable cell removal across all species when dosing MA564 proteins, uronic acids and fucose after AOM extraction (Figure 5-8). On analysing the extracted MA564 protein, several proteins were not detected. As a consequence, there was not enough scope for conjugation to take place despite the addition of the carbohydrates, which ultimately caused wide fluctuations in cell removal. However, when the right combination of proteins and carbohydrates are present in the AOM or are dosed externally, high cell removal can be achieved across several strains irrespective of cell characteristics (Figure 5-8).
In addition to the right combination of proteins and carbohydrates, the associated concentration is also critical for successful PosiDAF performance. For instance, several proteins, along with the same set of carbohydrates were found in MA555 and MA564, and in principle, the cell removal of both strains in PosiDAF should be the same.
However, the cell removal of MA564 was nearly three times higher than that obtained for MA555 (Yap et al. 2014b). As the concentrations of several carbohydrates in
MA555 were found to be much higher than in MA564 (Table 5-2), it is possible that all potential sites on the protein get conjugated by the free carbohydrates present in the
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Chapter 5 Protein-carbohydrate chemistry
AOM. Consequently, this leaves no free sites on the proteins to attach to the carbohydrates on the cell surface thereby denying the important link between the AOM and the cells.
5.4.2 Charged polymers as interlocutors between biopolymer-cell structures and bubbles
The application of charged polymers in PosiDAF has been extensively studied from the perspective of polymer-bubble attachment (Chapter 3, (Yap et al. 2014b)). But, it is suggested that the ability of charged polymers to bring together the biopolymers-cells linkages and bubbles during flotation is of equal importance. During the flotation of the three strains of algae with MA564 proteins and the carbohydrates (Figure 5-8), web-like networks were once again seen to form, the same as those observed in Chapter 4 and by
Yap et al. (2014b). Although these web-like networks are attributed to the conjugation between the proteins and carbohydrates, they were not visible prior to the introduction of cationic bubbles into the algal suspension. This suggests that despite the existence of linkages between the biopolymers in the AOM and those on the cell surface, it is only the introduction of the cationic bubbles that helps consolidate these into larger suprastructures. Figure 5-9 describes this mechanism.
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Chapter 5 Protein-carbohydrate chemistry
Figure 5- 9. Schematic of how the cationic polymers link the biopolymer-cell structures and the bubbles to result in effective flotation.
It is also suggested that during flotation, the cationic polymers interact with the protein- carbohydrate conjugates to aid in flotation, and not separately with the proteins as it has been previously claimed (Pivokonsky et al. 2006, Takaara et al. 2007, Henderson et al.
2010b). While Henderson et al. (2010b) suggested that the polymers primarily interact with the proteins present in the M. aeruginosa to enhance PosiDAF efficiency, the results from Figure (5-7) show that when the proteins and carbohydrates interact first with the polymer rather than the cells, the cell removal can get severely impacted. There are two standpoints from which this can be looked at: PDADMAC-protein interactions
1) prior to bubble formation, and 2) post bubble formation. In the former scenario, it is suggested that immobilisation of the protein takes place due to electrostatic interactions with PDADMAC solution in a manner similar to the deactivation of enzymes by polymer complexation (Margolin et al. 1985). In the second scenario where the protein interacts with the polymer coated bubble, surface denaturation of the protein can rapidly take place at the gas-liquid interface (Clarkson et al. 1999) which destroys the protein’s ability to take part in interactions with the carbohydrate. Overall, it is proposed that the proteins and carbohydrates in the AOM must conjugate with the proteins and ~ 144 ~
Chapter 5 Protein-carbohydrate chemistry
carbohydrates on the cell surface. Finally, the cationic polymers interact with the conjugated molecules to result in high flotation efficiency.
5.5 Summary
The specific conclusions drawn from this chapter are as follows:
• From the carbohydrate characterisation, it was seen that acidic carbohydrates
were detected in both strains of Microcystis aeruginosa. This finding is in line
with the observations made in Chapter 4 where it was concluded that acidic
carbohydrates were present in the strands that were formed during the flotation
of MA564.
• No proteins linked to N-linked glycosylation were found in any of the strains;
however, serine and threonine both of which are known to aid O-linked
glycosylation were detected in both strains of M. aeruginosa. No lectins that
were previously identified in cyanobacteria were detected in this study;
however, this does not rule out the presence of lectins in the species as
evidenced from the PosiDAF jar tests.
• BSA and MA564 protein were the best performing non-algal and algal proteins
respectively, and among the two, MA564 proteins outperformed BSA. However,
the proteins did not result in significant cell removal when used without any
carbohydrate.
• Cell separation efficiencies when dosing carbohydrates were lower in
comparison to those seen in Chapter 3. It is suggested that the dosed
carbohydrates interact with the proteins in the AOM. When variability in cell
separation was observed while dosing several carbohydrates, the interactions ~ 145 ~
Chapter 5 Protein-carbohydrate chemistry
were attributed to conjugation which may indicate the presence of lectins in the
AOM.
• When MA564 protein was dosed into the jar along with galacturonic acid and
fucose, the cell removal increased to over 85% across all strains. However, the
wide fluctuations in cell removal were observed as a lot of proteins were
denatured during protein extraction from the AOM.
• It is suggested that proteins and carbohydrates in the AOM must conjugate with
proteins and carbohydrates on the cell surfaces to result in effective flotation. If
the proteins and carbohydrates in the AOM conjugate with each other and not
between the AOM and the cell surface, cell removal can be reduced
considerably. This may explain why the cell removal in CV or MA555 was
lower when compared to MA564.
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Chapter 6
CONCLUSIONS AND FUTURE WORK
6.1 Conclusions
The underlying mechanisms governing algae and cyanobacteria separation in PosiDAF have been determined. The combined application of microscopic, chromatographic, spectroscopic, and spectrometric techniques to characterise the polymers, polymer- coated bubbles, and the AOM has helped in elucidating the principal mechanisms governing each key stage of the PosiDAF process. Figure 6-1 demonstrates these proposed mechanisms, summarising the findings in this thesis.
Initially, the comparison of hydrophobically modified polymers of PDADMAC and
PDMAEMA through atomic force microscopy studies and PosiDAF jar testing experiments revealed the nature of the polymer-bubble interactions; that is, how the different polymer backbones behaved and interacted at bubble surfaces. Specifically, it was determined that the PDMAEMA based polymers had a flat and tight conformation on the bubble surface whereas the PDADMAC polymers had an open conformation that extended into the matrix (Figures 3-7 and 6-1), therefore suggesting that the choice of polymer backbone plays a critical role in the outcome of the PosiDAF process. This was followed by the second major finding that the interplay between proteins, acidic carbohydrates, and carbohydrates present in the algal organic matter (AOM) and cells, and polymers, was crucial to the success of PosiDAF; potentially due to conjugation between the biopolymers that occurred via hydrogen bonding (Figure 6-1).
Furthermore, it was postulated that in strains that result in poor separation, the biopolymers that are present in the AOM conjugate with each other rather than
Chapter 6 Conclusions & recommendations
conjugating with the biopolymers on the cell surface. Due to this, the critical link between the cells and AOM does not get established leading to the observed poor separation. In cells that separate well, once the AOM-cell linkages are formed, the introduction of the charged polymers helps consolidate the AOM-cell-modified bubbles into large structures that float easily (Figure 6-1).
Figure 6- 1. Schematic describing: (a) hydrophobic interactions between the HMPs of
PDADMAC and bubble, and (b) lectin-carbohydrate conjugation caused by the hydrogen bonding between the cells and biopolymers.
Through the analysis of the AOM composition and its interactions with the polymer- coated bubbles, a deeper understanding of the PosiDAF process and the associated implication for the robustness of this process on a larger scale has been established.
Further tuning of the AOM composition will enhance the separation, resulting in an optimisation of this process. In a harvesting context, strains can be selected that have
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Chapter 6 Conclusions & recommendations
AOM that are enriched in the biopolymers that are known to enhance the separation efficiency.
By way of addressing the objectives outlined in Section 1.2, specific outcomes of this study are:
1) Review of the polymer application, and the influence and characterisation of algal organic matter on flotation (Chapter 2)
• In the first part of Chapter 2, key polymer characteristics, bubble properties, and
their synergistic impact on flotation were reviewed. From this, it was identified that
polymer properties, such as molecular weight (Mw), charge, and functional groups,
could aid in modifying particles or bubbles, and consequently impact flotation. The
use of hydrophobically-associating cationic polymers can increase the robustness of
the modified bubble DAF process by strongly interacting with both the bubble
surfaces and the particles. Techniques to determine the interactions between
polymers and bubbles were also reviewed, including atomic force microscopy.
While many techniques have been used for this purpose, none of these have been
employed to deduce polymer-bubble interactions in flotation that uses a modified-
bubble surface as opposed to modified particle surfaces.
• In Part B, the influence of AOM and its components on flotation were reviewed.
Prior research demonstrated that the character and concentration of AOM and its
components, specifically the biopolymers, could increase or decrease flotation
efficiency by interacting with the cells or the coagulants. However, the influence of
AOM and its biopolymeric composition in PosiDAF was not fully elucidated,
particularly the web-like networks that formed during flotation of M. aeruginosa
CS-564/01. Hence, a complete characterisation of the AOM was proposed and the
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Chapter 6 Conclusions & recommendations
experimental techniques used to analyse AOM and its components were reviewed.
It was found that there are well established techniques were available for
characterising the biopolymers such as mass spectrometry and ion-exchange
chromatography. However, none of these techniques were used to determine the
link between the AOM character and composition, and the PosiDAF efficiency.
2) To synthesise and characterise hydrophobically modified polymers (HMPs) of
PDADMAC and compare and contrast them in PosiDAF with previously studied
HMPs of PDMAEMA (Chapter 3)
• The hydrophobic modification of PDADMAC with various alkyl and aryl groups for
bubble surface modification in PosiDAF was successful. On application, analysis of
the results indicated that all the HMPs of PDADMAC and PDMAEMA achieved
increased or comparable cell removal to those obtained with unmodified
PDADMAC for all the species. When the PosiDAF treated effluent for all species
across the entire dose range was analysed, stable negative zeta potentials were
observed only when PDADMAC-BCF and PDMAEMA-C10 were used as the
bubble modification chemicals. Furthermore, when the residual PDADMAC-BCF
was quantified through fluorescence excitation emission matrix spectroscopy, very
low amount of polymer remained in the treated effluent. This combined with the
stable zeta potential of the treated water when dosing PDADMAC-BCF confirmed a
close adhesion of the polymer to the bubble surface.
3) To study the interactions between the polymer and the bubble at the air-water interface using atomic force microscopy (Chapter 3)
• Interpretation of the AFM results indicated that the adsorption conformation of the
polymer on the bubble surface altered based on the polymer backbone and the type ~ 150 ~
Chapter 6 Conclusions & recommendations
of hydrophobic moiety incorporated. For example, PDMAEMA and its derivatives
adsorbed very tightly to the bubble surface, unlike PDADMAC derivatives which
tended to project away from the bubble and into the surrounding matrix. Hence,
during further PosiDAF research, care must be taken before trialling a new polymer
as the polymer conformation on the bubble surface could influence the PosiDAF
process performance.
4) To examine the impact of varying AOM composition of several strains of algae and cyanobacteria on PosiDAF performance (Chapter 4)
• PosiDAF operated in the absence of AOM resulted in poor cell separation and
AOM is therefore considered a necessary system component.
• By dosing MA564 AOM into the jars containing CV and MH cells, linkages
between AOM and cells formed which consequently increased cell separation to
greater than 88% for both the species. This was comparable to the cell separation
obtained for MA555 in a previous PosiDAF study.
• Dosing of AOM1.50 and AOM0.45 from MA564 to jar tests containing samples of
MA555, CV and MH resulted in lower cell separations in comparison with
AOMC, demonstrating that high Mw biopolymeric material could impact the
PosiDAF process efficiency.
5) To identify the specific components of the AOM that affect PosiDAF performance (Chapter 4 and 5)
• The AOM fractions from all the species were characterised according to physio-
chemical properties, such as biopolymer composition and charge, and related
back to the PosiDAF performance. The AOM from MA564, which had the
highest acidic carbohydrate content in comparison to the other strains, had ~ 151 ~
Chapter 6 Conclusions & recommendations
greater than 95% PosiDAF efficiency in comparison to the other strains (Chapter
4).
• Web-like strands that enhanced flotation of MA564 were analysed. It was
demonstrated that interactions between acidic carbohydrates and proteins were
dominant at the strand edge whereas the centre of strand comprised of protein-
acidic carbohydrate and protein-carbohydrate globules (Chapter 4).
• The biopolymers that were observed to impact cell separation in PosiDAF were
successfully analysed. In terms of carbohydrate composition, acidic
carbohydrates were detected in MA555 and MA564 which corroborated with the
observations made in Chapter 4. Similarly, serine and threonine proteins both of
which are known to aid O-linked glycosylation with carbohydrates were detected
in MA555 and MA564. Additionally, lectins although unidentified were
observed to be present in the AOM fractions of the species (Chapter 5).
6) To determine whether the addition of the previously identified components can enhance performance for strains those are not well separated by PosiDAF
(Chapter 5)
• Dosing proteins and carbohydrates resulted in fluctuations in PosiDAF
efficiency. Proteins and carbohydrates did not result in significant cell separation
efficiencies when dosed separately. But, when MA564 protein was dosed into
the jar along with galacturonic acid and fucose, the cell removal increased to
over 80% across all strains. It is suggested that protein-carbohydrate conjugation
in the AOM played a major role in dictating PosiDAF efficiency; when the
proteins were not strongly conjugated with the carbohydrates in the AOM, they
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Chapter 6 Conclusions & recommendations
were able to interact and form better links with the proteins and carbohydrates
on the cell surface.
6.2 Recommendations for future work
The outcomes of this study have led to the identification of a number of research areas that could be investigated in the future. These areas are detailed as follows:
1) Current research on modified-bubble DAF has been confined to simulated and
water treatment plant influents. Particularly, only one water treatment plant site
has been investigated where the process optimisation with HMPs of
PDMAEMA was not successful. Therefore, it is recommended that biopolymers
that were demonstrated to enhance cell separation in this study be used for
PosiDAF process optimisation in a pilot scale plant.
2) The proteins that are deemed responsible for influencing flotation require further
investigation and identification. It is known that lectins are present in the AOM;
however, further research needs to be carried out through hemagglutination
assays, DNA isolation and gene expression to identify these lectins and how
they influence cell separation in PosiDAF.
3) The mechanism for the polymer conformation on the bubble surfaces has been
determined in this thesis by testing only two polymer backbones under
controlled laboratory conditions. A future research avenue could be the use of
advanced characterisation techniques such as neutron scattering and single
molecule extension of polymers to give insights into the polymer and bubble
properties that result in conformational changes, as well as predict polymer-
bubble interactions for other polymer backbones.
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Chapter 6 Conclusions & recommendations
4) Analyses of the AOM character and composition have contributed to the
understanding of reasons behind the process enhancements in PosiDAF. With
this knowledge, testing of strains that are naturally high in acidic carbohydrates
in PosiDAF is recommended. This would be particularly useful in the field of
algae harvesting for bioprocessing where PosiDAF application could result in
high algal cell separation efficiency and solid-liquid ratio.
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Chapter 7
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Yap, R. K. L., Whittaker, M., Diao, M., Stuetz, R. M., Jefferson, B., Bulmus, V., Peirson, W. L., Nguyen, A. V. and Henderson, R. K. (2014b). "Hydrophobically-associating cationic polymers as micro-bubble surface modifiers in dissolved air flotation for cyanobacteria cell separation." Water Research 61: 253-262.
Yates, P., Yan, Y., Jameson, G. and Biggs, S. (2001). Bridging flocculation mechanisms: the role of chain flexibility. Proceedings of the 6th World Congress of Chemical Engineering. Melbourne, Australia.
Zhang, C. and Zhang, J. (2015). "Current techniques for detecting and monitoring algal toxins and causative harmful algal blooms." J Environ Anal Chem 2(123): 2380-2391.1000123.
Zhang, J. and Pelton, R. (1999a). "The dynamic behavior of poly(N-isopropylacrylamide) at the air/water interface." Colloids and Surfaces A: Physicochemical and Engineering Aspects 156(1– 3): 111-122.
Zhang, M. and Guiraud, P. (2017). "Surface-modified microbubbles (colloidal gas aphrons) for nanoparticle removal in a continuous bubble generation-flotation separation system." Water Research 126(Supplement C): 399-410.
Zhang, W., Xiao, P., Liu, Y., Xu, S., Xiao, F., Wang, D. and Chow, C. W. K. (2014). "Understanding the impact of chemical conditioning with inorganic polymer flocculants on soluble extracellular polymeric substances in relation to the sludge dewaterability." Separation and Purification Technology 132: 430-437.
Zhang, X., Bishop, P. L. and Kinkle, B. K. (1999b). "Comparison of extraction methods for quantifying extracellular polymers in biofilms." Water science and technology 39(7): 211-218. ~ 180 ~
Chapter 7 References
Zhang, X., Fan, L. and Roddick, F. A. (2013). "Influence of the characteristics of soluble algal organic matter released from Microcystis aeruginosa on the fouling of a ceramic microfiltration membrane." Journal of membrane science 425: 23-29.
Zhang, X. L. and Han, Q. (2011). "Effect of cationic polymers on flotation deinking efficiency of MOW." Zhongguo Zaozhi Xuebao/Transactions of China Pulp and Paper 26(1): 12-16.
Zhao, G., Jiang, L., He, Y., Li, J., Dong, H., Wang, X. and Hu, W. (2011). "Sulfonated Graphene for Persistent Aromatic Pollutant Management." Advanced Materials 23(34): 3959- 3963.
Zhivkov, A. M. (2013). Electric Properties of Carboxymethyl Cellulose.
Zhou, J., Mopper, K. and Passow, U. (1998). "The role of surface-active carbohydrates in the formation of transparent exopolymer particles by bubble adsorption of seawater." Limnology and Oceanography 43(8): 1860-1871.
Zhou, Y. and Franks, G. V. (2006). "Flocculation mechanism induced by cationic polymers investigated by light scattering." Langmuir 22(16): 6775-6786.
Zilliges, Y., Kehr, J.-C., Meissner, S., Ishida, K., Mikkat, S., Hagemann, M., Kaplan, A., Börner, T. and Dittmann, E. (2011). "The cyanobacterial hepatotoxin microcystin binds to proteins and increases the fitness of Microcystis under oxidative stress conditions." PloS one 6(3): e17615.
Zilliges, Y., Kehr, J.-C., Mikkat, S., Bouchier, C., de Marsac, N. T., Börner, T. and Dittmann, E. (2008). "An extracellular glycoprotein is implicated in cell-cell contacts in the toxic cyanobacterium Microcystis aeruginosa PCC 7806." Journal of bacteriology 190(8): 2871-2879.
Zlokarnik, M. (1998). "Separation of activated sludge from purified waste water by Induced Air Flotation (IAF)." Water Research 32(4): 1095-1102.
~ 181 ~
Chapter 8
APPENDIX
Appendix A: Chapter 3
PDADMAC PDADMAC-BC PDADMAC-BCF PDADMAC-C5 PDMAEMA PDMAEMA-C10 75
65
55
45 Cell Removal Removal Cell (%) 35
25
5
-5 B
-15
-25 Zeta PotentialZeta (mV)
-35 0 0.002 0.004 0.006 0.008 0.01 0.012 Dose of Polymer (meq/L)
Figure A1. Comparison of PosiDAF performance with PDADMAC, PDADMAC-C5,
PDADMAC -BC, PDADMAC -BCF, PDMAEMA and PDMAEMA-C10 on CV; A – Cell removal efficiency and B – Zeta Potential of effluent Vs Dose of PDADMAC, PDADMAC-
C5, PDADMAC-BC, PDADMAC-BCF, PDMAEMA and PDMAEMA-C10;
Chapter 8 Appendix A: Chapter 3
PDADMAC PDADMAC-BC 55 PDADMAC-BCF PDADMAC_C5 PDMAEMA PDMAEMA-C10
45
35 Cell Removal Removal Cell % 25
15
20 B
10
0
Zeta PotentialZeta (mV) -10
-20 0 0.0005 0.001 0.0015 0.002 0.0025 0.003 Dose of Polymer (meq/L)
Figure A2. Comparison of PosiDAF performance with PDADMAC, PDADMAC-C5,
PDADMAC-BC, PDADMAC-BCF, PDMAEMA and PDMAEMA-C10 on MA555; A –
Cell removal efficiency and B – Zeta Potential of effluent Vs Dose of PDADMAC,
PDADMAC-C5, PDADMAC-BC, PDADMAC-BCF, PDMAEMA and PDMAEMA-C10;
~ 183 ~
Chapter 8 Appendix A: Chapter 3
PDADMAC PDADMAC-BC PDADMAC-BCF PDADMAC-C5 PDMAEMA PDMAEMA-C10 100
90
80 Cell Removal Removal Cell (%) 70
60
5
-5 B
-15
Zeta PotentialZeta (mV) -25
-35 0 0.0005 0.001 0.0015 0.002 0.0025 0.003 Dose of Polymer (meq/L)
Figure A3. Comparison of PosiDAF performance with PDADMAC, PDADMAC-C5,
PDADMAC-BC, PDADMAC-BCF, PDMAEMA and PDMAEMA-C10 on MA564; A –
Cell removal efficiency and B – Zeta Potential of effluent Vs Dose of PDADMAC,
PDADMAC-C5, PDADMAC-BC, PDADMAC-BCF, PDMAEMA and PDMAEMA-C10;
~ 184 ~
Chapter 8 Appendix A: Chapter 3
2.5
2
1.5 R² = 0.9987
Intensity 1 Intensity
0.5
0 0 0.002 0.004 0.006 0.008 0.01 0.012 Polymer concentration (meq/L)
Figure A4. Standard Curve of fluorescence intensity vs concentration of PDADMAC-BCF
2.5
2 R² = 0.9974
1.5
1 Intensity Intensity
0.5
0 0 0.002 0.004 0.006 0.008 0.01 0.012 Polymer concentration (meq/L)
Figure A5. Standard Curve of fluorescence intensity vs concentration of PDADMAC-BC
~ 185 ~
Chapter 8 Appendix A: Chapter 3
Figure A6. A sample standard fluorescence excitation emission matrix (F-EEM) obtained while testing PDADMAC-BCF in buffer used for preparing the DAF influent. Polymer peak excited and emitted at approximately 260 nm and 280 nm, respectively. Polymer concentration: 0.9 mg/L (0.004 meq/L)
~ 186 ~
Chapter 8 Appendix A: Chapter 3
Table A1. Polymers trialled: PDADMAC-BC, PDADMAC-BCF. Species trialled on:
Chlorella vulgaris CS – 42/7.
Initial polymer Residual polymer in treated % Polymer residual concentration effluent in treated effluent
meq/ L mg/L meq/ L mg/L
BC 0.002 0.49 0.0012 0.303 62.3 ± 1.1
0.004 0.98 0.00244 0.607 61.1 ± 1.9
0.006 1.45 0.00372 0.899 62.2 ± 2.1 PDADMAC - 0.008 2 0.0048 1.2 60.2 ± 1.6
0.010 2.48 0.0055 1.364 55.6 ± 1.8
Initial polymer Residual polymer in treated % Polymer residual concentration effluent in treated effluent
meq/ L mg/L meq/ L mg/L
BCF 0.002 0.45 0.00064 0.144 32.3 ± 2
0.004 0.9 0.00132 0.297 33.4 ± 2.4
0.006 1.4 0.00174 0.406 29.5 ± 0.8 PDADMAC - 0.008 1.95 0.00184 0.448 23.2 ± 1.1
0.010 2.45 0.0017 0.416 17.6 ± 1.7
~ 187 ~
Chapter 8 Appendix A: Chapter 3
Table A2. Polymers trialled: PDADMAC-BC, PDADMAC-BCF. Species trialled on:
Microcystis aeruginosa CS – 564/01.
Initial polymer Residual polymer in treated % Polymer residual concentration effluent in treated effluent
meq/ L mg/L meq/ L mg/L
BC 0.0005 0.125 0.0003 0.303 60.3 ± 1.1
0.001 0.25 0.0006 0.147 59.2 ± 1.9
0.0015 0.375 0.00094 0.213 57.2 ± 2.1 PDADMAC - 0.002 0.5 0.0013 0.305 61.2 ± 1.6
0.0025 0.625 0.0014 0.412 66.6 ± 1.8
Initial polymer Residual polymer in treated % Polymer residual concentration effluent in treated effluent
meq/ L mg/L meq/ L mg/L
BCF 0.0005 0.125 0.00064 0.041 33.3 ± 2
0.001 0.25 0.00132 0.09 36.6 ± 2.4
0.0015 0.375 0.00174 0.123 33.2 ± 0.8 PDADMAC - 0.002 0.5 0.00184 0.08 16 ± 1.1
0.0025 0.625 0.0017 0.143 23.3 ± 1.7
~ 188 ~
Chapter 8 Appendix A: Chapter 3
Table A3. Polymers trialled: PDADMAC-BC, PDADMAC-BCF. Species trialled on:
Microcystis aeruginosa CS – 555/1.
Initial polymer Residual polymer in treated % Polymer concentration effluent residual in treated effluent
meq/ L mg/L meq/ L mg/L
BC 0.0005 0.125 0.00035 0.086 69 ± 16
0.001 0.25 0.00078 0.195 78 ± 1.1
0.0015 0.375 0.0012 0.28 75± 2.9 PDADMAC - 0.002 0.5 0.0015 0.41 78 ± 2.1
0.0025 0.625 0.0016 0.48 77 ± 2.5
Initial polymer Residual polymer in treated % Polymer concentration effluent residual in treated effluent
meq/ L mg/L meq/ L mg/L
BCF 0.0005 0.125 0.00011 0.02 22 ± 3.6
0.001 0.25 0.00029 0.07 29 ± 1.8
0.0015 0.375 0.00042 0.106 28.5 ± 0.11 PDADMAC - 0.002 0.5 0.00046 0.115 23 ± 2.8
0.0025 0.625 0.00065 0.162 26 ± 3.3
~ 189 ~
Chapter 8 Appendix B: Chapter 4
Appendix B: Chapter 4
Table B1. Band assignment- FTIR, adapted from Giordano et al. (2001), Stehfest et al.
(2005), Burgula et al. (2007), Villacorte et al. (2015b), Gonzalez-Torres (2017)
Peak Wavenumber Functional group Biomolecular origin range1 (cm-1)
1 3500-3100 Hydrogen-bonded OH stretch Polysaccharides 2 2950-2850 Aliphatic C-H stretch Lipid 3 1750-1700 Ester, ketone and carboxylic Lipid, chlorophyll, acid C=O stretch carotenoid pigments 4 1660-1610 Amide C=O stretch Protein (Amide I)
Ѵ(COO)asym 5 1580-1550 Amide C-N stretch and N-H Protein (Amide II), bend Polysaccharides 6 1370-1310 Carboxylate anion stretch COO- Polysaccharides Amide CN and NH vibrations (charged)
CH2 deformation Protein (Amide III) Lipid 7 1260-1230 Sulphated ester stretch R-O- Polysaccharides - SO3 (charged) 8 1150-1130 Ether C-O-C stretch Polysaccharides 9 1100-1050 Alcohol C-O stretch Polysaccharides Phosphate ester P=O stretch Nucleic acids 1 The position of these assignments can vary in the literature. Individual bands may have attributions from a number of molecular groups.
~ 190 ~
Chapter 8 Appendix B: Chapter 4
Figure B1. LC-OCD and UV results for (A) MA555 (B) MA564 (C) MH (D) CV.
~ 191 ~
Chapter 8 Appendix B: Chapter 4
(A)
100µm
(B)
100µm
(C)
100µm
(D)
100µm
Figure B2. TEP stained with Alcian blue for (A) – MA555, (B) – MA564, (C) – CV, (D) –
MH.
~ 192 ~
Chapter 8 Appendix B: Chapter 4
4 AOMc AOM1.50 AOM0.45 3.6 3.2 2.8
2.4 2 1.6 1.2 0.8
Concentartion Concentartion (mg/L) 0.4 0
25 B 20
15
10
5 Concentartion Concentartion (mg/L)
0 MH CV MA555 MA564
Figure B3. (A) Protein; (B) carbohydrate concentrations of the respective filtered fractions of AOM from MH, CV, MA555 and MA564.
~ 193 ~
Chapter 8 Appendix B: Chapter 4
Figure B4. Images (A) to (D) - 20 x 20 μm2 FTIR images of strand edge with the corresponding cross section spectra of the strand edge. Images (E) to (H) - 20 x 20 μm2
FTIR images of strand centre with the corresponding cross section spectra of the centre of the strand. Red regions in images (A) to (H) are the relative intensities of the protein amide I peak at 1630 cm-1. Green regions in (A), (C), (E), (G) are the relative intensities of the carbohydrate C-O vibration at 1030 cm-1, and in (B), (D), (F), (H) are the relative intensities of the acidic carbohydrate anion COO- vibration at 1330 cm-1. The regions of orange and yellow in (A) to (H) are the overlaps of the respective chemical regions in the image.
~ 194 ~
Chapter 8 Appendix C: Chapter 5
Appendix C: Chapter 5
Table C1. List of enzymes present in MA555 and their properties before extraction.
Mw Score emPAI Protein Name 52510 811 10.68 Ribulose bisphosphate carboxylase large chain 51582 609 3.72 ATP synthase subunit beta 35848 591 6.21 Ketol-acid reductoisomerase 46514 352 0.95 Enolase 53945 316 0.93 ATP synthase subunit alpha 16461 258 7.31 Nucleoside diphosphate kinase 37223 188 1.59 D-fructose 1,6-bisphosphatase 42936 160 0.68 Probable agmatinase 2 44055 156 0.66 Sulfate adenylyltransferase 46255 155 0.47 Serine hydroxymethyltransferase 77961 150 0.26 Polyribonucleotide nucleotidyltransferase 48232 149 0.32 Glucose-1-phosphate adenylyltransferase 44873 140 0.49 LL-diaminopimelate aminotransferase 42613 132 1.07 Phosphoglycerate kinase 34894 123 0.46 ATP synthase gamma chain 37367 106 0.13 Undecaprenyl-phosphate 146740 98 0.10 DNA-directed RNA polymerase subunit beta 59046 98 0.16 Dihydroxy-acid dehydratase 7922 98 0.70 NAD(P)H-quinone oxidoreductase 31765 89 0.32 4-hydroxy-tetrahydrodipicolinate 52970 80 0.18 Glutamine synthetase 37889 80 0.12 Phosphoribulokinase 95978 79 0.05 Alanine--tRNA ligase 33930 75 0.14 Probable branched-chain-amino-acid aminotransferase 58609 72 0.16 Dihydroxy-acid dehydratase 19266 69 0.25 NAD(P)H-quinone oxidoreductase subunit J 38674 63 0.26 Fructose-bisphosphate aldolase 16210 59 0.31 Cyanate hydratase OS=Synechococcus sp. 12715 56 0.40 Photosystem II reaction center Psb28 protein 54238 56 0.18 Glutamate--tRNA ligase 116655 51 0.04 Error-prone DNA polymerase
~ 195 ~
Chapter 8 Appendix C: Chapter 5
13231 51 0.39 Ribulose bisphosphate carboxylase small chain 27729 50 0.17 Ubiquinone biosynthesis C-methyltransferase 102762 48 0.04 Pyruvate dehydrogenase E1 component 48796 48 0.20 Ferredoxin--NADP reductase 124866 48 0.07 DNA-directed RNA polymerase subunit beta 68228 47 0.07 Chaperone protein DnaK 38683 47 0.12 D-alanine--D-alanine ligase 67666 45 0.07 Arginine--tRNA ligase 46277 45 0.10 Adenosylhomocysteinase 35515 43 0.13 UDP-N-acetylenolpyruvoylglucosamine reductase 42881 43 0.11 1-deoxy-D-xylulose 5-phosphate reductoisomerase 21663 42 022 Superoxide dismutase [Fe] 52953 42 0.09 ATP-dependent RNA helicase DbpA 39147 41 0.12 Uroporphyrinogen decarboxylase 44452 41 0.11 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (ferredoxin) 17169 41 0.29 Ribosomal RNA large subunit methyltransferase H 26834 41 0.18 Short-chain reductase protein NovJ 155485 40 0.03 DNA-directed RNA polymerase subunit beta' 72798 39 0.06 Acetyl-coenzyme A synthetase 20319 39 0.24 Inorganic pyrophosphatase 54339 39 0.09 ATP synthase subunit alpha 59185 39 0.08 D-3-phosphoglycerate dehydrogenase 35636 38 0.13 Transaldolase A 34927 38 0.13 Transaldolase 34220 37 0.14 4-hydroxythreonine-4-phosphate dehydrogenase 76252 37 0.06 DNA ligase 25515 37 0.19 Phosphatidylserine decarboxylase proenzyme 87482 36 0.05 Phenylalanine--tRNA ligase beta subunit 29473 36 0.16 Indole-3-glycerol phosphate synthase 113672 36 0.04 Lon protease 41288 36 0.11 D-alanine--D-alanine ligase 43030 35 0.11 Glycerol-1-phosphate dehydrogenase [NAD(P)+] 13571 35 0.38 Holo-[acyl-carrier-protein] synthase 59469 35 0.08 Anthranilate synthase component 1 35798 34 0.13 Ribosomal RNA small subunit methyltransferase H
~ 196 ~
Chapter 8 Appendix C: Chapter 5
19617 34 0.25 ATP synthase subunit delta 55119 33 0.08 L-xylulose/3-keto-L-gulonate kinase 46138 33 0.10 Homoserine dehydrogenase 40135 33 0.12 Ribosomal RNA large subunit methyltransferase M 50201 32 0.09 ATP synthase subunit beta 1 13381 32 0.38 Ferredoxin-thioredoxin reductase, catalytic chain 25680 31 0.19 6-carboxyhexanoate--CoA ligase 29745 31 0.16 2-dehydro-3-deoxyphosphooctonate aldolase 133439 31 0.03 ATP-dependent helicase/deoxyribonuclease subunit B 58128 30 0.08 Glucan endo-1,3-beta-glucosidase 42948 30 0.11 Tryptophan synthase beta chain 36853 29 0.13 Glyceraldehyde-3-phosphate dehydrogenase 74407 29 0.06 Threonine--tRNA ligase 20938 29 0.23 ATP-dependent Clp protease proteolytic subunit 1 64709 28 0.07 Arginine--tRNA ligase 18528 27 0.27 Endoribonuclease YbeY 62595 26 0.07 Methanol dehydrogenase [cytochrome c] subunit 1 38831 26 0.12 3-dehydroquinate synthase 75018 25 0.06 DNA ligase 58440 25 0.08 Proline--tRNA ligase 2 27378 25 0.17 Ubiquinone biosynthesis O-methyltransferase 46394 25 0.10 Isocitrate dehydrogenase [NADP] 47602 24 0.10 Histidine--tRNA ligase
Table C2. List of non-enzymes present in MA555 and their properties before extraction.
Mw Score emPAI Protein Name 44890 380 0.81 Elongation factor Tu 10723 363 34.58 10 kDa chaperonin 14412 305 5.05 30S ribosomal protein S13 17211 257 2.54 Allophycocyanin beta subunit 7482 256 15.61 Gas vesicle structural protein 57617 203 0.72 60 kDa chaperonin 1 12471 203 1.82 Nitrogen regulatory protein P-II 17348 200 1.73 Allophycocyanin beta chain
~ 197 ~
Chapter 8 Appendix C: Chapter 5
40717 168 0.39 Flagellin B 40624 162 0.39 Flagellin A 58352 148 0.46 60 kDa chaperonin 1 20330 142 2.64 Ribosome-recycling factor 15821 140 2.93 50S ribosomal protein L15 18175 139 1.05 C-phycocyanin beta chain 67573 137 0.14 Chaperone protein dnaK2 57739 117 0.47 60 kDa chaperonin 2 13630 115 0.88 Azurin 16782 99 0.68 50S ribosomal protein L9 17275 98 1.13 Allophycocyanin alpha subunit 36997 98 0.13 Putative amino-acid ABC transporter-binding protein 6183 97 2.77 Gas vesicle protein C (Fragment) 58845 96 0.35 60 kDa chaperonin 2 9286 96 0.58 Gas vesicle structural protein A 9554 95 1.43 30S ribosomal protein S16 14894 94 1.40 Photosystem II 12 kDa extrinsic protein 27618 92 0.89 Elongation factor Ts 18220 92 1.61 30S ribosomal protein S5 12687 92 1.77 50S ribosomal protein L7/L12 11641 90 1.09 Thioredoxin-1 12696 87 0.97 50S ribosomal protein L7/L12 13591 86 0.38 50S ribosomal protein L7/L12 17305 86 1.13 Allophycocyanin beta chain 12758 85 2.86 50S ribosomal protein L7/L12 77586 77 0.06 Elongation factor G 36677 74 0.27 General L-amino acid-binding periplasmic protein 17507 74 0.64 30S ribosomal protein S7 48935 73 0.10 Nitrate transport protein NrtA 12025 68 1.93 Carbon dioxide-concentrating mechanism protein 66770 66 0.14 Aspartate--tRNA(Asp/Asn) ligase 67866 65 0.07 Chaperone protein dnaK2 23087 65 0.21 50S ribosomal protein L4 9498 63 1.43 UPF0367 protein MAE_19160 9694 60 0.55 50S ribosomal protein L27 13667 57 0.37 30S ribosomal protein S11
~ 198 ~
Chapter 8 Appendix C: Chapter 5
27416 52 0.17 30S ribosomal protein S3 34637 52 0.29 Orange carotenoid-binding protein 52144 50 0.09 Sensor protein CreC 57856 48 0.17 60 kDa chaperonin 48445 47 0.10 Nitrate transport protein NrtA 85808 46 0.05 Glutathione biosynthesis bifunctional protein GshAB 14679 45 0.34 30S ribosomal protein S8 20553 43 0.24 Elongation factor P 13017 43 0.39 50S ribosomal protein L18 38423 43 0.12 Protein RecA 18770 42 0.26 Macro domain-containing protein in non 5'region 17912 41 0.28 Allophycocyanin subunit alpha-B 8536 41 0.64 Acyl carrier protein 22539 41 0.21 50S ribosomal protein L3 49430 41 0.09 Bicarbonate-binding protein CmpA 13704 40 0.37 Putative regulator AldR 20023 40 0.24 Putative peroxiredoxin in rubredoxin operon 51593 40 0.09 Trigger factor 165771 40 0.03 Hemolysin 37625 37 0.12 Gas vesicle protein GvpN 26017 37 0.18 UPF0502 protein Patl_1161 11999 36 0.43 Membrane-associated protein slr1513 12630 36 0.41 Probable 30S ribosomal protein PSRP-3 13796 36 0.37 50S ribosomal protein L19 11895 34 0.44 Carbon dioxide-concentrating mechanism protein 35325 34 0.13 Orange carotenoid-binding protein 59110 34 0.08 Uncharacterized protein sll0772 23401 33 0.21 Transcription termination/antitermination protein NusG 95043 33 0.05 DNA mismatch repair protein MutS 30212 33 0.16 Elongation factor Ts 80884 32 0.06 Uncharacterized protein MPN_440 24983 32 0.19 Thiamine import ATP-binding protein ThiQ 37834 31 0.12 Fe(3+) ions import ATP-binding protein FbpC 27119 31 0.18 Ribosomal RNA small subunit methyltransferase G 47053 31 0.10 tRNA modification GTPase MnmE 109443 31 0.04 Translation initiation factor IF-2
~ 199 ~
Chapter 8 Appendix C: Chapter 5
105161 30 0.04 LPS-assembly protein LptD 7518 30 0.74 30S ribosomal protein S21 126572 30 0.04 Chromosome partition protein Smc 22747 30 0.21 50S ribosomal protein L25 53170 29 0.09 Trigger factor 169794 28 0.03 Chromosome partition protein MukB 21346 28 0.23 UPF0307 protein YjgA 20745 27 0.23 50S ribosomal protein L5 50715 26 0.09 GTPase Der 20058 23 0.24 Ecotin 8836 23 0.61 Photosystem I iron-sulfur center
Table C3. List of enzymes present in MA564 and their properties before extraction.
Mw Score emPAI Protein Name 52510 719 9.73 Ribulose bisphosphate carboxylase large chain 51582 466 2.07 ATP synthase subunit beta 44873 382 2.28 LL-diaminopimelate aminotransferase 35848 317 1.69 Ketol-acid reductoisomerase 46255 301 1.16 Serine hydroxymethyltransferase 16461 298 9.83 Nucleoside diphosphate kinase 46514 263 0.47 Enolase 44055 214 1.02 Sulfate adenylyltransferase 48517 203 0.32 Glucose-1-phosphate adenylyltransferase 33930 167 0.30 Probable branched-chain-amino-acid aminotransferase 53945 150 0.51 ATP synthase subunit alpha 49865 150 0.31 Dihydrolipoyl dehydrogenase 13231 104 0.92 Ribulose bisphosphate carboxylase small chain 68391 96 0.30 Methanol dehydrogenase [cytochrome c] subunit 1 31765 96 0.52 4-hydroxy-tetrahydrodipicolinate synthase 66770 94 0.22 Aspartate--tRNA(Asp/Asn) ligase 77961 86 0.12 Polyribonucleotide nucleotidyltransferase 34894 86 0.29 ATP synthase gamma chain 19826 80 0.55 6,7-dimethyl-8-ribityllumazine synthase 52992 80 0.18 Glutamine synthetase
~ 200 ~
Chapter 8 Appendix C: Chapter 5
26034 78 0.18 Triosephosphate isomerase 37223 75 0.43 D-fructose 1,6-bisphosphatase class 2 52970 73 0.18 Glutamine synthetase 16676 65 0.69 Biotin carboxyl carrier protein of acetyl-CoA carboxylase 34735 65 0.14 L-lactate dehydrogenase 37889 64 0.12 Phosphoribulokinase 44878 57 0.10 Tryptophan synthase beta chain 7922 56 0.70 NAD(P)H-quinone oxidoreductase subunit O 32498 52 0.15 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase 46121 51 0.10 Adenosylhomocysteinase 57923 51 0.08 Peptide methionine sulfoxide reductase MsrA/MsrB 36261 50 0.28 Ketol-acid reductoisomerase 39147 49 0.12 Uroporphyrinogen decarboxylase 33594 48 0.14 Ornithine carbamoyltransferase 40389 47 0.12 Threonine synthase 47726 46 0.10 tRNA(Ile)-lysidine synthase 44452 46 0.11 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase 39436 45 0.25 Phosphoserine aminotransferase 21663 44 0.22 Superoxide dismutase [Fe] 17169 41 0.29 Ribosomal RNA large subunit methyltransferase H 146740 41 0.03 DNA-directed RNA polymerase subunit beta 54339 41 0.09 ATP synthase subunit alpha 33328 40 0.30 Ferredoxin--NADP reductase 25317 40 0.19 3-oxoacyl-[acyl-carrier-protein] reductase 2 67666 40 0.07 Arginine--tRNA ligase 33254 40 0.14 Probable 2-methylisocitrate lyase 2 16389 40 0.30 Cyanate hydratase 87482 40 0.05 Phenylalanine--tRNA ligase beta subunit 53702 39 0.09 Serine--tRNA ligase 31334 39 0.15 S-methyl-5'-thioadenosine phosphorylase 38683 38 0.12 D-alanine--D-alanine ligase 40091 36 0.12 Phospho-N-acetylmuramoyl-pentapeptide-transferase 42948 35 0.11 Tryptophan synthase beta chain 26834 35 0.18 Short-chain reductase protein NovJ 44361 34 0.11 Aspartokinase 68684 34 0.07 Quinoprotein ethanol dehydrogenase
~ 201 ~
Chapter 8 Appendix C: Chapter 5
58128 34 0.08 Glucan endo-1,3-beta-glucosidase 20319 34 0.24 Inorganic pyrophosphatase 20409 34 0.24 ATP synthase subunit delta 55119 34 0.08 L-xylulose/3-keto-L-gulonate kinase 36759 33 0.13 Tetraacyldisaccharide 4'-kinase 43030 33 0.11 Glycerol-1-phosphate dehydrogenase [NAD(P)+] 78812 33 0.06 Malate synthase G 58332 33 0.08 Ribonuclease Y 38795 32 0.12 Phospho-N-acetylmuramoyl-pentapeptide-transferase 66805 32 0.07 1-deoxy-D-xylulose-5-phosphate synthase 23375 32 0.21 Ribosomal RNA small subunit methyltransferase G 71188 32 0.06 DNA gyrase subunit B 49577 32 0.09 UDP-N-acetylmuramate--L-alanine ligase 24531 31 0.20 Thymidylate kinase 19076 31 0.26 Inorganic pyrophosphatase 55519 31 0.08 3-octaprenyl-4-hydroxybenzoate carboxy-lyase 60734 30 0.08 Short-chain-fatty-acid--CoA ligase 152895 30 0.03 DNA-directed RNA polymerase subunit beta 20938 30 0.23 ATP-dependent Clp protease proteolytic subunit 1 54238 30 0.09 Glutamate--tRNA ligase 30405 30 0.16 Prolipoprotein diacylglyceryl transferase 49141 29 0.09 N-succinylarginine dihydrolase 33153 29 0.14 Cysteine synthase 22567 29 0.21 GTP cyclohydrolase-2 85744 28 0.05 DNA translocase FtsK 58302 27 0.08 Lysine--tRNA ligase 46837 24 0.10 Adenosylmethionine-8-amino-7-oxononanoate aminotransferase 47602 24 0.10 Histidine--tRNA ligase
Table C4. List of non-enzymes present in MA564 and their properties before extraction.
Mw Score emPAI Protein Name 12471 391 10.27 Nitrogen regulatory protein P-II 10723 304 9.82 10 kDa chaperonin
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Chapter 8 Appendix C: Chapter 5
17404 285 2.49 Allophycocyanin beta chain 27618 255 0.89 Elongation factor Ts 17359 224 1.73 R-phycocyanin-2 subunit alpha 44890 194 0.35 Elongation factor Tu 54896 179 0.50 Bifunctional purine biosynthesis protein 17278 155 1.13 C-phycocyanin-1 alpha chain 42268 153 0.37 Putative ABC transporter periplasmic-binding protein 7482 142 8.47 Gas vesicle structural protein 17401 141 1.72 Allophycocyanin alpha chain 18175 139 0.61 C-phycocyanin beta chain 13630 134 0.88 Azurin 11128 120 1.16 Carbon dioxide-concentrating mechanism protein 8836 119 5.72 Photosystem I iron-sulfur center 14412 119 0.82 30S ribosomal protein S13 36997 109 0.13 Putative amino-acid ABC transporter-binding protein 9498 103 1.43 UPF0367 protein MAE_19160 12025 103 1.93 Carbon dioxide-concentrating mechanism protein 13241 101 0.39 50S ribosomal protein L18 15821 97 1.99 50S ribosomal protein L15 20330 92 0.54 Ribosome-recycling factor 17275 91 1.13 Allophycocyanin alpha subunit 10433 91 0.51 Ferredoxin-1 8415 91 1.72 Cytochrome c6 13761 88 1.56 C-phycocyanin beta chain (Fragments) 43293 78 0.51 Arginine biosynthesis bifunctional protein ArgJ 67573 76 0.07 Chaperone protein dnaK2 34637 75 0.14 Orange carotenoid-binding protein 9081 73 0.59 DNA-binding protein HU-beta 13591 73 0.38 50S ribosomal protein L7/L12 6183 66 0.94 Gas vesicle protein C (Fragment) 12715 66 0.40 Photosystem II reaction center Psb28 protein 68158 64 0.07 Chaperone protein DnaK 27591 60 0.17 Positive alginate biosynthesis regulatory protein 9990 60 0.53 30S ribosomal protein S16 10908 60 0.48 10 kDa chaperonin 48935 59 0.10 Nitrate transport protein NrtA
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12624 58 0.41 50S ribosomal protein L7/L12 8357 58 0.66 50S ribosomal protein L29 18509 56 0.60 C-phycocyanin beta chain 51593 55 0.09 Trigger factor protein 22539 54 0.21 50S ribosomal protein L3 14624 53 0.34 30S ribosomal protein S6 58352 51 0.08 60 kDa chaperonin 1 14894 51 0.79 Photosystem II 12 kDa extrinsic protein 4980 50 1.24 Major cold shock protein (Fragment) 18625 49 0.60 Allophycocyanin subunit beta-18 11713 48 0.44 Thioredoxin-1 14679 48 0.34 30S ribosomal protein S8 30506 47 0.16 50S ribosomal protein L2 9349 42 0.58 30S ribosomal protein S17 9694 42 0.55 50S ribosomal protein L27 10560 42 0.50 10 kDa chaperonin 14689 41 0.34 Cytochrome c-551 34927 41 0.13 Transaldolase 23463 40 0.21 50S ribosomal protein L3 9658 40 0.55 UPF0296 protein PCC8801_0196 55992 37 0.08 Bifunctional purine biosynthesis protein PurH 7663 37 0.73 Photosystem I reaction center subunit IV 17507 36 0.28 30S ribosomal protein S7 43021 36 0.11 Arginine biosynthesis bifunctional protein ArgJ 20058 36 0.24 Ecotin 24866 35 0.19 Putative ATP-dependent Clp protease 16949 35 0.29 SsrA-binding protein 34452 35 0.14 Phosphate-binding protein PstS 69623 34 0.07 Heat-responsive suppressor HrsA 23466 33 0.21 UPF0316 protein SERP1448 23402 33 0.21 30S Ribosomal protein S4 7011 33 0.81 Cold shock protein CapA (Fragment) 38422 33 0.12 Protein RecA 15030 33 0.34 Cytochrome c-550 12630 33 0.41 Probable 30S ribosomal protein PSRP-3 12364 33 0.42 Nitrogen regulatory protein P-II
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20023 32 0.24 Putative peroxiredoxin in rubredoxin operon 16782 32 0.30 50S ribosomal protein L9 11862 31 0.44 Thioredoxin 51198 31 0.09 Uncharacterized protein PA3922 22747 30 0.21 50S ribosomal protein L25 40604 30 0.12 Putrescine-binding periplasmic protein SpuD 59016 29 0.08 60 kDa chaperonin 1 11272 29 0.46 Thioredoxin C-1 57739 29 0.08 60 kDa chaperonin 2 12993 28 0.39 Probable 30S ribosomal protein PSRP-3 12171 28 0.42 Nitrogen regulatoryprotein P-II 20681 28 0.24 UPF0312 protein Pput_4854 49430 28 0.09 Bicarbonate-bindingprotein CmpA 40693 26 0.12 Flagellin A
Table C5. List of enzymes present in CV and their properties before extraction.
Mw Score emPAI Protein Name 42520 56 0.1 Acetylornithine aminotransferase 50222 56 0.08 Ribose 1,5-bisphosphate phosphokinase PhnN 66640 53 0.06 Aspartate--tRNA ligase 53721 45 0.08 DNA gyrase subunit B (Fragment) 34953 42 0.12 L-lactate dehydrogenase 32536 39 0.13 Pyridoxal 5'-phosphate synthase subunit PdxS 80339 38 0.05 Malate synthase G 59321 36 0.07 Cholesterol oxidase 42408 34 0.1 Phosphoribosylglycinamide formyltransferase 34777 33 0.12 Anthranilate phosphoribosyltransferase 47522 29 0.09 Glutamyl-tRNA reductase 62212 28 0.07 Glucose-6-phosphate isomerase 9344 28 0.49 DNA-directed RNA polymerase subunit omega 32769 26 0.13 Homoserine kinase 80208 26 0.05 Polyribonucleotide nucleotidyltransferase 102552 26 0.04 Phosphoenolpyruvate carboxylase 31076 26 0.13 Putative thiosulfate sulfurtransferase
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47150 25 0.09 Glucose-1-phosphate adenylyltransferase 33625 24 0.12 Acetyl-coenzyme A carboxylase carboxyl transferase 71017 17 0.06 tRNA uridine 5-carboxymethylaminomethyl modification enzyme
Table C6. List of non-enzymes present in CV and their properties before extraction.
Mw Score emPAI Protein Name 20553 46 0.21 Ribosome-recycling factor 107395 41 0.04 Protein translocase subunit SecA 120383 40 0.03 Phycobiliprotein ApcE 57631 39 0.15 60 kDa chaperonin 26085 38 0.16 Probable GTP-binding protein EngB 58318 36 0.07 60 kDa chaperonin 43112 34 0.10 Elongation factor Tu 1 21252 34 0.2 Elongation factor P-like protein 58586 33 0.14 Methyl-accepting chemotaxis protein 3 13308 32 0.33 50S ribosomal protein L7/L12 105118 32 0.04 Translation initiation factor IF-2 11347 31 0.40 30S ribosomal protein S18 79612 30 0.06 UvrABC system protein B 30989 30 0.13 Urease accessory protein UreD 9625 27 0.47 Insertion element IS402 uncharacterized 9.6 kDa protein 70297 27 0.06 UvrABC system protein C 25882 22 0.16 Uncharacterized protein YveK 68891 18 0.06 Probable potassium transport system protein kup 1
Table C7. List of enzymes present in MA555 and their properties after extraction.
Mw Score emPAI Protein Name 52510 811 10.68 Ribulose bisphosphate carboxylase large chain 51582 609 3.72 ATP synthase subunit beta 46255 155 0.47 Serine hydroxymethyltransferase 77961 150 0.26 Polyribonucleotide nucleotidyltransferase
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48232 149 0.32 Glucose-1-phosphate adenylyltransferase 44873 140 0.49 LL-diaminopimelate aminotransferase 34894 123 0.46 ATP synthase gamma chain 37367 106 0.13 Undecaprenyl-phosphate 146740 98 0.10 DNA-directed RNA polymerase subunit beta 31765 89 0.32 4-hydroxy-tetrahydrodipicolinate 52970 80 0.18 Glutamine synthetase 37889 80 0.12 Phosphoribulokinase 12715 56 0.40 Photosystem II reaction center Psb28 protein 54238 56 0.18 Glutamate--tRNA ligase 116655 51 0.04 Error-prone DNA polymerase 27729 50 0.17 Ubiquinone/menaquinone biosynthesis C-methyltransferase 46277 45 0.10 Adenosylhomocysteinase 35515 43 0.13 UDP-N-acetylenolpyruvoylglucosamine reductase 21663 42 022 Superoxide dismutase [Fe] 52953 42 0.09 ATP-dependent RNA helicase DbpA 39147 41 0.12 Uroporphyrinogen decarboxylase 17169 41 0.29 Ribosomal RNA large subunit methyltransferase H 34220 37 0.14 4-hydroxythreonine-4-phosphate dehydrogenase 87482 36 0.05 Phenylalanine--tRNA ligase beta subunit 13381 32 0.38 Ferredoxin-thioredoxin reductase, catalytic chain 25680 31 0.19 6-carboxyhexanoate--CoA ligase 27378 25 0.17 Ubiquinone biosynthesis O-methyltransferase 46394 25 0.10 Isocitrate dehydrogenase [NADP] 47602 24 0.10 Histidine--tRNA ligase
Table C8. List of non-enzymes present in MA555 and their properties after extraction.
Mw Score emPAI Protein Name 44890 380 0.81 Elongation factor Tu 10723 363 34.58 10 kDa chaperonin 14412 305 5.05 30S ribosomal protein S13 17211 257 2.54 Allophycocyanin beta subunit 7482 256 15.61 Gas vesicle structural protein 57617 203 0.72 60 kDa chaperonin 1
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12471 203 1.82 Nitrogen regulatory protein P-II 17348 200 1.73 Allophycocyanin beta chain 15821 140 2.93 50S ribosomal protein L15 18175 139 1.05 C-phycocyanin beta chain 67573 137 0.14 Chaperone protein dnaK2 57739 117 0.47 60 kDa chaperonin 2 13630 115 0.88 Azurin 16782 99 0.68 50S ribosomal protein L9 17275 98 1.13 Allophycocyanin alpha subunit 36997 98 0.13 Putative amino-acid ABC transporter-binding protein YhdW 12696 87 0.97 50S ribosomal protein L7/L12 13591 86 0.38 50S ribosomal protein L7/L12 17305 86 1.13 Allophycocyanin beta chain 12758 85 2.86 50S ribosomal protein L7/L12 77586 77 0.06 Elongation factor G 36677 74 0.27 General L-amino acid-binding periplasmic protein AapJ 17507 74 0.64 30S ribosomal protein S7 66770 66 0.14 Aspartate--tRNA(Asp/Asn) ligase 67866 65 0.07 Chaperone protein dnaK2 23087 65 0.21 50S ribosomal protein L4 9498 63 1.43 UPF0367 protein MAE_19160 9694 60 0.55 50S ribosomal protein L27 13667 57 0.37 30S ribosomal protein S11 27416 52 0.17 30S ribosomal protein S3 34637 52 0.29 Orange carotenoid-binding protein 52144 50 0.09 Sensor protein CreC 22747 30 0.21 50S ribosomal protein L25 53170 29 0.09 Trigger factor
Table C9. List of enzymes present in MA564 and their properties after extraction.
Mw Score emPAI Protein Name 52510 719 9.73 Ribulose bisphosphate carboxylase large chain 51582 466 2.07 ATP synthase subunit beta 44873 382 2.28 LL-diaminopimelate aminotransferase
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35848 317 1.69 Ketol-acid reductoisomerase 46255 301 1.16 Serine hydroxymethyltransferase 16461 298 9.83 Nucleoside diphosphate kinase 46514 263 0.47 Enolase 44055 214 1.02 Sulfate adenylyltransferase 48517 203 0.32 Glucose-1-phosphate adenylyltransferase 33930 167 0.30 Probable branched-chain-amino-acid aminotransferase 53945 150 0.51 ATP synthase subunit alpha 49865 150 0.31 Dihydrolipoyl dehydrogenase 13231 104 0.92 Ribulose bisphosphate carboxylase small chain 68391 96 0.30 Methanol dehydrogenase [cytochrome c] subunit 1 31765 96 0.52 4-hydroxy-tetrahydrodipicolinate synthase 66770 94 0.22 Aspartate--tRNA(Asp/Asn) ligase 77961 86 0.12 Polyribonucleotide nucleotidyltransferase 34894 86 0.29 ATP synthase gamma chain 19826 80 0.55 6,7-dimethyl-8-ribityllumazine synthase 52992 80 0.18 Glutamine synthetase 7922 56 0.70 NAD(P)H-quinone oxidoreductase subunit O 32498 52 0.15 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase 46121 51 0.10 Adenosylhomocysteinase 57923 51 0.08 Peptide methionine sulfoxide reductase MsrA/MsrB 36261 50 0.28 Ketol-acid reductoisomerase 39147 49 0.12 Uroporphyrinogen decarboxylase 66805 32 0.07 1-deoxy-D-xylulose-5-phosphate synthase 23375 32 0.21 Ribosomal RNA small subunit methyltransferase G 71188 32 0.06 DNA gyrase subunit B 49577 32 0.09 UDP-N-acetylmuramate--L-alanine ligase 24531 31 0.20 Thymidylate kinase 46837 24 0.10 Adenosylmethionine-8-amino-7-oxononanoate aminotransferase 47602 24 0.10 Histidine--tRNA ligase
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Table C10. List of non-enzymes present in MA564 and their properties after extraction.
Mw Score emPAI Protein Name 12471 391 10.27 Nitrogen regulatory protein P-II 10723 304 9.82 10 kDa chaperonin 17404 285 2.49 Allophycocyanin beta chain 27618 255 0.89 Elongation factor Ts 17359 224 1.73 R-phycocyanin-2 subunit alpha 44890 194 0.35 Elongation factor Tu 14412 119 0.82 30S ribosomal protein S13 36997 109 0.13 Putative amino-acid ABC transporter-binding protein YhdW 9498 103 1.43 UPF0367 protein MAE_19160 12025 103 1.93 Carbon dioxide-concentrating mechanism protein CcmK 13241 101 0.39 50S ribosomal protein L18 15821 97 1.99 50S ribosomal protein L15 20330 92 0.54 Ribosome-recycling factor 17275 91 1.13 Allophycocyanin alpha subunit 10433 91 0.51 Ferredoxin-1 8415 91 1.72 Cytochrome c6 13761 88 1.56 C-phycocyanin beta chain (Fragments) 43293 78 0.51 Arginine biosynthesis bifunctional protein ArgJ 67573 76 0.07 Chaperone protein dnaK2 34637 75 0.14 Orange carotenoid-binding protein 9081 73 0.59 DNA-binding protein HU-beta 13591 73 0.38 50S ribosomal protein L7/L12 6183 66 0.94 Gas vesicle protein C (Fragment) 12715 66 0.40 Photosystem II reaction center Psb28 protein 68158 64 0.07 Chaperone protein DnaK 27591 60 0.17 Positive alginate biosynthesis regulatory protein 9990 60 0.53 30S ribosomal protein S16 10908 60 0.48 10 kDa chaperonin 48935 59 0.10 Nitrate transport protein NrtA 12624 58 0.41 50S ribosomal protein L7/L12 14689 41 0.34 Cytochrome c-551 34927 41 0.13 Transaldolase 23463 40 0.21 50S ribosomal protein L3
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9658 40 0.55 UPF0296 protein PCC8801_0196 55992 37 0.08 Bifunctional purine biosynthesis protein PurH 7663 37 0.73 Photosystem I reaction center subunit IV 23402 33 0.21 30S Ribosomal protein S4 7011 33 0.81 Cold shock protein CapA (Fragment) 38422 33 0.12 Protein RecA 15030 33 0.34 Cytochrome c-550 12630 33 0.41 Probable 30S ribosomal protein PSRP-3 12364 33 0.42 Nitrogen regulatory protein P-II 20023 32 0.24 Putative peroxiredoxin in rubredoxin operon 49430 28 0.09 Bicarbonate-bindingprotein CmpA 40693 26 0.12 Flagellin A
Table C11. List of enzymes present in CV and their properties after extraction.
Mw Score emPAI Protein Name 42520 56 0.1 Acetylornithine aminotransferase 50222 56 0.08 Ribose 1,5-bisphosphate phosphokinase PhnN 66640 53 0.06 Aspartate--tRNA ligase 53721 45 0.08 DNA gyrase subunit B (Fragment) 34953 42 0.12 L-lactate dehydrogenase 32536 39 0.13 Pyridoxal 5'-phosphate synthase subunit PdxS 80339 38 0.05 Malate synthase G 59321 36 0.07 Cholesterol oxidase 31076 26 0.13 Putative thiosulfate sulfurtransferase 47150 25 0.09 Glucose-1-phosphate adenylyltransferase 33625 24 0.12 Acetyl-coenzyme A carboxylase carboxyl transferase 71017 17 0.06 tRNA uridine 5-carboxymethylaminomethyl modification enzyme
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Table C12. List of non-enzymes present in CV and their properties after extraction.
Mw Score emPAI Protein Name 20553 46 0.21 Ribosome-recycling factor 107395 41 0.04 Protein translocase subunit SecA 120383 40 0.03 Phycobiliprotein ApcE 57631 39 0.15 60 kDa chaperonin 25882 22 0.16 Uncharacterized protein YveK 68891 18 0.06 Probable potassium transport system protein kup 1
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