ABSTRACT GYLYUK, ALEXEY. Properties of Gallium Nitride-Microorganism Interfaces

ABSTRACT

GYLYUK, ALEXEY. Properties of Gallium Nitride-Microorganism Interfaces. (Under the direction of Dr. Albena Ivanisevic).

A wide portfolio of advanced programmable materials and structures has been developed for biological applications in the last two decades. In particular, due to their unique properties, semiconducting materials have been utilized in areas of biocomputing, implantable electronics, and healthcare. As a new concept of such programmable material design, biointerfaces based on inorganic semiconducting materials as substrates introduce unconventional paths for bioinformatics and biosensing. In particular, understanding how the properties of a substrate can alter microbial biofilm behavior enables researchers to better characterize and thus create programmable biointerfaces with necessary characteristics on-demand. During the preliminary research stage, the most promising semiconductor material types along with target microorganisms were identified and subsequently utilized for further studies. Gallium-based surfaces (particularly, GaN with different doping levels) were chosen as the most promising semiconducting materials to be used in inorganic substrate-microorganism biointerfaces. The work was based on the hypothesis that they can be tailored to induce controllable microorganism behavior under specific user defined external conditions. At this stage of the work Pseudomonas aeruginosa was the test organism in the first two experimental parts of the dissertation work. As a the typical in-hospital infection, P.aeruginosa possesses high survivability and enhanced abilities to form resilient biofilms on various surfaces. These factors allowed us to perform a set of studies that enable to create GaN-P.aeruginosa biointerfaces, characterize them and identify factors triggering the morphological and physiological reactions of formulated structures. The final stage of the dissertation project involved utilization of the yeast culture Saccharomyces cerevisiae which expanded the class of microorganisms used in the dissertation work. Comparing several characterization techniques and applying modern approaches permitted the confirmation and further validated the results obtained previously. Additionally, identification of instruments and techniques possessing the most advantages for this type of research made it possible to improve the data gathering and analysis as well as define the future research directions.

by Alexey Gulyuk

A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Materials Science and Engineering

Raleigh, North Carolina 2020

APPROVED BY:

______Dr. Albena Ivanisevic Dr. Ramon Collazo Committee Chair

______Dr. Nelson Vinueza Benitez Dr. Yaroslava Yingling

DEDICATION

To my Mom and Dad.

BIOGRAPHY

Alexey V. Gulyuk received his B.S. in Engineering Physics from Belarusian State

University in 2014. He moved on to graduate studies in physics at North Carolina Central

University. Alexey successfully completed his research project under guidance of Prof. Igor

Bondarev and earned an M.S. degree in Physics in 2016. The same year, he started the PhD program in Materials Science and Engineering at North Carolina State University and worked under the direction of Prof. Albena Ivanisevic.

While being part of undergraduate and graduate community, Alexey expressed interest in natural sciences and engineering, teaching, mentoring, and establishing scientific communications in interdisciplinary groups. His previous research was primarily focused on signal generation and analysis, development of theoretical physical models involving advanced simulation techniques.

Current research interests lie in characterizing the inorganic semiconducting materials and development of new bioelectronic modalities by interfacing inorganic substrates with various microorganisms and organic tissue.

After receiving his PhD in Materials Science and Engineering, Alexey hopes to continue participating in research activities and serving as an active contributor to innovative development and progress in scientific community and industry.

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ACKNOWLEDGMENTS

First, I would love to express special gratitude to my advisor, Dr. Albena Ivanisevic, for her dedicated work, mentorship, guidance, support, and understanding through my years at NC

State. Thank you to my committee members, Dr. Yaroslava Yingling, Dr. Ramon Collazo, and Dr.

Nelson Vinueza Benitez for their valuable input and continual feedback. Furthermore, I would like to acknowledge our collaborators and people I had pleasure to work with: Dr. Dennis LaJeunesse from UNCG who became a source of continuous inspiration and wisdom, Dr. Susan Bernacki from

BME department who is always willing to help or provide some advice, Dr. Ronny Kirste from

MSE, Dr. Mark Walters from SMIF at Duke University, Chuck Mooney from AIF. I am very thankful to: Dr. Bernacki, Dr. Mozdziak and Dr. Petitte, Dr. Mackenzie and Valerie Lapham, Steve

Barr and Lisa Chang, and especially to Dr. Yingling for being excellent professors and mentors through the course of my interdisciplinary studies at NC State. Many thanks to people at MSE who always provided so much needed assistance: Dr. Elizabeth Dickey, Dr. Lewis Reynolds, George

Martell, and Edna Deas. Best wishes and many thanks to my group members: Sara Gleco, Patrick

Snyder and Taylor Adams for being inspiring peers and simply good friends.

I am also thankful to Dr. Igor V. Bondarev who was my mentor since I first came to the

US, whose guidance I relied on, and who always served as an example of academic excellence.

On a personal level, I would like to thank people who made everything possible and without whom I would never be here – my parents, Alla and Vasiliy – a source of everlasting love and continuous support in my life. Furthermore, I am sincerely thankful to Tanya who believed in me, for always being there for me. To my aunt Olga, Alex, Julia for their so much appreciated help, encouragement, and a lot of fun time spent together. Lastly, to my friends: Mitch, Chris, Ming,

Silvestr, Katya, and Ira for being a valued part of my life.

TABLE OF CONTENTS

LIST OF TABLES ...... vi LIST OF FIGURES ...... vii Chapter 1: Tuning Microbial Activity via Programmatic Alteration of Cell/Substrate Interfaces ...... 1 1.1 Introduction ...... 1 1.2 Microorganisms and Interactions with Planar Inorganic Matter and Their Importance for a Programmable Materials ...... 3 1.2.1 Advanced Programmable Materials ...... 4 1.2.2 Current Trends in Programmable Materials ...... 4 1.3 Interactions of Microorganisms with Planar Inorganic Matter and Their Importance for a Programmable Biointerface Conception ...... 7 1.3.1 Biointerface as a Novel Type of Material ...... 7 1.3.2 Inorganic Materials Interacting with Microorganisms ...... 9 1.3.3 Physiological Responses Within Biointerfacial Structures...... 12 1.3.4 Genetic Responses in Biological Systems ...... 17 1.4 Conclusions ...... 23 Chapter 2: Characterization of Pseudomonas Aeruginosa Films on Different Inorganic Surfaces Before and After UV Light Exposure ...... 29 2.1 Introduction ...... 29 2.2 Results and Discussion ...... 30 2.2.1 Characterization Prior To Bacterial Film Formation ...... 30 2.2.2 P.aeruginosa Film Characterization ...... 36 2.3 Experimental section ...... 41 2.4 Conclusions ...... 43 Chapter 3: The Interfacial Properties of Doped Semiconductor Materials Can Alter the Behavior of Pseudomonas Aeruginosa Films ...... 44 3.1 Introduction ...... 44 3.2 Results and Discussion ...... 45 3.2.1 Substrate Surface Characterization ...... 46 3.2.2 Biointerface Response Tests ...... 53 3.3 Experimental section ...... 58 3.4 Conclusions ...... 62 Chapter 4: Evaluating Stress-Triggered Intracellular ROS Generation of Saccharomyces Cerevisiae Cultures on Inorganic Semiconducting Substrates ...... 63 4.1 Introduction and Motivation ...... 63 4.2 Results and Discussion ...... 64 4.3 Experimental section ...... 68 4.4 Conclusions ...... 70 Chapter 5: Conclusions and Future Directions...... 71 5.1 Conclusions ...... 71 5.2 Future Directions and Perspectives ...... 72 BIBLIOGRAPHY ...... 74 APPENDIX ...... 103 Appendix A General Abbreviations...... 103

LIST OF TABLES

Table 1.1 Representative examples of smart materials and their potential application areas .. 25

Table 1.2 Semiconducting materials used for bioapplications ...... 26

Table 1.3 Classification of physiological responses expressed in biointerfacial structures ..... 27

Table 1.4 Genetic response studies performed on microorganisms ...... 28

LIST OF FIGURES

Figure 1.1 General conception of a biointerfacial structure ...... 7

Figure 2.1 Mean roughness values for plane substrate surfaces of Au, GaN and SiOx ...... 31

Figure 2.2 Representative digital pictures showing standard antibacterial tests of Pseudomonas aeruginosa performed for Au, GaN and SiOx substrates ...... 32

Figure 2.3 Mean contact angle values for Au, GaN and SiOx materials before and after irradiation with UV light ...... 33

Figure 2.4 Mean values representing the chemical composition on the Au, GaN and SiOx films before and after irradiation with UV light ...... 34

Figure 2.5 High resolution XPS scans of O 1s region on Au, GaN and SiOx before and after irradiation with UV light ...... 35

Figure 2.6 Representative AFM scans of Pseudomonas aeruginosa films formed on various inorganic substrates before and after UV-irradiation ...... 36

Figure 2.7 Mean length-to-width ratios of Pseudomonas aeruginosa cells formed on Au, GaN and SiOx surfaces before and after irradiation with UV light ...... 37

Figure 2.8 ”Blebbing effect” caused by UV treatment. Mean roughness values of single Pseudomonas aeruginosa cells scanned with AFM on Au, GaN and SiOx surfaces before and after irradiation with UV light ...... 38

Figure 2.9 (A) Surface adhesion histogram obtained from AFM Adhesion force map. Both figures represent data for Pseudomonas aeruginosa biofilm on the surface of GaN substrate before UV light irradiation. (B) Mean surface adhesion values of Pseudomonas aeruginosa biofilms on Au, GaN and SiOx sample before and after UV light treatment ...... 39

Figure 2.10 Summary of Fluo-4 Direct Calcium assay results ...... 40

Figure 3.1 (A) KPFM time-dependent measurements of GaN samples under UV-irradiation along with (B) mean surface potential values evaluated by KPFM of non-exposed and exposed to the UV light GaNm and GaNh samples ...... 46

Figure 3.2 Changes in water contact angles of clean and modified GaN surfaces ...... 48

Figure 3.3 Chemical compositions of (A) Clean GaN surfaces, (B) Fast GaN surfaces, and (C) Slow-treated GaN surfaces obtained by analysis of XPS survey scans ...... 49

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Figure 3.4 XPS analysis of the high-resolution data in the following regions: (A) O1s and (B) C1s regions for Clean, Fast-treated, and Slow-treated samples ...... 51

Figure 3.6 ROS studies of P.aeruginosa on GaN for (A) Clean, (B) Fast-treated, and (C) Slow-treated samples ...... 54

Figure 3.7 ROS studies of Ga(NO₃)₃ salt for (A) Droplet and (B) Solution techniques applied to P.aeruginosa cells initializing the biofilm formation ...... 56

Figure 3.8 Fluo-4 Direct Calcium assay test results ...... 57

Figure 3.9 DetectX® Catalase colorimetric activity kit data ...... 57

Figure 4.1 ROS plate reader studies of S.cerevisiae cultures on GaN samples ...... 65

Figure 4.2 Representative optical microscopy scans of S.cerevisiae biofilms ...... 66

Figure 4.3 ROS fluorescence microscopy studies of S.cerevisiae cultures ...... 67

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CHAPTER 1

Tuning Microbial Activity via Programmatic Alteration of Cell/Substrate Interfaces

1.1 Introduction Over the past decade, novel approaches and new types of inorganic and organic polymers, various composites, micro and nanomaterials and their implementations have become a prolific and broad research direction for the field of materials engineering. Importantly, smart materials with programmable characteristics and abilities to change their parameters upon certain conditions have received a lot of attention by scientists with different interests and research backgrounds.[1–4] Furthermore, unique properties and opportunities associated with nano-scale size of materials open paths for multiple applications in electronics, aerospace and automotive fields, as well as in chemical and bio engineering, healthcare and food processing.[5–8] In particular, the last three areas have benefited tremendously from novel approaches, since new nanomaterials have enabled scientists to develop various biosensing complexes[9,10], drug delivery systems[11–13], implants with improved capabilities and performance[14,15], effective antibacterial coatings[16,17], and additional advanced inventions taking over consumer products by making them simpler, safer, and more comfortable. Advanced materials with programmable characteristics for bioapplications have to possess stable structure and need to be biocompatible.[18,19] Thus, interactions between inorganic materials and living organisms play a key role in the research related to programmable materials in bioengineering and healthcare. The development of the aforementioned tools and systems (i.e. implants, biosensors, antibacterial coatings), depends on a clear understanding of how the properties of a particular material and its derivatives affect its interactions with cells, bacteria or fungi.[20] Such bio-organic interfacial structures are extensively researched by scientists working in the area of electro-biotics.[21–23] Additionally, by characterizing parameters such as cell survivability, cell attachment and cell growth rate, it has been possible to quantify the biocompatibility of specific materials of interest. There is an impressive amount of literature on the interactions of a variety of cell types and microorganisms with biocompatible polymers and composites[24,25], nanocolloids and nanoparticles.[11,26–31] For instance, ZnO has been used for drug [32,33] [34,35] [36,37] delivery and as a biocide . Ag nanoparticles are an effective bactericide. TiO2 has

also shown promise for various bio-applications[14,26] and as a material that suppresses the growth of unwanted microbial biofilms[17,38]. These well-studied examples of materials and applications provide great opportunities for their adaptation in novel applications that require predictable outcomes and programmable characteristics. While formulating programmable biointerfacial structures researchers need to understand the interactions of living species with 2D/planar/interfacial abiotic materials, whether they possess nanostructured features or not. However, in a number of cases, the inorganic material does not need to be in a colloidal formulation. Planar/2D materials, nanotextured or patterned, (but not nanosized) serve as a substrate for the colonization of a microorganism and the subsequent formation of a biofilm. These planar/2D materials do not penetrate the cell, and many cellular responses can be linked to the properties of the created biointerface. In these cases the biointerface is defined as an advanced closed system or a region of contact between a living cell and a substrate, which can be either organic or inorganic.[39] The biointerfacial properties of the substrate, biolayer and external stimuli coexist, and are interdependent.[40] Therefore, it is critical not only to characterize the individual components of this biointerface, but also how its components program and develop potentially new and unexpected emergent properties from their interactions. Different applications require certain materials with specific characteristics. Inorganic bio- functional material systems whether they are insulators, metals and semiconductors need to be non-toxic, biotissue-compatible, and often biodegradable as well.[41] For example, Ti-alloy implants have been tailored for good tissue attachment and high antibacterial capabilities[42], whereas bacterial biofilm biosensors should not suppress bacterial cell growth and be highly sensitive to any changes in the surrounding environment.[43,44] Furthermore, doping of semiconducting substrates with noble metals provides high conductivity, which is crucial in bioelectronics, however some noble metals have considerable biological effects, such as Ag, while others like Au are neutral. Therefore even minor components of a biointerface need to be considered as sub-biointerfaces because they are broadly used in biosensors and bioimplants.[41,45– 47] With these caveats in mind, any work on developing advanced materials for biosensors and bioimplantable electronics devices needs to quantify and tailor the properties of these “living organism-inorganic semiconducting substrate” programmable systems. The scope of this review is to highlight the common trends in programmable or “smart" biomaterials development and cover current progress in the research of planar/2D inorganic

biointerfaces: materials and their interactions with microorganisms. Additionally, the discussion on the future directions of this field helps to better oversee the role of biointerfaces as adaptable materials or structures that can be employed in a range of different environments. We will also summarize possible ways of advancing the understanding of processes occurring in these multi- component systems and identify the most promising candidates for future research in the field of bioelectronics. In this review paper, we emphasize the recognition of the major challenges associated with determining and defining physiological and genetic responses in biofilms to abiotic interfacial materials. We have identified the organic and inorganic programmable materials that are developed for practical and potential bioapplications to better understand the tendencies in this research field; the inorganic materials that are currently utilized as substrates for biointerfaces with microbes; the species of microbe that are targeted and may be utilized as a part of a programmable biointerfacial structure. Table 1 summarizes our representative findings on advanced programmable materials that typically change their properties or express reactions under certain conditions, usage, or stimuli. Table 2 compiles the planar semiconductor substrates in/for biosystems, the types of microorganisms that have been used to study biological responses to these substrates and their applications. Table 3 classifies the types of physiological responses that may be observed in certain microorganisms due to substrate characteristics and (or) external stimuli applied. Table 4 summarizes the genetic responses of microorganisms to inorganic materials. The tables also include references that comment on response mechanisms associated with specific target organisms. The goal of this organization is to define the cellular mechanisms and processes involved at a functional biointerfaces, correlate surface properties with these cellular behaviors, and perhaps be able to use the correlation to predict the behavior and responses that are associated with specific materials to develop new “smart” interfacial biosystems with programmable characteristics. 1.2 Microorganisms and Interactions with Planar Inorganic Matter and Their Importance for Programmable Materials In this section we provide a definition for a “smart” material and then formulate possible requirements for typical biointerfacial structure that may be considered programmable. We envision novel types of biointerfaces as promising candidates with wide portfolio of potential applications. In general sense such materials may be completely organic, but the main focus will be on materials and approaches that may be used in the areas of bioelectronics and bio-sensing.

1.2.1 Advanced Programmable Materials Typically, advanced programmable materials are material compositions that possess a set of highly-adaptable physical, chemical, electrical or even biological characteristics responding accordingly to changes in external conditions or stresses.[3,4] Specifically, these are materials that may change parameters in a predictable fashion and the degree of certain responses will be based on the independent sensing abilities of the developed structure. Furthermore, the alterations of characteristics for programmable materials are reversible in many cases, thus allowing to restore the original shape, color, potential, pH as example parameters.[4] Currently there are multiple technologies that allow to fabricate structures with programmable behavior, and the more or less sophisticated concepts typically involve a combination of approaches and techniques. Such materials can be applied in various spheres of modern science and engineering and have a wide range of potential applications of design approaches. Particularly, our focus is on bio-applications of “smart“ materials. This is important for proposing a concept of biointerfacial structure as a programmable system and for its further discussion. One has to understand whether the biointerface aligns with an idea of “smart” or, in other words, programmable material. To do such, first one needs to define whether reviewed structures and compounds can be considered programmable and to identify the most common types of materials, approaches, and stimuli or conditions that trigger a response. Furthermore, the areas of potential applications or prototype systems that utilize the invented smart-material structures are of a particular focus. 1.2.2 Current Trends in Programmable Materials Table 1 summarizes our findings on research results available and allows to make some general conclusions regarding the current trends in programmable materials for bioapplications. In column 1, the materials that serve as a main base for introduced concepts are listed and classified accordingly. Particularly, many papers use several types of nano[48] and micro[49–51] particles (denoted as NPs and MPs in Table 1, respectively), quantum dots (QDs)[52–54], polymers[55–58] or hydrogels[59–63], and in such cases the generalized descriptions were applied. Column 2 comments on unique “smart” features of reviewed materials and structures that may distinguish the programmable materials from conventional metals, alloys, or polymers. These features range from material disintegration[61,62,64] or swelling[59,60] under certain external conditions, to various types of luminescence[52,53] or fluorescence[54,65], release of chemical agents[49–51] or toxins (like doxorubicin or DOX-toxicity)[65], and many forms of flexibility or

motion (such as triggered folding, bending, returning to its original shape - shape-memory effect)[55,56,63,66–72]. As for stimuli (Table 1, column 3) applied to achieve desired response from reviewed programmable materials, they are very diverse and can be generally classified as the following: 1) temperature fluctuations[59,60,69,71–73], 2) aqueous solutions of solvents or water[51,54,62–64,74–76], 3) pH level-associated changes[50,64,77], 4) chemically-triggering agents[49,50,78] 5) influence of magnetic field[55,73], electrical[48,66,70,79] or mechanical[61,80] stimulation, 6) various types of irradiation (near-infrared (NIR) or visible light)[55,56,74–76,81–83]. Finally, the potential applications of the programmable materials are listed in column 4. The concepts and materials are used or to be used in bioimaging[53,54,58,60–62,64,65,82], drug delivery[45-48,56,58-62,74,81], soft robotics[55,67,71–73,79,80], and biosensing[66,68–70,78] or bioelectronic[73,80,83] systems. Taking a closer look at the variety of materials utilized in programmable systems and listed in Table 1, it is possible to classify them according to their form or structure as 1) particles (top sector of Table 1), 2) layers or fibers: layers of polymers and hydrogels (middle sector in the table), 3) composites (bottom part of the table), and even hybrid structures where inorganic layers are interfaced with tissue. It is important to note that the most utilized groups of materials in programmable compounds development are hydrogels and polymers. For example, polydimethylsiloxane (PDMS)[55,79], polyolefin[56], polycaprolactone (PCL)/hexamethylene diisocyanate (HDI)/butadiene (BD) compound[69], polyethylene glycol (PEG)[61,80], chondroitin sulfate[62], polyglycidyl methacrylate (PGMA)[78], polychlorinated biphenyl (PCB)[68], poly-N- sopropylacrylamide (PNIPAM)[83] are among the polymers used to create nanofibers, composites, hydrogels and other hybrid structures. Additionally, some organic compounds such as dextran[49] or 2-dimethylaminoethyl methacrylate (DMAEMA)[50], keratin[57,58], and resin[67] serve to accomplish similar aims. For the purposes of bioelectronics, one often needs to examine the use of inorganic entities such as metals and semiconductors. Thus, gold (Au)[48,65], iron (Fe)[67,73], platinum (Pt)[72], rhodium [68] [71] [51] [52,53] (Rh) , gallium (Ga) , silicon (Si) , tungsten disulfide (WS2) , molybdenum disulfide [81] [53] [74,83] (MoS2), titanium dioxide (TiO2) , boron nitride (BN) and carbon nanotubes (CNTs) are common representatives in materials systems we have reviewed. In addition, there are examples of review papers on inorganic substrates utilizing them as smart materials, and reviews on interfacing such materials with different biological entities. For instance, Jeong et al.[84] discuss the development of hybrid gold nanoparticles for diagnostics, tumor treatment purposes, and Ping

et al.[25] comment on approaches and benefits of transition metal dichalcogenide (TMD) nanosheets for biosensors. Apart from that, some studies[25] discuss the importance of developing the inorganic substrates as systems that react to the presence of certain types of organic matter, which renders them programmable interfaces serving as sensor devices. Additionally, another example by Liu et al.[85] discusses how inorganic substrates can accommodate living cells and form a stable structure. Studying the examples from the Table 1, it is possible to conclude the fabrication methods are very different in its nature and there is a wide range of technologies that render the materials with “smart” behavior. For example, polymer contraction or casting, novel fabrication methods and solution formulation approaches along with creating mixtures and substances that respond to various stimuli allow to obtain “smart” structures with a controllable way and degree of response.[4] Here it is also necessary to acknowledge that the stability of a substrate in specific environments can play a crucial role in its biointerface functioning. The fabricated structures should have stable parameters over time under the designed conditions, thus making their behavior reliable and predictable. Typically there are various several available paths towards the creation of programmable materials: 1) as one-layer structure consisting of one or several materials and providing the reaction to external stimuli, 2) as a structure with two, three or even more layers, which is usually a combination of materials with different abilities. The structures consisting of several materials offer more ways to define their structure: the contents can be mixed, patterned, assembled layer- by-layer and many other combinations. This second approach allows to design very complex systems that may respond to a set of external stresses separately or simultaneously. It is important to discuss the potential biological performance of the reviewed materials since it will dictate any possible biotechnological applicability. This aspect can also be reviewed from two different angles. First, the strength of the external stimuli required to trigger the material’s response (i.e. current[70,79], magnetic field[55,56,67], and various irradiations[52,53,82]) can cause some unpreferable or even irreversible reactions observed in biological entities involved in the interactions with the “smart“ structures. Second, the degree of response from a programmable material may affect the cell attachment or cell survivability on the surfaces made of this material. For example, high surface potential, extreme surface contact angles, significant changes in surface chemistry may bring a high degree of stress in the influenced bio systems. At the same time,

changing some material’s parameters (for example pH[50,77]) from unpreferable to neutral or less severe will allow the bio-entity to prosper and express more stable behavior. Taking a look at the examples from Table 1, we can note that the modalities, materials and conceptions offered by various research groups may serve as promising candidates in the areas of bio-robotics, bio-sensing, in tissue engineering and bioimplantable devices. [49,55,67,73,80] Thus, one can conclude that organic and inorganic “smart“ materials play a significant role in modern biotechnologies and expanding the portfolio of technologies and approaches allowing to formulate new types of such materials will be highly beneficial. 1.3 Interactions of Microorganisms with Planar Inorganic Matter and Their Importance for a Programmable Biointerface Conception The following section and its subsections will describe and discuss the biointerfacial structures and their importance for developing new types of materials. Organic matter interfaced with various surfaces may express a set of responses typically triggered by the properties of organic or inorganic substrates used. Particularly, interactions of microorganisms with inorganic surfaces is in of interest. Based on published studies inorganic substrate/microorganism biointerfaces can serve as materials with programmable properties and numerous potential applications. 1.3.1 Biointerface as a Novel Type of Material The most generalized and basic definition of a biointerface available elsewhere[87] denotes it as a region of contact between a biological matter and a substrate. Important to add is that the substrate can be also organic or inorganic, and by biological matter one can assume any living organism or organic material. Figure 1.1 serves a pictorial representation of the conception of a biointerface.

Figure 1.1 General conception of a biointerfacial structure.

In terms of using it a “smart” material, we define a biointerface as a structure where an inorganic (or organic) surface makes a stable contact with a biological entity. As it was stated before, the biological matter in this structure (biointerface) can be represented by a list of candidates, and they can make a contact with a surface in several forms. For example, a biomolecule, living cell, microbe, virus, bacteria, yeast, fungi and others may be represented as a single entity (left side of the figure), as well as in a form of a tissue or a mature biofilm (right side). In general sense one can assume that the contact occurs between a biological object and a substrate, but there are multiple cases (as it can be seen further in the manuscript) when substrates can be functionalized. This scenario brings additional aspects in the interactions happening within the biointerface. Some agents may be used to provide better cell attachment, others express biocidal properties or, conversely, make the substrate neutral and protect the bio tissue from potential harmful behavior of some materials. The substrate-bio entity interactions may have different origins and each particular case should be considered. For example, a microorganism may experience direct interactions of its cell membrane and chemical agents released from the surface of a substrate material. Furthermore, the bulk of a substrate material may provide some effects triggering a slow passivation effect in a studied biointerface. Apart from that, stimulations (such as electro, radio, and magnetic) applied to the substrate material (or provided by it) can serve as factors influencing the properties of cells or biofilms. Furthermore, the reaction of a single cell may be different from a reaction of a resilient biofilm due to group sensing and stress response mechanisms typically developed in such communities. Additionally, the responses observed in different types of microbes (like Gram- positive or Gram-negative) or even among different strains of the same microorganism can vary significantly allowing virtually infinite set of “substrate/bio cell” combinations to be used for particular applications and conditions. As stated, a typical biointerface consists of at least two layers where the substrate can react to stimuli and can influence the biolayer. Thus, a combined reaction of the entire structure is observed, and this system becomes another layer of sophistication among next generation of responsive biomaterials. The “inorganic substrate-microorganism” biointerface can be viewed as an advanced programmable material that can be used in bioelectronic or biosensing applications. Furthermore, the findings from the Table 1 on conductive and semiconducting materials that may be formulated as micro or nano structures support the idea of using them for bioapplications. This

brings us to the next step which is to identify the components and possible programmable parameters of “smart” biointerfaces. 1.3.2 Inorganic Materials Interacting with Microorganisms Following the logic described in the construction of Table 1, first we try to identify the common trends and the set of most popular materials in the area of interfacing inorganic matter with different microorganisms. Table 2 presents a summary of representative findings of common inorganic substrate material interactions with microorganisms. These materials are not considered as “smart“, however their use and proper modification, and subsequent interfacing with microorganisms can lead to the creation of programmable biointerfaces. The physical parameters of a substrate as well as long-term stability of its structure define the behavior and even overall survivability of a biointerface. For example, the degradation of hydrogel-based structures or some polymers in physiological conditions as well as dissipation of certain silicon-based nanomaterials in biologic fluids compromises the development of their non- conventional applications. Supporting the idea of a stability importance, the majority of reviewed studies focus on bio and biomedical applications that are used to improve the durability and stability of implants or other devices in the context of human or animal tissue as well as the antibacterial and antifouling properties of these materials. Column 1 identifies the material(s) studied from a reference source. Column 2 comments on the type of the surface that was created. For example, the materials are commonly used in the form of thin films[84-98], nanopatterned fiber[88–90], nanopowder[91,92] or nanorod[93,94] coatings on various test substrates (i.e. glass, steel, and specific polymers). It is important to note, that in many cases thin films can experience further functionalization with antibacterial or chemical agents altering adherence, roughness or other properties of utilized substrate.[89,100,101,106-116] The typical fabrication methods used are: sol-gel coating[91,95–101], atomic layer deposition[102,103], sputtering[90,104], successive ionic layer adsorption reaction (SILAR)[105,106], and metalorganic chemical vapor deposition (MOCVD)[107–109]. Column 3 summarizes the types of microorganisms deposited on the substrates. Furthermore, additional factors like external stimuli (i.e. UV[84-86,90-93,96,97,102-105,107,108,111,113,117,119-123] or visible light[88,99,110,111]) may be introduced for the purpose of cleaning, changing material’s characteristics or boosting reactions happening within the biointerfaces. Column 4 summarizes applications and use of such external stimuli. Finally, column 5 represents a brief summary of the general purpose or reported potential application(s) based on the studies performed.

The primary goal of most of the research in the area of inorganic material/bacterial cell interfaces is to characterize materials with particular capabilities, such as antifouling or antibacterial properties (Table 2). At the same time, in certain cases the summary was broader, and a wide set of biointerface or microorganism characteristics were obtained.[100,112–117] The most common inorganic materials used in biointerfacial structures are metal oxides such as ZnO[84- 89,98,99,104-106,117] [90-97,100-103,107-113] and TiO2 . These materials have been demonstrated as effective antibacterial coatings. Thin films of ZnO or TiO2 on implants, such as hip or knee replacements, medical devices or hospital surfaces reduce the risk of contamination. The success of the coatings was verified by testing a range of microorganisms both prokaryotic and eukaryotic, which also represents a broad range of pathogenicity, biofilm formation capabilities, and cellular structure. The species tested include microbes associated with human infections from gram negative bacteria such as: Escherichia coli (E.coli)[40,84,85,87,88,90-94,99,102,104-110] Pseudomonas aeruginosa (P.aeruginosa)[88,89,118–121], Proteus vulgaris (P.vulgaris)[88,103,105,106], Acinetobacter baumannii (A.baumannii)[122], Klebsiella pneumoniae (K.pneumoniae)[106]; and gram positive bacteria such as: Bacillus subtilis (B.subtilis)[88,103,117], Staphylococcus aureus (S.aureus)[101,102,104–106,116,123–125] and members of the same family Staphylococcus epidermidis (S.epidermidis)[113,114] and Staphylococcus haemolyticus (S.haemolyticus)[105], the tooth decay causing bacterium, Streptococcus mutans (S.mutans)[126]. Other examples that can be found in the table include such bacteria as the gram negative mineralizing bacteria Shewanella putrefaciens (S.putrefaciens)[98], the skin microbiome gram positive bacterium Micrococcus lylae (M.lylae)[127], the industrially relevant gram negative bacterium, Streptomyces avidinii (S.avidinii)[107], the large gram positive soil bacterium, Bacillus megaterium (B.megaterium)[92], and the extremophile, radiation resistant gram-positive bacterium, Deinococcus radiodurans (D.radiodurans)[128,129]. Eukaryotic fungal microbes included in these studies were the agriculturally important “black mold” filamentous fungus Aspergillus niger fungi (A.niger)[103] and the fermentation industry cellular yeast, Saccharomyces cerevisiae (S.cerevisiae)[108]. This range of microbes represents not only a diversity of species and genomes but also other cellular properties such as the composition and mechanical properties of the cell wall. Microbial cell walls are structural and sensory components of these cells and play significant roles in the interfacial interactions. Furthermore, the characterization of the interactions between a substrate and microorganisms requires one to define the specific material properties of the surface and the

microbial response to these properties. For example, a direct correlation between surface roughness and cell attachment will play a key role in the development of new “smart” materials or devices that will interact with living tissues or biofilms. High degree of roughness allows bacterial strains such as S.aureus and P.aeruginosa to increase their adhesion rate in orders of magnitude.[130] Surface wettability, which is also related to roughness or functionalization of a surface, is also known to regulate bacterial cell attachment and influence antifouling properties of substrata.[131–134] In contrast, surfaces possessing suppressed electrostatic interactions, weaker van der Waals forces, and short-range interactions (e.g. Hydrogen-bonding) that were altered due to chemical composition changes and were harder to colonize and to form a mature biofilm.[135–138] In some cases, the application of a surface coating plays two roles, one being antimicrobial. Ti-based alloys with additions of silver (Ag) and gallium (Ga)[122], or aluminum (Al) and vanadium (V) (denoted as Ti-6Al-4V)[114] provided enhanced tissue adhesion and compatibility with mammalian cells, while demonstrated improved biocidal and mechanical properties due to their unique crystallographic and chemical structure[113]. This duality is challenging in that at a fundamental level all cells share the same components (i.e. lipids, protein and carbohydrate) and the interface needs to deal with particular aspects of two different cell types. As it was briefly discussed in sections 1.2.1 and 1.2.2 (Table 1), an alternative strategy is to use a set of materials that will be possessing a set of diverse characteristics that originate from contributing components. When two or more materials are combined in one structure, they may be assembled in many different ways that all can obviously have potential pros and cons. They will depend on particular method and configuration. Specifically, a biointerface consisting of several patterned materials having an optimum crosstalk with biological matter can express a diverse set of reactions and unique or untypical behavior. At the same time, having two materials in layer-by-layer configuration may sometimes cause the shielding effect and some expected responses may not be observed in the formulated biointerfacial structures. Thus, paying special attention to such aspects of the biointerface development, it is possible to create novel types of “smart” materials with an impressive set of characteristics. Most notable example of the already existing multimaterial substrates is the material composites class. Composites are getting more attention in biointerface applications because of the ability to incorporate interfacial properties more easily and directly. Composite materials such [125] as zinc-based titanium dioxide (Zn/TiO2) and palladium oxide/nitrogen-doped titanium dioxide

composite fiber (PdO/TiON)[111] are being used in many application which require catalytic or dynamic activation via light. An additional group of materials that show promise in programmable biointerfaces due to their utilization in bioelectronics and nascent biocompatibility are group III-V semiconductors, such as gallium nitride (GaN). GaN, AlGaN and InGaN, have shown to be biocompatible with a wide range of molecules and cell types including proteins[107], peptides[139] or even neural cells[140,141]. Recent work has provided considerable evidence that these III-V semiconductor materials such as GaN/AlGaN, interface with bacteria and yeast and have great potential applications in biosensing and bioelectronics.[43,107,108,142] Lastly, work with semiconducting materials such as silicon (Si)[115–117] has provided information on their biocompatibility, adhesion profiles, and bactericidal capabilities. Such studies complete the gap in the fundamental knowledge necessary to build and utilize “smart” bioelectronic devices. The focus of the remainder this review is to define a set of surface properties in inorganic substrates that may program and control the microbial behavior. By moving away from the current trend in biointerfaces that only examine biocompatible materials, we can identify more substrate-based material properties that stimulate microorganisms using the same response pathways that enable reactions to various external conditions and functionalizations. 1.3.3 Physiological Responses Within Biointerfacial Structures To work at higher level of complexity and to better identify factors influencing the programmable behavior of biointerfacial structures, we have classified the response of microbes to surface mediated external stimuli on biointerfaces into two categories: physiological responses (Table 3) and genetic responses (Table 4). These two response types are related, and their differences may be debated, but we wanted to distinguish between whole cell responses such as adhesion, proliferation or viability, which may involve multiple responses to a stimulus, or the culmination of the final output of a response to a stimulus. In contrast, a genetic response involves the activation of a singular signal transduction pathway or gene. Much of the work in this area has involved the former types of biological characterization, while we predict in the future with the large availability of genomic and proteomic tools more work in biointerfaces will involve characterizing the latter. We have identified four types of physiological responses that have been observed in studies of microbial biointerfaces: 1) adhesion or adsorption to a substrate, 2) cell proliferation, 3)

alteration to cell morphology or biofilm organization, and 4) transformations occurring in cellular secretion processes (Table 3, column 1). As with the studies in semiconductor/interfaces, a large collection of microbes has been examined, some of which are associated with human disease, while others are important for synthetic or industrial processing. It can be seen that E.coli[85,93,125,130,144-155], S.aureus[93,96,97,112,113,122,123,126,144,156-165], B.subtilis[117,147], P.aeruginosa[118,135,148–154] and S.epidermidis[113,114,147,152,155,156] are the representatives of pathogens and monosomal contaminants that serve as perfect model microorganisms for research purposes. Other examples include cultures of A.baumannii[122], S.putrefaciens[143,157], Pseudomonas fluorescens (P.fluorescens)[158,159], Streptococcus mutans (S.mutans)[126], Streptococcus thermophilus (S.thermophilus) and Streptoccocus waiu (S.waiu)[160], Proteus mirabilis (P.mirabilis)[161] and, finally, Bacillus anthracis (B.anthracis)[154]. Furthermore, some scientists focus on fungi[103], yeast[108] or algae[131], thus expanding the portfolio of studied species. In particular, one of these examples from Table 3 is Chlamydomonas reinhardtii (C.reinhardtii)[131] – a freshwater alga that was used as one of the representative organisms attaching to tungstite oxide (TO). In these studies, a diverse portfolio of the substrate materials has been used (Table 3, column 3). It includes polymers that have been applied in various forms: thin film coatings[147,148,151,153,156,162,163], brush-coatings[152], and self-assembled monolayers[164]. Notable polymers reported are: silicone[153,155,165], rubber[135], fluorinated ethylene propylene (FEP)[162], polypropylene (PP)[150], polystyrene (PS)[162,166,167], polyethylene (PE) and low-density polyethylene (LDPE)[162], polydimethylsiloxane (PDMS)[147,168], polyviny lchloride (PVC)[148], polyurethane (PU)[169], polysulfobetaine methacrylate (pSBMA)[169,170], polytetrafluoroethylene (PTFE)[166,167], polyethyleneglycol with b-cationic polycarbonate (PEG-b-PC)[153], polyethylene oxide (PEO) and polypropylene oxide (PPO)[152], poly-l-lysine[171]. Important to notice, that even though polymers like FEP, PP, PS are considered organic compounds, they may be used as functionalization agents providing better cell attachment or suppressing some reactive behavior of inorganic substrates discussed further. Metals, oxides and semiconductors have been used as planar, flat surfaces, or in complex composition either doped with nanoparticles, or formulated as alloys. The examples from the table include Al[135,160], Zn[160], Cu[160] , Ti[122,172], Fe[157],Si[115–117] [173] [135,159,160,174] [123,124,126,149] [112,143] and glass , stainless steel , TiO2 , ZnO , tungstite oxide (WO3 · [131] [122] [125] H2O) (denoted as TO) , Ti doped with Ag or Ga , TiO2 doped with Zn nanoparticles , ZnO

[112] [113,114] doped with Ag nanoparticles , Ti6Al4V alloy . Composites are represented by Ni-P-TiO2- [158] [175] PTFE , VO2/W . Lastly, some less common examples are represented by lead acetate–soaked [154] paper strips (Pb(Ac)2) for testing a reaction of bacteria to stress conditions , or biopolymer chitosan (CS) doped with Ga[156], used as a coating material. Accessing substrate surface properties and correlating them with physiological parameters of biofilms can serve as a starting point for the development of novel types of materials with “smart” characteristics or responses. For example, surface roughness and wettability may significantly alter the cell attachment rate. According to Balazs et al.[148], surface treatments decreasing a water contact angle of PVC substrate at 75%-90% allows to achieve a 70% reduction in bacterial adhesion of four different strains of P.aeruginosa bacteria. In addition, the nature and the magnitude of surface charge also alter bacterial adhesion and even influence the morphological structure of a biofilm: bacteria may form mushroom-like structures on antibiotic substrates possessing negative charge opposing to flat structures on positively-charged substrates.[135] Thus the initial characterization of substrates plays a significant role and needs to be considered while performing research activities. Another aspect of the reviewed studies that is overlooked and critical for compatibility, is the mode of delivery to a surface as any environmental/external condition may influence behavior, even after incubation on the surface. In most of these experiments, the microbial cells were delivered to the surface substrate via either direct contact with a droplet of cell suspension[111,115,124,165,172,174,176], or with solid nutrient media (such as agar), which is followed by a period of incubation[120,121,124,146,155,170] in order to provide a standardized and simple system for the application of microbes to an interface. However, these methods are in most cases a less than realistic presentation of microbial cells interactions with a substrate. Foremost, the number of cells per unit volume are extremely high and, in many cases, may display different behavior through mechanisms such as quorum sensing when compared to smaller groups of cells or even single cells. In deference to this, some studies have begun examining formation of a biofilm in a flow- chamber[113,152,159,163,177] or exposure of low levels of microorganisms dissolved in liquid media to the material in a culture tube[112,148,153,156,168,178]. These types of procedures are often performed in an incubator at optimal growth temperature, typically 36°C-38°C (for human related microbes, such as E.coli), to maximize the proliferation rate.[112,115,123,153,155,168,172] However, some groups are beginning to consider alternative culture conditions such as culturing microbe at room temperature

to test biointerfacial response under particular conditions[99,100,103,165,178,179]. The challenge of these endeavors is to design experiments that enable proper and careful characterization of the interfacial properties while not compromising the growth and viability of the microorganism. Furthermore, it is necessary to follow-up these experiments by treating a biointerface with cell viability assays followed by microscope examination and cell counting (quantifying the bactericidal properties of the material)[113,115,122,123,152,156,172]. Other approaches to maintaining this consistency involve the removal of cells from the material and amplify clonally[100,125,153,155,174,180], thus multiplying the quantity of cells that were influenced by intrinsic properties of utilized substrates. The above mentioned freshly grown cells can be isolated for further characterization and usage. Most of these examinations of physiological activities of microbes at interfaces involve surface modifications or an additional step of substrate preparation. Different surface treatments have been applied during performed experiments to change, activate, or add specific properties to the surfaces (Table 3, column 4). Utilized surface treatments include opsonization, in which a surface is functionalized with immunoproteins or antibodies. For instance, treating biofilms with immunoglobulin G resulted in multiple reactions including increased phagocytic ingestion and increased hydrophobicity.[162] Other modifications include: surface plasma treatment (which alters surface chemical composition, wettability and roughness)[148], antibiotic treatment (with antibiotics such as gentamicin, ampicillin, and nalidixic acid[154], heparin and hyaluronan[165], [155] [147] rifampicin, triclosan and trimethoprim ), treatments with gas (e.g. ozone (O3) ) and irradiation with UV light[113,114,118,119,126,147]. Also, polymeric coatings: PLL-g-PEG, PLL-g- PEG/PEG-RGD, PLL-g-PEG/PEG-RDG[123], octadecyltrichlorosilane (ODTS)[165] that offer antifouling capabilities were implemented by researchers. Important to note, that some treatments may be used for routine surface preparation, while others change the initial material significantly and, sometimes, irreversibly. In these cases, the biological entity will be interacting with the modified materials and will express new (or additional) reactions. Such situations should be carefully considered by the researchers since they add another degree of freedom (and, obviously, bring some sophistication) into biointerfacial studies. Additionally, most of the work that involves physiological alteration of microbial behavior has not been used in studies of biointerfaces with semiconductor materials, which often have unique surface properties such as charge and compositional differences.

The predominant type of materials used in reviewed studies are various polymer films, glass, steels, and alloys. Investigating adhesion, proliferation, secretion, and morphology of biofilms formed on semiconducting materials (such as described in Table 2) becomes a very important research topic for scientists who tailor inorganic materials and their potential biological implementations. At the same time, the portfolio of substrates is diverse, and areas of their possible applications are very broad, so it is hard to simply correlate surface parameters with physiological properties of microorganisms adhered. For example, hydrophobic and hydrophilic surfaces may express opposite trends in colony proliferation under different environments[181] However, it is possible to identify some trends of using surface treatments of various substrates to form zwitterionic coatings that will suppress a bacterial adhesion and at the same time allow attachment of certain necessary cells like osteoblasts or fibroblasts. Also, a perfect example of programmable substrate for biointerfaces is combining several materials as a single compound and activating the generation of reactive oxygen species. This can be achieved via various types of photocatalysts that alter morphology and adhesion of biofilms and can be classified as another popular way of substrate formulation for biological applications. In some studies, external stimuli have been used to directly influence the biofilm on the interface, thus exploring the reaction of microorganisms placed on a substrate under changing external conditions. For example, a set of studies uses low or high temperature[124,131,150], visible [110] [154,167,180] light , metal salts (like Ga(NO3)3 and FeCl3) , or chemicals and surfactants (glutaraldehyde, ortho-phthalaldehyde, cetyltrimethylammonium bromide, benzalkonium chloride, sodium dodecyl sulphate, sodium hydroxide, sodium hypochlorite)[159] dissolved in the media, as factors activating processes of biofilm morphology and growth rate alterations. The goal of these surface treatments is to develop new ways to control cells and specifically microbial behavior. Towards these ends, surface treatments have led to the development of novel materials (Table 3, column 5). In many cases changing the abiotic component of the interface resulted in some sort of physiological response from the microbe, i.e. changes to adhesion, proliferation, degradation profiles, antibacterial behavior, or changes in morphological structure of biofilms. Particularly, authors quantified attachment and growth rate of biofilms on various materials[85,97,98,108,113,122,123,125-127,144-148,154,156,166,167,172,173], antifouling and antibacterial capabilities of substrates and surface treatments[93,96,97,108,113,128-130,137,149-153,158,159,161-164,168,171], and biofilm morphology alterations[117,135,143,150,156,164,182]. As for internal changes in microorganisms,

researchers have been able to access secretion modifications: decreased or increased production of metal ions in signaling systems (Ss)[157,166,167], changes in virulence of studied cultures[167] or even [154] ability to produce gas (H2S) to resist antibiotic treatment . Furthermore, some sources from the secretion group[157,166,167] also comment on the operation of signaling systems, which are related to the topic discussed later in the Table 4 (genetic responses produced by microorganisms). 1.3.4 Genetic Responses in Biological Systems The area that holds the most promise for characterizing the microbe/substrate interaction at the cellular levels is the identification of specific genetic responses of a microorganism to a particular material property (Table 4). Microbes have evolved a complex network of signal transduction and transcriptional responses to many environmental stimulus and new material properties of a substrate will be obviated by the identification of specific cellular responses that are activated by these properties. All microbes use signal transduction systems that are based on several protein/enzyme based cellular components: a membrane-based receptor, intracellular transduction enzymes, and transcription factor that triggers modification of gene expression.[184–186] Genetic response of microorganisms works with cellular regulatory systems to alter and control cellular and physiological processes. In bacteria, many of the stress response mechanisms involve a two- component signaling system (TCSs) in which specific external factors (e.g. ROS, heavy metals, pH) activate a membrane-bound receptor which triggers an intracellular enzymatic pathway that results in the expression of specific response target genes. Crosstalk between various TCSs coordinate stress responses from multiple stresses which provides the cell an internal mechanism to adapt to constantly changing variety of environmental conditions.[184,187,188] In addition to responses to environmental stimuli such as pH or ROS, bacteria and other microbes have evolved intercellular signaling mechanisms that enable communication and coordination of individual cells within a population of microbes or biofilm. This group- developed cell behavior in biofilms is called quorum sensing. In the context of biointerfaces as programmable materials, this can be considered another external stimulus that indirectly arises from a substrate/cell interaction. Microorganisms utilize the secretion of small analytes to share information of external stress conditions with a large group of cells thereby coordinating a collective biofilm response. In particular, gram-negative bacteria use autoinducer agents and genes alteration to initiate the reaction across neighboring cells.[185,186] How this impacts biointerfaces is

unclear, but given that quorum sensing is a function of cell number and density, during the characterization of material/bacteria interaction the cell culturing conditions and age must be considered as an external factor when examining microbial reactions at biointerfaces. In addition to the acute responses via signal transduction, long term adaptation to chronic environmental stresses will result in permanent changes to microbe genomes and the accumulation and selection of advantageous mutations that can significantly change concentration or structure of master regulators within a cell and start a population of new, modified generations of cells.[184,189] The genetic response to biointerfaces provides the highest resolution tool for defining the microbe/substrate interaction and will bring biointerface research to the next level because one will be examining specific gene/properties interactions. This information is predictive and allows controlled design of materials that elicit specific and desirable microbial responses to a surface. It will allow one to coordinate external stresses or continuous use of certain operating conditions that may affect the populations of cells forming a biofilm, which will finally provide information critical for a number of industries from biomedical device design to fermentation. This knowledge has the potential to aid in the development of more advanced types of “smart” biointerfaces and improve their potential long-term performance. Table 4 summarizes our findings from papers related to a genetic response that is caused by external stress (denoted as “stress” in the table) and from research related to a two-component signaling system in microorganisms (“TCSs”, as it is abbreviated in the table). The first column identifies the main purpose (or focus) of the studies, which can be stress-related study, or TCSs study, as it was stated before. General trends are such, that stress papers utilize various agents to see the changes in gene expressions, growth or morphology profiles. The majority of TCSs papers do multiple genetic modifications within a cell to record how these perturbations can cause further gene concentration reactions, thus allowing to identify particular genetic components of analyzed signaling systems. All genetic/stress responses to a surface interaction will have some connection to a change in physiology. Many microbial cells will respond genetically to environmental stress by changing their overall morphological appearance, such as presence or absence of pili, fimbria, shape, or attachment rate alterations. We have grouped the papers in the table according to the type of stress. As with the physiological examination of microbial interfacial reactions, genetic responses, target microorganisms are either grown in a liquid media (nutrient broth) and incubated in cell culture

tubes, flasks or culturing chambers[190–200], or grown in Petri dishes on solid nutrient media like agar[128,201–206]. Given the sensitivity of genetic stress responses, the diversity of these culturing conditions could be a cause for concern for consistency. Based on our literature analysis, relatively small amount of published papers have examined the microbial response to metallic, semiconducting or polymeric substrates and in many cases, cells do not interact with inorganic materials at all, since external stresses may be represented by temperature, pH changes or by gene removal or implantation. Some experiments of genetic responses conducted in homogeneous solutions do not involve the creation of biointerfaces. However, they are important for understanding the processes occurring within the cell and thus can be considered as additional parameters and conditions that should be controlled while formulating the biointerfaces. These types of work are still valid for the purpose of our review since they give an overview of the toolset available for genetic studies in microorganisms. Such tools may be used in multiple manipulations with external structure of cells in various interdisciplinary studies. Six general classes of stresses were identified in these studies (Table 4, column 2). The types of stresses include: 1) environmental stresses, i.e. change in pH, temperature, and similar factors, 2) oxidative stresses, i.e. disbalance between free radical concentration and organism’s ability to neutralize them; 3) osmotic stresses, i.e. intracellular solute imbalance; 4) ionizing stress, i.e. stresses caused by various types of radiation; 5) a stress-response to heavy metal salts dissolved in media, and, 6) mechanical or chemical stress disturbing or disrupting the microbial cell wall and affecting a cell membrane, denoted as cell envelope stress in the table. The primary tools for genetic analysis involve direct manipulation of the genome through either deletion, insertion, or mutation of specific genes to access the signaling systems operation. Similar to previous sections of this review, a wide range of microorganisms has been studied, and, matching the previous tables, E.coli[136,178,186-188,191,194,196-199,203-221], P.aeruginosa[197,205,207–213], B.subtilis[206,214–216] and S.aurelis[190,206,217] are the most occurring bacterial species (Table 4, column 3), which provides both the identification of novel stress mechanisms that are associated with specific microbes as well as helping decipher ancient stress response mechanisms that are shared by many related microbes. Furthermore, Pseudomonas putida (P.putida)[200], D.radiodurans[128,129], S.pneumoniae[204,206], Caulobacter crescentus (C.crescentus)[218,219], and A.baumannii[198], Mycobacterium tuberculosis (M.tuberculosis)[195], Shigella sonnei (S.sonnei)[220], Streptococcus suis (S.suis)[221], Chlamydia caviae (C.caviae)[222],

Mycobacterium marinum (M.marinum)[223], Helicobacter pylori (H.pylori)[224] were among other microorganisms used for studying the TCSs and mapping their genetic responses. The generation of the stress is also critical for characterizing these TCS based stress response (Table 4, column 4). The stress agents or factors used to induce stress were diverse. Environmental manipulations such as simple temperature increase or decrease and variations in pH-level[197,209,225–227], forced aerobic or anaerobic conditions for culture growing in media[224,228], or media lacking nutrients and causing microorganisms to enter a starvation mode[224]. To introduce the changes in external parameters (temperature, pH level and respiration conditions), the researchers were tweaking parameters of biofilm incubation in liquid media thus mimicking changes that could happen with cultures in the real-life conditions. By simply increasing incubation temperature or pH level, it is possible to observe an increase in concentration of enzymes and compounds closely associated with TCSs and intercellular salt overly sensitive (SOS) signaling systems in biofilms.[225,227] For example, E.coli expressed a large production and accumulation of indole (intercellular signaling compound) at temperatures around 50°C and at pH levels of 5-9.[226] Changing growth conditions from aerobic to anaerobic, it is possible to observe an increased concentration of certain membrane lipids in H.pylori, which suggests an activation of particular regulatory systems associated with cell metabolism and growth.[224] Additionally, biofilm density can also cause stress to the culture grown, according to findings reported in this mini-review.[206] Thus, even variation of environmental conditions may trigger intercellular signaling and regulatory systems that are associated with further physiological reactions and significantly affect properties of the biointerfaces created. Furthermore, reactive oxygen species (ROS), as oxidative stress-agents, while being generated on a substrate or dissolved in media, may cause toxic effects significantly affecting the signaling processes in a cell, be a source of severe damage to its structure, and cause mutations in both growing and stationary-phase cells.[188] To [190,197,207,209,215,217,223,229] induce an oxidative stress, researchers utilize hydrogen peroxide (H2O2) , or superoxide (O2-), that may be generated on CdSe or CdTe quantum dots under illumination with light[192], or by using chemical compounds like paraquat – N,N′-dimethyl-4,4′-bipyridinium dichloride[230]. No less important is an osmoregulation within a cell envelope. By varying the concentrations of salts in the surrounding environment, it is possible to control the amount of water that goes out or in the cell which will ultimately affect the production of proteins associated with signaling systems and the secretion processes related to cell survival. Typically, salts like

NaCl[199,202,231–233] cause substantial changes in osmosis of microorganisms and serve as an agent of osmotic stress. Another stress-factor commonly applied in the microorganism studies are ions of heavy metals: cadmium, zinc, cobalt, copper, nickel and others, that can be found in the test media or, for example, in soil.[197] The heavy metals accumulated in cells affect the intracellular processes which result in irreversible damage to DNA, disrupt the signaling within a cell and other effects that can be considered genetic response or, in other words, mutation. In many cases to release ions such as Cu+, Cd2+, Zn2+, Mg2+ into the biological systems, the protocols require adding salts to the media where cells are going to be incubated. The reagents used in reviewed papers [193,203] [234] [197,200] [197] [214,235] were CuSO4 , CuCl2 , CdSO4 , ZnCl2 , and MgSO4 . An alternative way to cause some direct or indirect influence on genetic structure of an organism is ionizing radiation. The radiation can affect the cell directly, by altering the proteins forming a genetic structure, or indirectly, when clusters of reactive species formed and, by interacting with the cell, cause the further genetic reactions.[236] The ionizing stress can occur by various types of radiation: α- waves[236], γ-waves[128,129,237] and terahertz (THz)-frequency radiation[201]. To protect the stability of the molecular structure and ensure successful survival of a microorganism, a significant amount of resources is spent to form and maintain the integrity of the cell envelope.[238] Thus, any means compromising rigidness of outer cell wall and disturbing or disrupting the stability of the cell membrane will carry cell-envelope stresses, triggering a cascade of severe physiological, morphological and genetic reactions that can even affect cell viability, virulence and antimicrobial susceptibility.[186,238,239] The cell-envelope stress factors are membrane-active agents: antibiotics (like polymyxin B)[208], disinfectants (such as biocide chlorhexidine, ethanol, sodium dodecyl sulfate, chemicals compounds (ethylenediaminetetraacetic acid, p-xylene, n-hexane), and various cationic antimicrobial peptides (like melittin, alexidine, poly(hexamethylenebiguanide) hydrochloride and cetrimide)[210,235]. Moreover, apart from adaptive mutations mainly caused by stress factors (so called stress-induced mutation mechanism[188]) discussed previously, many researchers take an approach of direct genetic manipulation.[240–243] As stated above, the connection between the physiological response and the genetic response needs to be made, and genetic analysis enables this through the direct or indirect examination of genes that have been affected by the processes occurring within a cell. In particular, authors correlate genetic mutations under external stress or genetic modifications (like gene knockouts, implantation of foreign genes) with physiological parameters such as activity of

secretion systems and cell viability[191,205,211,212,225–227,244], lipid concentrations[224], turgor pressure (a force within a cell pushing against the cell wall) profile[199] and, lastly, morphological changes[191,205,211,212,224,244] observed in studied microorganisms. Furthermore, the purpose of many studies was to identify what TCSs are responsible for microorganisms’ ability to survive.[131,132,192,193,195,197,200,212,214,219-221,223,229,235,246] In this case genetic reactions were correlated with viability, cell attachment rate and other important parameters. However, for many TCSs studies a slightly different approach was employed: by performing genetic manipulations and modifications, researchers were identifying the components of signaling systems, trying to find a correlation between damage to one gene and potentially affected activity of another gene.[195,196,216,218–220,245,246] Also, a significant portion of reviewed papers retrieve activity profiles of genes under external stresses, which can be a set of lowered or increased concentrations of proteins associated with studied genes.[193,197,202,203,206,209,228,232,234] Similarly, some works comment on particular results of genes experiencing downregulation (decrease in activity)[210,235,247], upregulation (increased genetic activity under external stress)[190,192,215,217,223,229,230] or process of stimulation of gene activity (induction)[190,217]. To provide a representative example on two different approaches, a paper by Kawai et al.[214] defined as “signaling systems responsible for survivability” utilizes magnesium salt

(MgSO4) to introduce alterations in various mutants of B.subtilis and how the survival rate varies from one strain to another, thus making a conclusion regarding what genes are responsible for 2+ [193] Mg tolerance. In contrast, Yamamoto et al. by utilizing copper sulfate (CuSO4) as a stress factor reported gene transcription to access protein concentrations associated with copper intake, and further focused on the correlation between gene activity, copper concentration and accompanied genetic reactions in related TCSs. However, both approaches allow to access information about alteration of genetic structure and survivability associated with particular genes of microorganisms under stress conditions. Knowing what genetic systems are involved in these processes and what are possible ways of predicting or controlling them, one can introduce another layer of control for biointerface parameters required for particular applications. Researchers have intensively investigated the structure of genetic responses in microorganisms for decades and multiple genetics studies have been performed on microorganisms in swimming planktonic form. Along with summarizing the experience of these studies, some more research should be done on how genetics of microorganisms formed on

inorganic substrates. Such work will enable the development of better understanding of processes that may take place at biointerfaces, since some parameters of materials may serve as a cause of genetic reactions that take place in cells (a certain indicator of this are various physiological responses that were reviewed in Table 3 previously). Furthermore, determining potential ways to architect the living organisms as a part of a biointerface by identifying these “genetic triggers”, it may be possible to add another layer of sophistication in the above-mentioned systems. Such knowledge plays a significant role in the process of development of devices like advanced biosensors since engineers need to consider what factors apart from morphology and biofilm viability can be used as an indicator of induced stress in the programmable biointerface system. 1.4 Conclusions Working at the cross-section of different disciplines brings many advantages and much appreciation that processes occurring in studied objects may originate from a wide spectrum of physical, chemical, or biological effects and phenomena. Biointerfaces by their nature are very complex materials and require broad expertise to work with. These “smart” biosystems are constructed as several-layered structures and may express a wide range of reactions and responses. In a biointerface, substrates and biofilms exist in conjugation and both components influence each other simultaneously: the changes in substrate properties can certainly affect microorganisms, and microorganisms can change the surface chemistry of a substrate while attempting to respond to the external stress. Making efforts to engineer and define such adaptable behavior of the advanced structures presents certain research challenges. They need to be addressed and some propositions may be offered. We classified the reviewed sources into four tables and defined three major groups of research topics (inorganic substrates, physiological and genetic reactions of microorganisms) important for discussing the nature of biointerfaces as programmable materials. Now it is possible to summarize major conclusions and highlight certain trends that have been identified. The first category is represented by the papers discussing particular material or a group of materials interacting with bacteria, yeast or fungi. Typically, a wide set of surface characterizations are performed in order to access substrate’s properties before and after being in contact with living matter. Additionally, biofilms or single cells may be tested for quantifying possible morphological and (or) physiological responses. This type of research is usually performed by scientists working in materials science-related fields and the main idea is to fabricate a material or various coating and explore their properties and potential practical applications. Another example may be a set of

papers primarily focusing on physiological or morphological responses of biolayers interacting with substrates or powdered materials that are being affected by changing external conditions. These research works have a biological focus and sometimes deemphasize materials and their properties. The last major class of papers are genetics-driven and, in many cases, do not use materials at all and, obviously, comments on a broad range of genetic, physiological or morphological parameters of studied microorganisms. Practically, this third type of research is represented by papers that only relate to bioscience as an initial assessment. Naturally, the possibility of combining all three approaches opens new paths for research improvement. Since materials properties, external conditions, physiological and genetic responses are linked, a clear and straightforward connection between substrate characteristics and reactions occurring within biofilms needs to be made. Thus, to understand the behavior and characteristics of a biointerface, it is necessary to perform characterization of a substrate material and access the morphological and physiological properties of a biolayer together, at once. This is critically important for clearly defining the adaptable nature of the biointerface operation. Furthermore, to verify long-term stability of such systems, it is necessary to map the genetics of the biofilm. This permits to observe what signaling systems are involved in the reaction, what mutations took or may take place and other types of genetic responses might be experienced by the biointerface in the future. With respect to potential practical applications of such approaches, one should think of materials and cultures that might be used to construct novel types of “smart” biointerfaces. Particularly, due to unique electronic properties and proven biocompatibility, certain semiconductor surfaces[250,251] are promising for the formation of interfaces with organic tissue and various types of microorganisms. Also, for our research, Gallium-based semiconducting compounds like GaN and AlGaN are promising for the formation of interfaces with organic tissue and various types of microorganisms. In summary, it is necessary to put together collaborative efforts in designing programmable materials based on microorganism-semiconductor biointerfaces. Doing so will enable combining the aforementioned approaches in order to perform extensive explorations into the creation and utilization of advanced materials and adaptable structures targeting applications in bio-implantable electronics, biosensing and bioinformatics.

Table 1. Representative examples of smart materials and their potential application areas Type of material Material features Stimuli Projected applications Ref. # Au nanorods DOX toxicity, fluorescence photothermal excitation bioimaging, drug delivery 63 * † - * † * † (dextran, DMAEMA , Si ) MPs selective release of agents glucose, ROS (O2 , H2O2), pH change , PBS drug delivery 49, 50 , 51 * † * † (BN , WS2 ) QDs photoluminescence vis., UV light bioimaging 52 , 53

MoS2+WS2 QDs catalytic abilities, fluorescence H2SO4 bioimaging 54 Au electrodes+NPs biomolecule release electric current, water drug delivery 48 soft robotics, fluid polymer (PDMS*, polyolefin†) flexibility (folding, contraction) magnetic field, vis.light 55*, 56† transport† (α*, β†)-keratin biopolymer shape memory effect water shape memory materials 57*, 58† TixTa1-xSyOz nanosheets photothermal properties NIR irradiation drug delivery, bioimaging 82 polymer (PCL/HDI/BD)*, sensors, actuators, † * † shape memory effect temp. change*,‡,⸸,electric current† 66 , 69 , 70 , polymer+(C†, Ga‡, Pt⸸) nanofiber bioelectronics, soft robotics‡ 71 ‡,72⸸ copolymer-based hydrogels swelling temp. change drug-delivery, bioimaging* 59*, 60 PEG-based hydrogels, chondroitin disintegration/self-healing ultrasound, water*,† biomaterials, drug delivery 61, 62*, 64 sulfate hydrogel* salt-PEG hydrogels electrical activity (conductivity) mechanical stress soft robotics, bioelectronics 80 chitosan/copolymer hydrogel self-folding water drug delivery 63 polymer+silica/Fe₃O₄ NPs composite shape memory effect magnetic field, temp. change soft robotics, bioelectronics 73 * † † * * (MoS2 , MoS2/CN ) composite catalytic abilities vis.light, H2SO4 renewable energy storage 74 , 75 , 76

TiO2/WS2 composite photocatalytic abilities vis. light catalysis systems 81 resin-Fe composite flexibility (curving, bending) magnetic field soft robotics, drug delivery 67 PGMA/Au composite 2D folds into 3D solvent (CH₄O), salts soft robotics, sensors 78 PCBs/Rh composite shape memory effect ultrasound cavitation (in liquid) sensing/switching systems 68 pNIPAM/CN composite self-folding, tunable response NIR irradiation, temp. change bioelectronics, tissue engr. 83 polymer (PMDS)+bio.cells hybrid bending, change of curvature electric current soft robotic systems 79 DNA-based hydrogels shape-memory effect*, swelling pH change*, enzymes† drug-delivery 77*, 86† Note: NPs - nanoparticles, MPs - microparticles, QDs - quantum dots. Reference sources marked with *, †,‡, ⸸ primarily utilize materials, stimuli and/or have potential applications marked with these special characters.

Table 2. Semiconducting materials used for bioapplications Material Surface type Target microorganism Stimuli Targeted response Ref. # ZnO thin film E.coli UV light antibac., properties characterization 100, 112 ZnO thin film S.aureus UV light antibacterial 102 ZnO thin film E.coli, S.aureus, S.putrefaciens* no antibacterial 101, 104, 143* ZnO thin film (functionalized) E.coli, S.aureus, P.vulgaris, K.pneumoniae no antibacterial 106 ZnO thin film B.subtilis, A.niger UV light antibac., properties characterization 103 ZnO thin film (doped) S.aureus, P.vulgaris, S.haemolyticus no antibacterial 105 ZnO patterned nanorods E.coli UV light antibacterial 93,94 ZnO patterned nanocomposite (doped) E.coli, P.aeruginosa, B.subtilis, P.vulgaris vis. light antibacterial 88 * * TiO2 thin film E.coli UV, vis. light antibacterial 96, (99, 110) , 119, 144, 145 * * * TiO2 thin film (functionalized) E.coli UV, no antibacterial 95 , 97, 98 , 131

TiO2 thin film (functionalized) M.lylae UV light antibacterial 127

* * TiO2 nanomebrane (functionalized) E.coli, P.aeruginosa no biosensing 89 , 90 * * TiO2 thin film P.aeruginosa, S.mutans UV light antibacterial 118,119,126 * * TiO2 nanopowder coating E.coli , B.megaterium UV light antibacterial 91, 92

* * TiO2 thin film (functionalized) S.aureus no, UV antifouling 123,124 GaN thin film (functionalized) D.radiodurans no biosensing 142 GaN thin film (functionalized) E.coli no biosensing, selective functionalization 139 GaN thin film (functionalized) S.cerevisiae no biocompatibility 108 GaN thin film P.aeruginosa, E.coli* UV light biocompatibility 120, 121, 146* AlGaN, GaN thin film (functionalized) S.avidinii no biosensing 107 Ti-6Al-4V alloy S.aureus, S.epidermidis UV light adhesion profile, cell behavior 113, 114 Ti/Ti-Ag/Ti-Ga alloy A.baumannii no antibacterial 122 PdO/TiON composite fiber E.coli vis. light antifouling, antibacterial 111

Zn/TiO2 composite E.coli, S.aurelis no antibacterial 125 Si wafer E.coli, S.aureus*, B.subtilis† no adhesion profile 115, 116*, 117† Note: reference sources marked with*, † utilize primarily (only) target microorganisms and/or stimuli marked with these special characters.

Table 3. Classification of physiological responses expressed in biointerfacial structures Study type Target microorganism Substrate material Surface treatment Response observed Ref. # adhesion E.coli,⸸, S.aureus, S.epidermidis polymers (FEP, PS, PE), silicone* opsonization, antibiotic* cell attachment profile 155*, 162⸸

adhesion E.coli, B.subtilis, S.epidermidis polymer (PDMS) UV, O3 growth profile 147 * † ‡ *,† ‡ * † ‡ * adhesion E.coli , S.aureus , P.aeruginosa Si , TiO2 - growth, attachment profile 115 , 116 , 149 , 173 adhesion E.coli, C.reinhardtii TO UV, high temp. antifouling, attachment profile 131 adhesion S.aureus, S.epidermidis alloy (Ti6Al4V) UV cell attachment profile 113, 114 adhesion S.thermophilus, S.waiu metals (steel, Al, Zn, Cu) - growth, attachment profile 160 adhesion P.aeruginosa polymer (PVC) surface plasma cell attachment profile 148 * * * * adhesion S.aureus, E.coli TiO2, silicone monomolecular coatings antifouling profile 123, 165 , 176, 179

adhesion P.fluorescens Ni-P-TiO2-PTFE - antifouling, antibacterial 158 proliferation E.coli polymers (PU, PDMS*, polylysine†) - antibacterial, antifouling 163†, 168*, 169, 171†,183* proliferation E.coli ZnO, ZnO+Ag, steel* - growth profile, antibacterial 112, 174* proliferation E.coli, S.aureus, P.mirabilis, P.aeruginosa* polymers (pSBMA, PS*) - antibacterial, antifouling 151, 170* ,⸸ x * † †,x *,⸸ proliferation S.aureus, S.epidermidis* , E.coli VO2/W, CS+Ga , TiO2+Zn - antibacterial 125 , 156 , 175 proliferation S.aureus*, S.epidermidis, P.aeruginosa polymers (PEO, PPO, PEG-b-PC*) - antibacterial, antifouling 152, 153* * * * proliferation S.aureus, S.mutans TiO2 low temp., UV antibacterial, growth profile 124, 126 , 149 proliferation A.baumannii, S.aureus* Ti, Ti+Ag, Ti+Ga - antibacterial properties 122, 172* morphology E.coli, B.subtilis* PDMS, polymer SAMS†, Si* - growth, morphology profile 117*, 164†, 182 * * morphology S.aureus, E.coli, P.aeruginosa TiO2 UV antibacterial properties 118 , 119 morphology S.aureus, S.epidermidis CS+Ga - biofilm degradation profile 156 morphology P.aeruginosa steel, Al, rubber -, high temp.* antifouling, altered structure 135, 150* morphology S.putrefaciens ZnO - morphology, attachment prof. 143

secretion E.coli, S.aureus, B.anthracis, P.aeruginosa Pb(Ac)2-soaked paper strip metal salts, antibiotic H2S concentration alterations 154 secretion P.fluorescens steel surfactants biofilm growth and secretion 159 secretion S.putrefaciens Fe(III) - Fe-based Ss operation change 157 secretion P.aeruginosa polymers (PS,PTFE) metal salts, -* virulence, signaling operation 166*, 167 Note: references marked with *, †, ‡ utilize primarily (only) target microorganisms/substrate materials/surface treatments marked with these special characters. References noted as ⸸, x have all listed and include target microorganisms/substrate materials/surface treatments marked accordingly.

Table 4. Genetic response studies performed on microorganisms Study focus Type of stress Target microorganism Stress agent Response type Ref. # stress environmental E.coli temp. fluctuations, pH* secretion profile/viability 180, 225, 226, 227* stress environmental E.coli aerobic/anaerobic gene activity profile 228 stress environmental B.subtilis, S.aureus, S.pneumoniae biofilm density gene activity profile 206 stress environmental H.pylori aerobic/anaerobic, starvation lipid conc., morphology profile 224 * * stress oxidative E.coli O2- , H2O2 gene upregulation 192, 229, 230

* † * † stress oxidative S.aureus, E.coli, B.subtilis , M.marinum H2O2 gene induction/upregulation 190, 215 , 217, 223

stress oxidative P.aeruginosa H2O2, temp. fluctuations gene activity profile 197, 207, 209 stress osmotic E.coli NaCl gene activity profile 202, 231, 232 stress osmotic E.coli NaCl turgor pressure profile 199 stress osmotic P.aeruginosa NaCl Ss for survivability 233 stress heavy metals E.coli Cu gene activity profile 193, 203, 234 stress heavy metals E.coli*, B.subtilis Mg Ss for survivability 214, 235* stress heavy metals P.putida, P.aeruginosa* Cd, (Zn, Co, Cu, Ni)⸸ Ss for survivability 197*,⸸, 200 stress ionizing D.radiodurans, E.coli* γ-radiation Ss for survivability 128, 129, 237* stress ionizing E.coli THz*, α-radiation Ss for survivability 201*, 236 stress cell envelope E.coli, P.aeruginosa* membrane-active agents gene up/downregulation 210*, 235 stress cell envelope P.aeruginosa antibiotic Ss for survivability 208 stress* - E.coli, P.aeruginosa* gene disruption/insertion morphology, viability profile 191, (205, 211-213)*, 244 TCSs - E.coli gene disruption/insertion Ss identification/survivability 196, 224, 245-249 TCSs - B.subtilis, M.tuberculosis* gene inactivation Ss identification 195*, 216 TCSs - C.crescentus, S.sonnei* gene disruption/insertion Ss identification 218, 219, 220* TCSs - S.pneumoniae, S.suis*, C.caviae† gene inactivation Ss for virulence/survivability 204, 221*, 222† TCSs - A.baumannii gene disruption/insertion Ss for survivability 198 Note: reference sources marked with *, †, utilize primarily (only) target microorganisms, and/or stress agents marked with these special characters. Reference sources marked with ⸸ utilize all listed target microorganisms and stress agents including stress agents marked with this special character.

CHAPTER 2

Characterization of Pseudomonas Aeruginosa Films on Different Inorganic Surfaces Before

and After UV Light Exposure

2.1 Introduction Modern computing technology is approaching limits of exponential development and various paradigms to overcome existing limitations have been proposed, including quantum computing and natural (bio-inspired) computing[252],[253,254]. Biocomputing is a novel branch in the unconventional computing area, relying upon the ability of biological systems to cope with and adapt to changing environmental conditions. Across many available biological organisms and systems (ant colony algorithms, neural cell networks, immune systems, genes, viruses), bacteria show great promise in biocomputing due to their unique combination of being a relatively simple organism with a complex genome that responds to and survives in a broad range of environmental conditions[253,255,256]. Recent studies have shown that bacteria are capable of sensing wide range of stimuli and provide chemical and physical responses[256–258]. These properties open paths for bacteria to be employed in information storage platforms. For example, there are reports on the potential use of certain strains of bacteria as memory devices, logic networks and gates, genetic transistors (biological analog of semiconductor transistor), genetic circuits, as well as biocomputational devices[252,253,259]. In addition, certain bacteria have been utilized as biosensors, represented as interfaces responding to certain types of input signals or conditions[256,260]. All the factors and approaches make bacteria a perfect candidate for use in non-conventional biological computing systems or, to be more particular, creates a separate direction of research – bacterial computing. Furthermore, the search for concepts of bacterial computing systems that can be used in certain areas with specific or extreme conditions requires studying the surface interactions of bacteria with technologically relevant inorganic materials in response to specific stimuli. Thus, gained knowledge and developed approaches can be applied to novel bioelectronic interfaces. Pseudomonas aeruginosa is a Biosafety Level 2 (BSL-2) bacteria, a typical representative of in-hospital infections targeting people with depressed immune system. P.aeruginosa becomes problematic for antimicrobial treatment due to its inherent resistance to many antibiotics, capability to rapidly acquire strong resistance to adverse external conditions and ability to develop

drug resistance using diverse mutations mechanisms[261–264]. It has been reported that P.aeruginosa is capable of forming tough and resilient biofilms on various substrates[257,265,266]. Furthermore, by transitioning from planktonic to biofilm form, P.aeruginosa significantly increases its survivability[267,268]. As Gram-negative bacteria, P.aeruginosa possesses adhesive pili that are involved in cell adhesion processes, biofilm formation and bacteriophage adsorption[269,270]. All these specific properties related to its ability to survive and adapt to variable specific environments make P.aeruginosa a perfect candidate for potential use in biological computing where stability of the system under aggressive external conditions is one of the desired properties. Development of biointerfaces with stable and predictable intrinsic properties requires understanding the mechanisms of their formation along with parameters that can be affected by external stimuli. Recent studies discuss P.aeruginosa biofilms and their attributes on certain substrates. However, there is lack of fundamental knowledge to systematically characterize the formed biofilms on various substrates relevant to electronics application, particularly under different conditions.

In this study we compare films of P.aeruginosa formed on Au, GaN, and SiOx surfaces. Chemical and topographical parameters of the inorganic materials, known to be affecting the nucleation of biofilms, were investigated. Bacterial films were formed on the inorganic surfaces before and after the material (not the bacteria) were exposed to UV light (365 nm wavelength). We report on the antimicrobial capabilities, hydrophobicity and change of chemical composition of the surface before and after UV light illumination. The properties of the biofilms and surfaces are compared using X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM). Calcium assay tests are utilized to estimate the effect of surface type on the change in cell membrane potential. The detailed characterization develops further understanding of the influence of substrate properties on the behavior of P.aeruginosa biofilms and potential for usage in robust bioelectronic platforms experiencing rapid change of external conditions. 2.2 Results and Discussion The following section consists of two major parts: gaining various parameters of the utilized substrate materials, and characterization of biofilms on top of these substrates. Both parts are vital for developing understanding of processes occurring within a biointerface. 2.2.1 Characterization Prior To Bacterial Film Formation We chose to utilize three different types of inorganic substrates since they represent classes of materials often used in electronic interfaces. Numerous studies have used gold (Au) films as a

model substrate to understand adsorption of biomolecules as well as cells[271–273]. Gold films provide inert, stable and durable coating with impressive electric characteristics highly desirable [274,275] in circuit electronics . SiOx is the second type of material we utilized because it is also heavily used in electronics. All modern metal oxide semiconductor field effect transistors rely on a thick layer of oxide as insulation and passivation of the silicon surface providing an efficient electrical interface and thus making SiOx one of the most technologically important inorganic materials. The third surface in our studies was GaN. We chose this wide band-gap semiconducting material because it is widely used in optoelectronics[276–278]. Previously published work examined the surface chemistry change caused by external stimuli[279,280], but it will be beneficial to investigate approaches of non-invasive methods for the change in electronic properties. In addition, recent research has identified GaN as a material with a great promise to be used in bioelectronics due to its stability in water and its biocompatibility characteristics in vitro and in vivo[281,282]. Prior to bacterial film formation we assessed differences in surface quality and topography. Figure 2.1 presents the surface roughness data comparison derived from AFM scans of substrates.

3.0

2.5

2.0

1.5 RMS, nmRMS, 1.0

0.5

0.0 Au GaN SiO x Figure 2.1 Mean roughness values for plane substrate surfaces of Au, GaN and SiOx.

The mean roughness values for SiOx and for GaN were 0.533 ± 0.264 nm and 0.576 ± 0.379 nm, respectively. The data allow us to define SiOx and GaN samples as flat surfaces without significant roughness fluctuations[283–285]. The root mean square (RMS) value for Au is relatively higher, 1.490 ± 1.268 nm. The bigger standard deviation in the values can be rationalized by the presence of distinct grains on the gold surface as a result of the thermal evaporation and has been discussed in the literature[286–288]. Overall all surfaces did not show any specific topographic

features or variations[289,290] that can hinder or enhance biomolecular adsorption or bacterial adhesion. We verified that the three different surfaces have no specific antibacterial activity. While many researchers are directing efforts to develop antibacterial surfaces, we seek to use a surface that does not kill bacteria. Thus, the chosen pathogenic bacteria biolayer regardless of its survivability properties should not be affected by the biocidal abilities of substrate. Therefore, here we want to identify a robust surface for use in bioelectronics that can be utilized as a predictable interface to trigger a bacterial response without destroying the biofilm. We performed a set of antibacterial tests to examine the Au, GaN and SiOx samples for potential biocidal properties. A method described in Foster et al.[291] was utilized to investigate antibacterial behavior of the substrates. All three materials showed no significant differences compared with unmodified glass (negative control). Standard antibacterial tests (antibiotics discs) were utilized and helped to assess the survivability of P.aeruginosa on each substrate. Representative results seen on Figure 2.2 show that all tested materials exhibit no antibacterial behavior in a short-term (12-hour) timeframe.

Figure 2.2 Representative digital pictures showing standard antibacterial tests of Pseudomonas aeruginosa performed for Au, GaN and SiOx substrates. Note: The positive control is represented by antimicrobial susceptibility disks with Streptomycin.

We referenced the behavior of P.aeruginosa in contact with each of our materials to susceptibility disks with Streptomycin, a widely used, broad spectrum antibiotic that targets protein synthesis and is used to target bacterial infections[292]. Reports in the literature have indicated that nanostructured surfaces and dissolved metal ions can lead to antibacterial effects[258,266,293–295]. In summary, via the performed tests can confirm that all three surfaces are stable under the desired biofilm formation conditions, do not degrade in any fashion that can harm the bacteria.

A critical component of a functional biointerface is its ability to trigger a response due to some type stimuli that is mediated by its chemical and/or electronic properties[253,254,260]. Rather than utilizing solution-based treatments, UV illumination of the samples was chosen as an environmental factor that can change surface properties and subsequently trigger an interfacial mediated response associated with film formation. Sterilized and prepared accordingly wafer samples were exposed to UV light prior to bacterial film formation. The reason for choosing a UV- treatment is its simplicity, the fact that it does not require specialized set-up and it has been previously extensively studied as a disinfecting and cleaning treatment, thus can be easily adapted to biointerfaces containing pathogenic organisms. Exposure to UV light of semiconductor materials results in charge accumulation on the surface and reports in the literature have shown that the surface chemistry composition can be altered[296–298]. We assessed initial changes in surface properties by monitoring changes in the hydrophobicity/hydrophilicity of the substrates via contact angle measurements. The mean values for the contact angles on the Au, GaN and SiOx samples before and after exposure to UV light are summarized in Figure 2.3.

Figure 2.3 Mean contact angle values for Au, GaN and SiOx materials before and after irradiation with UV light. Note: Representative digital pictures of water droplets on each sample are pasted above the bar graphs.

All examined samples can be characterized as exhibiting hydrophobic behavior since their contact angles were consistently greater than 35-40°[299]. The mean value of the contact angle on Au was the highest at 63.34°, followed by 64.89° for GaN and 43.52° for SiOx prior to UV light exposure. After UV light treatment of the samples, one can see small differences in the measured

contact angles. The Au mean contact angle increased approximately by 5% and became 66.75°, whereas the GaN and SiOx measurements decreased by 5% with values of 61.66° and 41.37° respectively. Based on these observations one can conclude that UV light exposure alone in air does not cause a statistically significant change in the hydrophobicity of any of the studied surfaces. Quantitative surface characterization can provide further understanding of changes caused by UV irradiation of the samples. XPS is a widely utilized surface characterization tool for [300–305] assessment of surface compositional changes . Cleaned Au, GaN, and SiOx samples were examined by XPS before exposing them to the UV light as well as after 1-hour irradiation. Survey scans were collected and then analyzed to extract the surface chemical composition. Figure 2.4 summarizes the total composition changes in a bar graph format.

Figure 2.4 Mean values representing the chemical composition on the Au, GaN and SiOx films before and after irradiation with UV light.

Here we highlight the main observations with respect to the surface composition after exposure to UV light. On Au samples, compared to the clean, non-irradiated samples, the O concentration increased by 12.36%, the C concentration increased by 9.67% and a small decrease by 3.93% was recorded in the amount of Au. Upon statistical analysis none of the changes observed on the Au samples before and after exposure to UV light can be considered significant. The same overall observation was made on GaN surfaces. The biggest change in the surface composition after exposure to UV light was recorded for O (55.68% increase from the original amount), followed by Ga which exhibited a 14.36% decrease. In addition, on the GaN surfaces the C increased by 14.07% and N increased by 6.30%. Analysis of the data on SiOx samples revealed the smallest compositional changes where the Si amount increased by 2.44%, the O decreased by

1.51% and the C contribution decreased by 10.11%. Overall the initial compositional changes supported the notion that further analysis of the high-resolution data is needed to understand specific surface transformations. Many studies have reported UV light as an effective treatment for the removal of organic contaminants from the surface of various materials[296,298,306]. Furthermore, UV can be used to oxidize surfaces[297,307]. Therefore, we fitted and analyzed the O 1s spectra collected on all of our samples. Figure 2.5 compares changes on each type of material before and after UV exposure and identifies major species after the data was fitted by CASA XPS.

Figure 2.5 High resolution XPS scans of O 1s region on Au, GaN and SiOx before and after irradiation with UV light. Note: The dotted lines represent the location of important components identified after the data was analyzed by CASA XPS.

On Au samples, the spectra are asymmetrical with a tail extending towards higher binding energy, consistent with previous research results[303]. Upon deconvoluting of the data using established parameters available in the literature[302,303,305], the following species were assigned: 2- O (Au2O) at 531.94 eV contributing to 81.09% of the total O composition, OH at 533.33 eV comprising 18.14% and H2O at 534.68 eV responsible for 0.77%. After UV light exposure we 2- analyzed the data and assigned the following species: O (Au2O) peak was found at 532.11 eV,

OH at 533.73 eV and H2O peak at 535.50 eV. After UV light treatment, the OH species 2- concentration decreased by 10.07%, O (Au2O) increased by 2.74% and the most significant change was in the amount of water – H2O contribution which decreased by 33.77%. GaN non- [302] 2- irradiated samples have four main species in the O 1s spectrum: O (as in Ga2O3) at 532.05 eV, residual-OH Ga at 534.28 eV, O=C at 535.81 eV and H2O at 537.86 eV. Upon exposure to the 2- UV light we recorded the following components: 531.70 eV for O (as in Ga2O3), residual-OH Ga

peak at 533.56 eV, O=C contribution at 535.68 eV and H2O peak at 537.44 eV. A significant change in all component contributions was observed (see supporting information), with the largest for residual-OH Ga with 161% increase, whereas the O=C species increased by 38.94%, the H2O 2- contribution increased by 28.74% and the O (as in Ga2O3) peak reduced by 17.05%. Analysis of the O 1s spectra on thermally grown SiOx revealed one sharp peak, which remained symmetrical before and after UV exposure and centered at 532.61 eV and 532.66 eV, respectively. Taken in sum, the XPS data indicates that there is significant change in the types of oxide species present on the surface of the UV-treated samples of Au and GaN. This is an important observation to consider in order to rationalize the results presented further in the study. 2.2.2. P.aeruginosa Film Characterization The morphological and adhesion properties of the P.aeruginosa films formed on Au, GaN and SiOx surfaces were characterized by AFM. Many researchers have utilized AFM as one of the main tools for gaining structural and functional data under different conditions[308–314] along with studying membrane protein localization and molecular interactions[315–318]. Figure 2.6 shows representative topographical images of the films formed on Au, GaN and SiOx samples before and after expose to the UV light.

Figure 2.6 Representative AFM scans of Pseudomonas aeruginosa films formed on various inorganic substrates before and after UV-irradiation.

Overall, the biofilms are densely packed, the P.aeruginosa cells appear healthy and express no evidence of chemical rupture, physical disturbance or any membrane damage signs. The AFM scans were analyzed to extract length- to-width ratio, which is one of important parameters related

to the bacteria growth. For statistical analysis, at least three samples of each material, under each condition were utilized. Up to 10-12 cells were measured on each AFM scan. Figure 2.7 compares the mean length-to-width ratios for Au, GaN, SiOx samples before and after exposing to the UV light.

5 * *

ns 3

2 Length/width ratio

0 UV- UV+ UV- UV+ UV- UV+ Au GaN SiO x Figure 2.7 Mean length-to-width ratios of Pseudomonas aeruginosa cells formed on Au, GaN and

SiOx surfaces before and after irradiation with UV light.

We recorded significant differences in the mean ratios of films grown on samples that were either exposed or not exposed to UV light in the cases of Au and GaN. The ratio for films on Au decreased by 13% (from 3.0 to 2.6) and for films on GaN the decrease was equal to 24% (from 3.4 to 2.6). An opposite trend was observed for films grown on SiOx samples exposed to UV light or not prior to P.aeruginosa introduction. The mean length-to-width ratio had a non-significant increase by 8% (from 2.5 to 2.7). Additionally, we utilized the AFM data to assess changes to the cell wall organization, specifically the generation of outer membrane vesicles (OMV) by quantifying changes on an individual cell surface roughness. OMV are components of the bacteria stress response which aid in nutrient procurement, biofilm production, and pathogenicity[319]. Bacteria generating OMV, also called blebbing, have a roughened cell surface. An individual cell RMS value was extracted by drawing a line along the bacteria center of symmetry along the entire cell. In a similar manner to the analysis described above, at least three samples in three different regions were scanned on each substrate as well as under each UV light condition. Up to 10-12 cells were measured from each AFM scan. Figure 2.8 compares all the data extracted from this analysis. 37

ns ns

40 RMS,nm

0 UV- UV+ UV- UV+ UV- UV+ Au GaN SiO x Figure 2.8 ”Blebbing effect” caused by UV treatment. Mean roughness values of single

Pseudomonas aeruginosa cells scanned with AFM on Au, GaN and SiOx surfaces before and after irradiation with UV light.

When one compares the data on each material where films were formed on surfaces that were either treated by UV light or not, statistically there were no significant differences when the data is analyzed for differences on each material after treatment. However, it is important to note that the mean RMS values on Au and GaN decreased on materials treated by UV light, but the opposite behavior was observed on SiOx. In general, the morphological analysis performed by AFM supports the notion that the nature of the material and UV light treatment can change the properties of the P.aeruginosa cells comprising the films on the surface. Biofilm adhesion is a property that is important to assess since it is closely connected to film growth and development[269,320–325]. Further AFM characterization of the P.aeruginosa films was done by Force Volume maps. A representative map along with a histogram extracted from it is shown on Figure 2.9(A). The Force Volume scan was done as 32x32 points image and each Force Volume map was used to extract a mean adhesion force value. For statistical comparisons, a minimum of 3 force maps for each substrate and each condition were collected. The data was analyzed in both the Asylum Research software package and Origin Pro utilizing 2-way ANOVA.

On Figure 2.9(B) a statistical comparison for the films on Au, GaN and SiOx samples before and after UV light irradiation is presented.

Figure 2.9 (A) Surface adhesion histogram obtained from AFM Adhesion force map (seen in the inset). Both figures represent data for Pseudomonas aeruginosa biofilm on the surface of GaN substrate before UV light irradiation. (B) Mean surface adhesion values of Pseudomonas aeruginosa biofilms on Au, GaN and SiOx samples before and after UV light treatment.

The data shows a visible change in adhesion force for all samples before and after UV light exposure. However, the change of adhesion force on GaN samples, which experienced 61% decrease, was statistically significant. Additionally, on Au surfaces there was a 48% decrease in adhesion force and as an opposite trend was recorded SiOx surfaces with a 64% increase in adhesion force on films grown on material after UV light irradiation. The adhesion data correlates with the morphological data described in the previous section as well as with the data extracted by surface characterization. Thus, changes in surface chemistry (residual OH and O2-) caused by UV irradiation affect the biofilm properties. At the same time, certain other properties, such as substrate hydrophobicity can have a negligible effect on biofilm formation, which correlates with conclusions from prior literature[322,326–328]. Recent research defines Ca2+ as an important indicator and regulative factor in a wide range of biological systems. Both intracellular and intercellular Ca2+ concentrations define and influence numerous functions in eukaryotic and prokaryotic cells[329,330]. For example, in eukaryotes Ca2+ serves as an universal transmitter of signals from the surface to the cell core[331]. Apart from that, in prokaryotes free Ca2+ is known to play a role in physiological processes, such as spore formation, virulence and change in cell differentiation[332]. Free intercellular Ca2+ is associated with regulation of cell processes involving structural integrity, motility, metabolism, life cycle, pathogenesis, sporulation, transport and stress signals[331]. Possible changes in intercellular Ca2+ gradients can be

viewed as an internal physiological response of bacteria cells to changes in environmental conditions[333–335] and it is an important parameter to consider when analyzing the properties of the formed P.aeruginosa films and morphological responses to the substrate materials we studied. The quantification of intracellular Ca2+ concentrations is done by assays utilizing different readouts based on optical changes such as photoluminescence and fluorescence[295,330]. We utilized a Fluo-4 Direct™ Calcium Assay that is based on a fluorescence signal and has been tested on various types of biological systems[295,336]. Multiple time points were scanned and 2 points showing the response of the stabilized solution under ambient conditions were extracted and analyzed in OriginPro. The summarized data for 20 and 30 minutes time points on all materials under different conditions is shown on Figure 2.10. Statistical analysis clearly shows a difference between films grown on samples before and after UV illumination. Variations in Ca2+ concentrations are typically linked to changes in the integrity of the cell wall, but it can also be indicative of other biological factors alterations[331,332]. With respect to our study the Fluo-4 Direct™ Calcium Assay support the notion that the altered surface properties after UV exposure facilitate the formation of biofilms with different mechanical properties.

* * * ns 30000 *

ns ns ns 20000

Fluorescence,f.u. 10000

0 20 30 20 30 20 30 20 30 20 30 20 30

Au GaN SiOx Au GaN SiOx UV- UV+ Figure 2.10 Summary of Fluo-4 Direct Calcium assay results. Note: Fluorescence measurements were taken after 20 and 30 minutes for Pseudomonas aeruginosa cells grown on the Au, GaN and

SiOx substrates before and after UV light treatment.

2.3 Experimental section Materials and Supplies: All commercial materials and supplies were used as received according to the manufacturer’s instructions: Pseudomonas aeruginosa (Schroeter) Migula (ATCC® 27853™) from ATCC; Tryptic Soy Broth, Prepared Media Bottle, 125 mL (Item # 776840) from Carolina Biological Supply Company; Thermo Scientific™ nutrient soy agar powder, 500g (Cat.CM0003B); Fisherbrand™ Petri Dishes with Clear Lid (Cat.FB0875714); Oxoid™ Streptomycin/Penicillin/Novobiocin Antimicrobial Susceptibility Disks (Cat.CT0047B; Cat.CT1755B) from Thermo Fisher Scientific; Invitrogen™ Fluo-4 Direct™ Calcium Assay Kit (Cat.F10471) from Thermo Fisher Scientific; High Quality (HQ)™ HQ-75-Au AFM Probes with frequency 푓 = 75 푘퐻푧, and spring constant 푘 = 2.5 from Oxford Instruments. Ga-polar GaN was grown as previously reported[337]. Semitransparent Au thin films (20 nm thickness) were deposited by e-beam on ~1.3 μm thick Ga-polar (0001) GaN on 430 μm thick Al2O3 substrate with a 5 nm of Ti adhesion layer. SiOx wafers were purchased from University Wafer with 500 nm thermally grown oxide. Wafer sample preparation: All wafer samples for every test performed in this study were prepared in the same way to maintain sterility of samples. Wafer samples were sterilized by autoclaving at 121°C for 30 min and then stored for 3 days to equilibrate a possible effect of autoclaving on material surface. Biofilm sample preparation: Tryptic soy broth was autoclaved at 121°C for 25 min and then slowly cooled to room temperature. Pseudomonas aeruginosa (P.a.) dry pellet was dissolved in 20 ml sterile tryptic soy broth, vortexed for 120 sec and then placed in the shaking incubator (environmental shaker) at 37°C to incubate overnight. The rest of the P.a. dry pellet was frozen using 15% Glycerol solution according to standard freezing protocols and stored at -78°C. Tryptic Soy Agar plates were prepared for further use where 16 g of nutrient soy agar powder was dissolved in 400 ml of DI water. Subsequently the solution was autoclaved at 121°C for 25 min and slowly cooled to room temperature. The solution was poured into Petri dishes and after overnight cooling was available for further use. Using a method adapted from Foster et al.[291] we transfer 5 ml bacterial suspension washed by vortexing for 60 seconds 3 times in 10 ml sterile DI water and then centrifuged at 5000 x g for 10 min. The pellet that was obtained was suspended in 500x Tryptic Soy broth to obtain a solution of approximately 2 x 108 CFU/ml, as required by adapted protocol. The concentration was confirmed with Biomate3 Spectrophotometer

(ThermoElectron Corporation) at O.D. 600 nm. A droplet (approximately 10 µl) of obtained solution was transferred to tryptic soy agar plates and spread across the area of the Petri dish with sterile culture spreader. Sterile wafers pieces from the test materials (GaN, Au or SiOx) were put face down onto the agar. The samples were incubated at 37°C for 8 hours to obtain a biofilm of desired density. The samples designated as UV+ were produced the same way, however prior to the biofilm preparation the inorganic surfaces were exposed to UV lamp illumination for 1 hr. (Longwave UV Lamp (Analytik Jena) with a 365 nm wavelength). Antibacterial tests: Adapted from Foster et al.[291] the method includes a preparation of a “sandwich”: a wafer piece of a test material is placed on a sterile coverslip, a droplet of bacterial suspension is put on the top of wafer sample and then covered with a round coverslip. The prepared “sandwich” is transferred into a Petri dish on gauze moistened with water and then incubated at 37°C for 1, 4, 12, 24 hours. After the incubation the entire “sandwich’ is placed in 20 ml sterile DI water and vortexed for 60 sec. This suspension is used to make 1:10-1:1000 dilutions in DI water. Subsequently even droplets of desired concentrations are transferred and spread in a fresh Nutrient Agar plate. Prepared plates were incubated as directed and used for further data analysis. Adapted Kirby-Bayer technique[338] requires 4-5 individual colonies picked from the agar plate were transferred to 5 ml of sterile DI water. The suspension was vortexed for 60 sec. A fresh Nutrient agar plate was inoculated evenly with inoculation loop. Sterile samples were put face down, antimicrobial susceptibility disks with various antibiotics are also transferred. Prepared samples were incubated at 37°C for overnight and representative digital photos of grown cultures were taken with Nikon D3300 camera. Calcium assay test: Prepared UV- and UV+ samples were washed in 5 ml of sterile DI water at 5000x g for 5 minutes. The cell concentration was checked with Biomate3 Spectrophotometer and showed comparable results for samples composed of different materials. Utilizing the manufacturer’s protocol, prepared Fluo-4 Direct™ Calcium Assay was added to 24- well plates with cell suspensions and then incubated in the dark at 37°C for 1 hour. Fluorescence measurements were performed using Tecan GENios microplate reader with 485 nm excitation and a 535 nm emission filters installed. Analysis of the obtained data was done using OriginPro 2017 (v. 9.4.1.354). AFM characterization: Asylum Research Cypher was utilized to obtain at least 3 scans of random areas of prepared film samples and clean materials. HQ-75-Au AFM cantilevers with

frequency 푓 = 75 푘퐻푧, and spring constant 푘 = 2.5 were used at scanning frequency of 0.5 Hz to obtain images of 5x5 µm areas. Tapping mode was performed in air at room temperature. Force maps were generated for biofilm samples using the same cantilevers. Generated images and adhesion force maps were processed using Asylum Research software (v. 13.01.68) bundled into Igor Pro (v. 6.22). RMS roughness and adhesion force values were extracted for comparison among all samples. Cell geometrical parameters were obtained from AFM scans using Image J software package (v. 1.51q). XPS characterization: All data was collected with Kratos Analytical Axis Ultra XPS. Wide survey scans were acquired with 160 eV pass energy. High resolution data was collected for: Au 4f, Ga 3d, Ga 2p, Si 2p, C 1s, N 1s, O 1s, S 2p at a pass energy of 20 eV. Atomic compositions of elements and peak fits were analyzed using Casa XPS software package (v. 2.3.19). Peak calibrations were performed by setting the adventitious C 1s peak to be equal to 284.8 eV. Contact angle: Contact angle measurements were recorded on Ramé-hart automated dispensing system. 1.2 µl Di water droplets were deposited onto Au, GaN, SiOx wafer samples. Ramé-hart Model 200 F4 series standard goniometer was used to collect images. OnScreenProtractor Java-applet (v. 0.5) was employed to analyze the droplet images. Statistical analysis: Data analysis was carried out using 1-way, 2-way and 3-way ANOVA method with OriginPro 2017 (v. 9.4.1.354). 2.4 Conclusions In summary, we recorded and analyzed P.aeruginosa biofilm formation mechanism on the surface of Au, GaN and SiOx under different environmental conditions by exposing the materials to UV light prior to film formation. The surface chemistry does change upon exposure to the UV, and in turn can induce the differences in the biofilm properties. The wide bandgap semiconductor material, GaN, is more versatile substrate for biocomputing applications and is the only one that shows noteworthy effects because that is where one would expect all the surface states (and its persistent photoconductivity) to be significant. Specifically, individual bacteria within films can differ in their morphological characteristics as well as adhesion characteristics. Furthermore, films grown on surfaces after exposure to UV light uniformly show higher intracellular Ca2+ concentration consistent with a different mechanical response. The results of this work can be used to design functional interfaces that utilize both the properties of the material as well as morphological and biological change in the bacterial biofilm.

CHAPTER 3 The Interfacial Properties of Doped Semiconductor Materials Can Alter the Behavior of

Pseudomonas Aeruginosa Films

3.1 Introduction UV light treatments have found a broad range of applications in different spheres of human life: manufacturing, healthcare, forensic science, agriculture, in natural sciences and engineering. For example, UV-irradiation is widely used in materials science, chemistry, biology and many other areas to change the bulk and surface properties of various materials, substrates and interfaces.[339–344] For almost a century, UV treatments have been among the most effective and straightforward methods of disinfection with the cleaning and/or deactivating microorganisms through the irradiation of surfaces.[345–348] UV light exposure is also a primary tool in food water[349–351],air purification.[352] In addition to surface sterilization, UV light treatments have been used to control synthetic chemical reactions. UV light alters the surface chemistry of many material to form of catalytic sites[353,354] or oxides.[355–357] Furthermore, UV light is the reaction initiator in many polymer cross-linking processes where it triggers the formation of additional bonds between polymeric chains.[358–361] In these applications, UV-irradiation is a practical methodology with many advantages including its non-invasive delivery, its applicable to broad range of materials, and the low cost of UV systems and the speed of its application. Certain classes of materials such as semiconductors undergo a number of changes after exposure to UV light. More specifically, UV-irradiation induces a free charge carriers in which: (1) there is an accumulation of surface charge and (2) it may lead to changes in the bulk material, such as increased electrical conductivity.[362,363] The importance of this phenomena, the reasons why they arise and possible influence of doping levels of substrate materials used were studied in previous work of our research group.[364] While the effects of UV irradiation on different semiconductor materials are used in many applications, little work has been done to examine how UV light can be applied to biointerfaces to alter interfacial properties and control the behavior of microorganisms without killing them. In our prior work we have demonstrated that the properties of wide bandgap materials in conjunction with UV light can play a part in biointerfaces with mammalian cells.[108,364,365] We seek to extent the utility of wide bandgap materials that incorporate living microorganisms and show that they can serve as a versatile bioelectronic interface.[366]

In this study, we used GaN semiconductor thin films were doped with Si to obtain different carrier concentrations: 1x1018 cm-3 and 2x1019 cm-3 (medium and high doping concentration respectively). The surface charge of the films was changed by exposure to UV light and the altered interfaces were employed to modulate the behavior of a test microorganism (particularly, bacteria). We aim to use UV light to alter the properties of semiconductor interfaces but not for the purposes of developing an anti-bacterial material. In our prior work, we have shown that P.aeruginosa grows and remains healthy on GaN.[120] During this study we never exposed the bacteria directly to UV light in contrast to what other researchers have done to explore antibacterial materials. As it was stated in the previous chapter, Pseudomonas aeruginosa is one of the most typical representatives of in-hospital infections targeting people with a depressed immune system. The bacteria possess a high resistance to antibiotics and has an ability to develop a strong resistance to adverse external conditions using various mutation mechanisms. All these factors make P.aeruginosa an excellent candidate for these studies due to its relevance in biomedical research and its ability to quickly adapt to changes in external conditions.[261,262,264,292] We aim to show in this work that UV-treated GaN surfaces can serve as programmable functional interfaces that contain living bacteria.[367] With this in mind we show that changes in semiconductor surface properties can be programmed to change the P.aeruginosa behavior without killing it. In order to understand the behavior of GaN-P.aeruginosa biointerfaces, surface characterization of the materials was performed along with biological assay studies. Chemical composition, topography and surface charge of the GaN samples were quantified by X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), Kelvin Probe Force Measurements (KPFM) and contact angle (CA) measurements. Reactive oxygen species (ROS) assay, catalase activity assay and calcium concentration assay tests were utilized to determine the behavior of P.aeruginosa films under different interfacial conditions. We monitored Ga release into the aqueous solutions using inductively coupled plasma mass spectrometry (ICP-MS). In this work we demonstrated that the quantitative and qualitative changes to the P.aeruginosa films are directly related to changes in the GaN materials interfacial properties in a UV activation dependent manner, which supports the notion that GaN films can be the “control unit” of functional biointerfaces. 3.2 Results and Discussion Same it was done previously (Chapter 2), the following section will consist of two parts,

where one is the characterization of substrate materials, and the second is the characterization of biointerfacial structures formulated during our studies. 3.2.1 Substrate Surface Characterization We began this work by hypothesizing that the accumulation of charge on the surface of inorganic samples is a significant way to manipulate the response of biological system formed on a GaN surface. However, prior work has shown that bacterial film formation and behavior can be manipulated by other interfacial characteristics such as surface roughness.[322,326,327] In our own work with the same materials we have demonstrated that surface roughness can be an important factor in cell adhesion.[279,368] Therefore, we assess the surface characteristics of GaN samples with two different doping concentrations (GaNm(edium) and GaNh(igh)) that were either treated or not treated with UV light. We characterized the roughness of clean GaN samples using AFM topography imaging. The roughness was 0.962 nm with standard deviation (SD) to be 0.514 nm for GaNm and 0.731 nm (SD 0.250 nm) for GaNh. To check a possible effect of the illumination with UV light, we collected scans right after the UV treatment. Root mean squared (RMS) roughness values were 0.903 nm (SD 0.419 nm) for GaNm and 0.821 nm (SD 0.302 nm) for GaNh samples. Statistical analysis supports the conclusion that UV light irradiation of all of the types of GaN samples does not significantly affect their roughness. The change in surface charge of GaN samples was quantified using KPFM (Figure 3.1).

Figure 3.1(a) shows how surface charge changes with time under different external conditions (no UV light, UV light is on and, finally, UV light is off). One observes a slow decay of the charge after the UV-irradiation was stopped. Figure 3.1(b) represents the values of surface potential of GaNm and GaNh substrates before and right after UV irradiation. The mean values are the following: 567.04 mV and 1035.38 mV (438.34 mV increase) for GaNm, 551.60 mV and 968.35 mV (416.75 mV increase) for GaNh samples, respectively. Based on the statistical analysis, we consistently detected a significant increase upon UV-illumination of all GaN samples. GaNh samples have slightly higher surface potential after the removal of the UV light exposure and a slower decay rate, which means that the charge decay time is longer than one for GaNm samples. However, GaNh samples have lower surface potential for initial and UV-irradiated conditions (as it can be seen in Figure 3.1). Thus, the amount of doping does not result in a significant difference in surface potential change and play a bigger role when we move deeper from the surface, which correlates with theoretical and experimental observations of these semiconducting materials.[369,370] Generally, semiconductors of various types (and GaN as one of them) possess a significant amount of charge carriers concentrated near the surface, that are also play a role in the bulk properties.[365,368] Above mentioned carriers are responsible for occupying surface energy states, whose configuration is governed by the chemical composition and polarity of the material deposited. Furthermore, the finite number of allowed surface energy states explains a fact that the difference in surface potential between GaN samples of different doping level is insignificant. UV light has been routinely used to clean different types of surfaces[296,371] and change the hydrophobicity of materials.[296,298,306] We carefully evaluated changes of contact angle (CA) of prepared GaN samples under the following conditions: 1) Clean – clean GaNm and GaNh samples; 2) Fast – GaN samples, which are GaN substrates with a P.aeruginosa biofilm which is removed by vortexing, and subsequently removed from the DPBS solution; and 3) Slow – GaN samples, which are GaN substrates with a P.aeruginosa biofilm, which is removed by vortexing and returned to the DPBS solution overnight. UV-treatment was done by the exposure of clean equilibrated samples to the UV light source for 1 hour. The baseline data of the clean GaN samples allowed us to evaluate the changes to the surface properties of the GaN materials caused by the formation of P.aeruginosa biofilms and various alterations in external conditions. Additional details regarding sample preparation procedures are provided in the Experimental section. Figure 3.2 summarizes all CA results for all conditions before and after exposure to the UV light.

Figure 3.2 Changes in water contact angles of clean and modified GaN surfaces. Note: M and H indicate medium and high doping respectively, whereas – and + are used to indicate no exposure and exposure to UV light, respectively.

For the clean samples we observed a significant difference in CA for both GaNm and GaNh substrates between UV-treated and non-treated samples. GaNm samples had CA of 70.08° for UV- and 57.58° for UV+ samples; GaNh samples had CA values of 71.05° and 63.45° for UV- and UV+, respectively. For both Fast and Slow sets of samples, GaNm and GaNh did not show a significant difference between clean UV-exposed and UV-irradiated samples. The difference between Fast and Slow sets of GaN samples was not significant, which suggests that the changes to the substrate’s hydrophobicity due to P.aeruginosa biofilm formation are rapid and irreversible. However, there were significant differences among Clean, Fast, Slow treated sample sets of both GaNm and GaNh that were not exposed to the UV light. Basic statistical comparison is performed on UV-exposed and non-exposed materials of the same condition: significant difference is shown with asterisk (*) and non-significant difference is expressed as (ns). Here and further, Greek alphabet letters are used to mark statistical interactions between various materials and conditions. For example, alpha (α) distinguishes the interactions between Clean and Fast GaN samples. In the legend, one can see all interactions tested for α: direct statistical comparison between Fast and Clean UV-illuminated samples along with indirect UV-positive /UV-negative Fast and Clean samples. Other statistical interactions are presented in the same manner and can be observed

following the same logic of reading the statistical legend insets in pictures of this manuscript. Here on Figure 3.2, the differences in the results among the three conditions (Clean, Fast, Slow) can be explained by the formation of oxides on the surface of samples upon exposure of liquid solutions for Fast and Slow-treated samples. Furthermore, an effect of UV-irradiation diminishes with time and in the case of Fast and especially Slow conditions it completely fades away. As a result, one records no significant difference between UV- and UV+ samples, as it shown on the graph. However, all the gathered measurements help us to better understand how different conditions can affect the hydrophobicity of GaNm and GaNh samples used in our experiments. We subsequently quantified the elemental composition of the prepared GaNm and GaNh samples by XPS. We initially analyzed survey scans allowing us to access the chemical composition of a surface along with possible change caused by different external conditions. The XPS scans of the Clean, Fast and Slow GaNm and GaNh samples are in Figure 3.3.

Figure 3.3 Chemical compositions of (A) Clean GaN surfaces, (B) Fast GaN surfaces, and (C) Slow-treated GaN surfaces obtained by analysis of XPS survey scans. Note: The data is collected on GaNm and GaNh samples both equilibrated in the dark (-) and UV-exposed (+).

The total chemical composition of Clean GaN samples has four major contributors: Ga, N, C, and O (Figure 3.3a). Clean GaNm samples express the following changes under UV-irradiation: Ga – 1.46% decrease, N – 3.98% decrease, C – 38.77% increase and, importantly, O shows 20.46% increase. We observed similar results for Clean GaNh: Ga contribution decreased on 3.69%, N increased on 3.07%, C shows 27.29% increase and O increased on 8.42% for UV-irradiated samples respectively. There was no significant change in all contributing chemical elements for both GaNm and GaNh substrates upon exposure to the UV light. XPS analysis of Fast and Slow treated GaN substrates express two additional components, Na and Cl, which are presented in the DPBS solutions. Fast-treated UV+ GaNm samples expressed the following changes to their chemical composition when compared to non-irradiated samples: Ga composition decreased by 2.33%, N composition decreased by 1.14%, C composition increased by 32.82%, Na content increased by 49.69% and Cl content increased by 62.65%. Fast GaNh samples were characterized with the following changes to their chemical composition after exposure to the UV light: Ga composition decreased 8.74%, N showed a decreased of 8.25%, C showed a 76.07% increase, O content increased 66.36% increase, Na concentration increased almost in three times and Cl almost doubled its amount. Only Ga and C express a significant statistical difference upon exposure to the UV light for both GaNm and GaNh samples (Figure 3.3b). The increased carbon contamination appeared to be caused by contact with P.aeruginosa biofilm and was greater in the UV-treated samples. XPS analysis of Slow-treated GaNm and GaNh samples showed similar changes to their chemical composition as Fast-treated samples (Figure 3.3c). XPS analysis of Slow-treated GaNm change between UV- and UV+ compositions is the following: Ga content decreases by 3.82% ; N content decreases by 5.07% ; C content increases by 31.36% ; O content increases by 11.59%; and Na shows a 29.53% increase and Cl has a 34.39% increase. Slow-treated GaNh samples are characterized with 10.14% decrease in Ga content, a 9.21% decrease in N content, a 74.08% increase in C content, a 74.72% increase in O content (Figure 3.3c). Furthermore, we find that the Na concentration doubles and Cl increased by 84.25% on Slow-treated GaNm surfaces after UV+ activation. Between GaNm and GaNh substrates after UV irradiation, the only significant differences observed was the change in Ga content where GaNh samples showed a significant difference after UV-irradiation. Comparing Fast-treated and Long-treated GaN samples, the increased in Cl deposition correlated with the increase incubation in DPBS buffer solution. These

results confirm that UV irradiation of the GaN surfaces and the presence of a P.aeruginosa biofilm alters the chemical composition of GaN surface in a rapid manner. Upon exposure to liquid solutions one needs to consider the formation of oxide species because they can ultimately leads to dissolution of metal species in solution, which have been shown to change the survival of bacteria.[372,373] Important surface changes can be tracked through the presence of variable types of oxygen and carbon species. Thus, we analyzed the narrow XPS scans of O1s and C1s spectra for Clean, Fast-treated, and Slow-treated GaN samples (Figure 3.4).

Figure 3.4 XPS analysis of the high-resolution data in the following regions: (A) O1s and (B) C1s regions for Clean, Fast-treated, and Slow-treated samples. Note: GaNm and GaNh samples were measured as equilibrated (-) and after exposure to the UV light (+).

The O1s spectra contains three components: metal oxide (Ga2O3), hydroxyl groups (OH), and water with carbon contamination (Figure 3.4a). Overall, Fast and Slow-treated GaNm and GaNh samples show a decreasing trend of metal oxide content and an increase in OH group contribution. The amount of water and carbon contamination is slightly larger for both Fast and Slow-treated GaN samples when compared to Clean GaN samples. There was no significant difference in the content of O species between UV- and UV+ samples for both GaNm and GaNh samples Figure 3.4). The C1s spectra consists of three major contributors: C-C, C-O-C, and O- C=O bonds (Figure 3.4b). Both Fast and Slow-treated samples show a significant decrease from Clean GaN samples in the presences of C-C bonds, but an overall increase in both C-O-C and O- C=O bonds (Figure 3.4b). The difference between Fast and Slow GaN samples is not statistically significant. We observe no significant different in the C1 spectra of GaNm and GaNh treated either way after UV treatment. Another indicator of a change in chemical properties can be accessed through Ga 2p spectra deconvolution. All samples examined had overlapping Ga 2p1/2(element) and Ga 2p1/2(native oxide) components and therefore we monitored the potential changes to the oxidative state of Ga through the Ga 2p peak spectra. We observed a small but significant shift towards lower binding energies in Fast and Slow-treated GaN samples when compared to Clean GaN, both medium and high doped. However, we did not observe any significant differences in the Ga 2p peak position after UV irradiation in any sample. There is a possibility that the increased amount of oxide on the surface of GaN samples can facilitate the release of Ga in solution. We measured the dissociation of Ga ions from the GaN surfaces into aqueous solutions by ICP-MS. In these experiments we tested the solutions incubated with both GaNm and GaNh and treated under all conditions, UV+/- and Long/Slow (Figure 3.5). We measured Ga by ICP-MS after all interfacial conditions, although statistically there was no significant difference among any all of the condition treatments (Clean, Fast, Slow), doping concentration or in between UV-irradiation vs. no UV-irradiation (Supporting Information). Clean GaN samples exhibited Ga concentrations in the range of 19.9-27.7 μg/L. For Fast and Slow- treated GaN samples, the range increased to 22.1-69.1 μg/L and 27.3-58.7 μg/L respectively. We also tested the Blank, DPBS and Peroxide solutions for the presence of Ga traces and all samples were below the ICP-MS detection limits. We conclude that the amounts of Ga present in the solution due to dissociation from the GaN are not responsible for differences P.aeruginosa behavior. These results also support the notion that if there are changes in the P.aeruginosa

behavior after exposure to the variably treated GaN materials they are interface dependent rather than being mediated by variable amounts of dissociated metal ions in solution.

ns ns

120

ns 100 ns

60 ns

ns 40 Ga concentration, μg/L Gaconcentration,

† †† † 0 M- M+ H- H+ H2O2- H2O2+ PBS Blank M- M+ H- H+ H2O2- H2O2+ PBS Blank M- M+ H- H+ H2O2- H2O2+ PBS Blank Clean Fast Slow

Figure 3.5 ICP-MS data for all solutions used in the ROS dye experiments. Clean, Fast and Slow sets of samples were evaluated for Ga leakage. UV- and UV+ samples represent equilibrated and UV-irradiated samples. Note: †-amount of Ga was below detection limits (< 1.0 ug/L). ††- for all solutions but DPBS amounts of Ga were below detection limits. For DPBS, tests report the value of 1.1 ug/L, which is still considered to be insignificant.

3.2.2 Biointerface Response Tests There are a variety of established ways of monitoring the biological response to environmental changes during bacterial growth and biofilm development.[331,374–376] The changes in oxide content of the Fast and Slow-treated GaN surfaces that had been subjected to P.aeruginosa biofilms suggested that the presence of this living layer was responsible for these chemical changes to the surface. A significant response component to many biological systems including bacterial biofilm formation is the presence of free radical oxygen species (ROS). To determine whether there is an increase in ROS species on these GaN surfaces we utilized a dye (2’,7’-

Dichloroflyorescin diacetate) sensitive to multiple types of reactive oxygen species (ROS). They . . include hydrogen peroxide (H2O2), hydroxyl radical ( OH), superoxide radical ( O ), singlet 1 oxygen( O2) and can be produced during internal cellular response to alterations of external conditions.[377–379] Same as in previous characterization sections, we examined Clean, Fast treated, and Slow-treated GaN samples. Both UV+ GaNm and GaNh were treated with UV light for 1 hour. Compiled results of ROS dye tests are represented on Figure 3.6.

Figure 3.6 ROS studies of P. aeruginosa on GaN for (A) Clean, (B) Fast-treated, and (C) Slow- treated samples. Note: GaNm, GaNh and hydrogen peroxide were UV-irradiated to see the change in the amount of ROS from non-treated samples. DPBS and Blank serve as important reference points introducing the baseline of fluorescence signal.

For all conditions, hydrogen peroxide was used as a positive control to establish the validity of the assay. Clean dye solution is denoted as Blank. Initially we determined the amount of ROS species present on the surface of GaN samples before and after UV-irradiation (Figure 3.6a). We 54

find that there is no significant difference for both GaNm and GaNh samples when compared to the Blank. Furthermore, UV light does not cause a significant difference in ROS production on between UV-treated and non-treated samples. However, incubation of both GaNm and GaNh surfaces with P.aeruginosa resulted in elevated levels of ROS generation (Figure 3.6b, c). In both the Fast and Slow-treated GaN samples the amount of ROS generated by P.aeruginosa cells washed from GaNm and GaNh samples is significantly greater than Blank and obviously greater than the amount from Clean, untreated samples. There is no significant change in the signal level for UV- and UV+ samples. Control experiments show that the DPBS buffer alone generated no autofluorescence signal confirming that it was the presence of a P.aeruginosa biofilm in contact with a GaN substrate that generated the high levels of ROS. Slow-treated GaN samples produced similar results as the Fast-treated samples, although the total signal is lower than that found on Fast-treated samples (Figure 3.6c). As with the Fast-treated samples, we observed no significant change in ROS generated florescence after UV-irradiation of the GaN. However, the overall amount of signal from GaN is lower, than in case of Fast samples. To comment on this fact, one should notice the difference in the signal produced by H2O2, Blank and DPBS. Blank and DPBS levels still stay at the comparable levels, thus indicating slow degradation and overall stability of dye solution with time. Hydrogen peroxide increases the level of signal in case of Slow samples, showing that the dye degrades after prolonged exposure to ROS as witnessed by the signal increase. These results suggest that prolonged exposure of the P.aeruginosa biofilm, allows the bacteria to adjust to the surface conditions by removing or lowering the increased ROS species. Furthermore, this effect is independent of doping level and that the amount of doping in GaN samples does not play a significant role in this response of the P.aeruginosa biosystem studied. As we showed above, the GaN substrate results in a bacterial response, specifically the increased production of ROS. To determine whether P.aeruginosa cell behavior is altered by Ga in the absence of interface substrate we tested whether various concentrations of ionic Ga altered bacterial ROS production. We generated Ga ion in solution using commercially available salt gallium nitrate (Ga(NO₃)₃). We used several concentrations: (1 μg/L, 0.5 μg/L and 0.25 μg/L) and the negative control (no salt) was just P.aeruginosa cell/dye suspension on an insert glass coverslip substrate. The gallium nitrate solutions were used in two different ways: 1) as droplets, that were put on the surface of agar with P.aeruginosa cells before biofilm formation step; and 2) as solutions where the initial amount of P.aeruginosa cells was suspended and used to form the biofilm. The

two different modes of biofilm-initiating P.aeruginosa bacteria interaction allowed us to test two parameters. In the droplet technique only, the P.aeruginosa cells contacted the substrate (i.e. glass coverslip) experienced the Ga ions, while in the solution technique, all bacteria in the Petri dish were exposed to Ga ions in solutions. In the droplet assay, we observed a significant alteration in ROS production for all Ga solutions with P.aeruginosa cells when compared to Blank (Figure 3.7a). Also, no-salt, 0.25 μg/L and 1 μg/L express the significant difference in signal levels as well. In the Solution assay, we observed a linear dependence of production ROS with gallium nitrate concentrations (Figure 3.7b). All tested solutions possess signals significantly higher levels of ROS production than blank; 0.5 μg/L and 1 μg/L solutions produce signals that were significantly greater than no-salt P.aeruginosa cell solution. The results summarized in Figure 3.7 support the notion that the presence of an interface can suppress the effect of Ga ions in solution and impede the production of ROS even in the presence of ample amount of metal ions in solution.

Figure 3.7 ROS studies of Ga(NO₃)₃ salt for (A) Droplet and (B) Solution techniques applied to P.aeruginosa cells initializing the biofilm formation. Note: 0 ug represents cells not affected by salt. Blank expresses the baseline fluorescence signal. Statistical analysis interactions shown in captions.

We also examined the change of intercellular calcium (Ca2+) concentrations as a possible bacterial behavior indicator related to stress responses necessary to maintain homeostasis.[329,330] A Fluo-4 direct Ca assay monitored changes after each experimental variation. We recorded Fluo- 4 fluorescent levels, Figure 3.8, immediately after incubation of P.aeruginosa with GaNm and GaNh substrates (both UV- and UV+).

0.06

0.04 Δ RFU Δ

0.02

0.00 UV- UV+ UV- UV+ M H Figure 3.8 Fluo-4 Direct Calcium assay test results. Note: Fluorescence measurements taken for P.aeruginosa grown on the surface of GaNm and GaNh samples before and after UV exposure.

A significant increase in fluorescence was detected on GaN substrates that been UV- irradiated when compared to non-irradiated GaN controls. The magnitude of the response correlates with a doping amount. Along with the KPFM data, these results suggest that the intracellular calcium changes in biolayers are related to surface potential changes in GaN samples. Since we detected a significant increase in ROS production by P.aeruginosa biofilms in the presence of Ga, we investigated whether part of this response included expression of the oxidative stress enzyme, catalase. Catalase is an enzyme found in most organisms that specifically decomposes hydrogen peroxide to water and oxygenation serves as an indicating factor of an oxidative stress response.[380,381] Particularly, catalase concentration correlates with the amount of ROS species in the cell.[382] Using a colorimetric Catalase activity assay we tested P.aeruginosa biofilms formed on pristine and UV-treated GaNm and GaNh samples , with results on Figure 3.9.

6 * *

Catalase, U/ml 2

0 UV- UV+ UV- UV+ M H Figure 3.9 DetectX® Catalase colorimetric activity kit data. Note: P.aeruginosa biofilm cell response was evaluated for GaNm and GaNh samples before and after irradiation with the UV light. 57

We observe that both UV irradiated GaNm and GaNh sets have a significant increase in catalase activity when compare to the control, non-irradiated samples. These results correlate with the observed increase in intracellular calcium. Furthermore, because the results are collected immediately after contact with variably treated GaN, one can conclude that the change in surface potential is the initial trigger of P.aeruginosa biofilm response. 3.3 Experimental section Materials and Supplies: All commercial materials and supplies were used as received according to the manufacturer’s instructions: Pseudomonas aeruginosa (Schroeter) Migula (ATCC® 27853™) from ATCC; Tryptic Soy Broth, Prepared Media Bottle, 125 mL (Item # 776840) from Carolina Biological Supply Company; HyClone™ DDPBS (Dubelcco’s Phosphate- Buffered Saline)/Modified, 500 mL (Cat.SH30028.02) ordered from GE Healthcare Life Sciences; DMSO(Dimethyl Sulfoxide), 250mL (Ref.25-950-CQC) ordered from Corning Mediatech; nutrient soy agar powder, 500g (Cat.CM0003B); Fisherbrand™ Petri Dishes with Clear Lid (Cat.FB0875714) from Thermo Fisher Scientific; Corning® 96 Well Clear Polystyrene Microplate (Mfr.#Corning3358) ordered from Sigma-Aldrich; 2’,7’-Dichloroflyorescin diacetate (crystalline), 250 mg (Cas.76-54-0) ordered from Sigma-Aldrich; DetectX® Catalase Colorimetric Activity Kit (Cat.K033-H1) ordered from Arbor Assays; Invitrogen™ Fluo-4 Direct™ Calcium Assay Kit (Cat.F10471) from Thermo Fisher Scientific; Hydrogen peroxide (30%), 500ml. (Cat. BP2633500) ordered from Fisher Scientific; High Quality (HQ)™ HQ-75-Au AFM Probes with frequency 푓 = 75 푘퐻푧, and spring constant 푘 = 2.5 from Oxford Instruments; ASYELEC.01- R2 AFM Probes with conductive Ti/Ir coating, frequency 푓 = 75 푘퐻푧, and spring constant 푘 = 2.8 ordered from Oxford Instruments; Gallium (III) nitrate hydrate (crystalline), 25g. (Lot.MKCC1844) ordered from Sigma-Aldrich; UVP Mini UV Lamp, (Cat.U-2221-13) purchased from Analytik Jena. GaN with Ga-polar GaN (Medium) and (High) types was grown as previously reported in our laboratories.[285,383,384] Biofilm sample preparation: Tryptic soy broth was autoclaved at 121°C for 25 min and then slowly cooled to room temperature. Pseudomonas aeruginosa dry pellet was dissolved in 20 ml sterile tryptic soy broth, vortexed for 120 sec and then placed in the shaking incubator (environmental shaker) at 37°C to incubate overnight. Tryptic Soy Agar plates were prepared for further use where 16 g of nutrient soy agar powder was dissolved in 400 ml of DI water.

Subsequently the solution was autoclaved at 121°C for 25 min and slowly cooled to room temperature. The solution was poured into Petri dishes and after overnight cooling was available for further use. Using a method adapted from Foster et al.[291], we transfer 5 ml bacterial suspension washed 3 times in 10 ml sterile DPBS or DI water and then centrifuged at 5000 x g for 10 min. The pellet that was obtained was suspended in DI water to obtain a solution of approximately 2 x 108 CFU/ml. The concentration was confirmed with Biomate3 Spectrophotometer (ThermoElectron Corporation) at O.D. 600 nm. A droplet (45 µL) of obtained solution transferred to tryptic soy agar plates and spread across the area of the Petri dish with sterile culture spreader. Sterile wafers with test materials were put face down onto agar. Samples incubated at 37°C for 8 hours to obtain a biofilm of desired density. The samples designated as UV+ were produced the same way, however prior to the biofilm preparation the inorganic surfaces were exposed to UV lamp illumination for 1 hr. (UVP Mini UV Lamp (Analytik Jena) with a 365 nm wavelength). UV+ hydrogen peroxide solutions were irradiated for 1 hour in uncovered wells, thus being directly exposed to the UV light. Ga(NO₃)₃ samples preparation: Ga(NO₃)₃ salt solutions (Droplets) were prepared by dissolving crystalline Ga(NO₃)₃ in DI water to obtain desired (1 μg/L, 0.5 μg/L, 0.25 μg/L, 0 μg/L – no salt added) concentrations. Using biofilm sample preparation steps, we spread a droplet of P.aeruginosa solution across the area of the Petri dish with sterile culture spreader. Droplets (25 μL) of Ga(NO₃)₃ were put onto the surface of the agar and then covered with sterile glass coverslips. Samples then were incubated at 37°C for 8 hours to obtain a biofilm of desired density. Ga(NO₃)₃ salt solutions (Solution) were prepared by dissolving crystalline Ga(NO₃)₃ in DI water to obtain desired (1 μg/L, 0.5 μg/L, 0.25 μg/L, 0 μg/L – no salt added) concentrations. A P.aeruginosa pellet from biofilm preparation steps is dissolved in obtained Ga(NO₃)₃. Droplets (45 μL) of the prepared P.aeruginosa-Ga(NO₃)₃ solutions were transferred to tryptic soy agar plates and spread across the area of the Petri dish with sterile culture spreader. Sterile glass coverslips were put face down onto agar. Samples incubated at 37°C for 8 hours to obtain a biofilm of desired density. ROS assay test: 2’,7’-Dichloroflyorescin diacetate solution was prepared according to adapted protocols available.[385] The 1 mM stock solution was prepared by dissolving 2’,7’- Dichloroflyorescin diacetate in DMSO. The working dye solution of 1μM was prepared by dissolving the stock solution in DPBS. The unused stock solution was aliquoted into a single use

vial and stored at -20°C and kept from light. Clean samples (UV- and UV+) of GaN(medium) and GaN(high) were put into working 1 μM dye solution, vortexed for 2 minutes and incubated in the dark at 37°C for 1 hour. Solutions were transferred into 96-well plates, equilibrated for approximately 15 minutes and then read in the plate reader. Fast P.aeruginosa samples were prepared as following. Samples with P.aeruginosa biofilms were washed by vortexing with working 1μM dye solution for 2 minutes and then incubated in the dark at 37°C for 1 hour. Then samples were vortexed again, equilibrated and solutions were transferred into 96-well plates and read in the plate reader. Long P.aeruginosa samples were prepared according to similar procedures. Samples with P.aeruginosa biofilms were washed by vortexing with working 1μM dye solution for 2 minutes and then incubated in the dark at 37°C for 1 hour. Samples removed from an incubator, vortexed and stored in the dark and equilibrated at room temperature for 12 hours. After that, the solutions were transferred into 96-well plates and read in the plate reader. Hydrogen peroxide (0.5% solution) was used as a positive control for this dye. Blank samples (just dye solution) and clean DPBS solutions were used as a control of a fluorescence signal baseline. All readings of 96-well plates (6 wells for each sample in each plate) were performed with Tecan GENios microplate reader with 492 nm excitation and a 535 nm emission filters installed. Further analysis of raw data was done in Origin Pro 2017 (v. 9.4.1.354). Catalase activity test: Catalase activity assay solutions and standards were prepared according to instructions provided in manufacturer’s protocol. Prepared P.aeruginosa samples of GaNm and GaNh were washed in Catalase assay buffer by vortexing for 2 minutes. Obtained cell suspensions were transferred to 96-well plates and then the steps from the assay protocol were completed. Ready solutions were incubated for 15 minutes at room temperature in the dark and then evaluated with Tecan GENios microplate reader. The optical density was read at 570 nm. Readings (6 wells for each sample in each plate) for both samples were averaged and then curve fitting routine was performed with a help of online tool from MyAssays (available through Arbor Assays). Calcium assay test: Prepared UV- and UV+ samples were washed in 5 ml sterile DI water by vortexing for 2 minutes. Cell concentration checked by Biomate Spectrophotometer showed comparable results for samples of different materials. Utilizing manufacturer’s protocol, prepared Fluo-4 Direct™ Calcium Assay was added to 96-well plates with cell suspensions (6 wells for each sample in each plate) and then incubated in the dark at 37°C for 1 hour. Fluorescence

measurements were performed using Tecan GENios microplate reader with 485 nm excitation and a 535 nm emission filters installed. Analysis of obtained data was done in Origin Pro 2017 (v. 9.4.1.354). AFM characterization: Oxford Instruments MFP-3D Origin AFM setup was used to obtain at least 3 scans of random areas of clean wafer samples. At least 3 samples were prepared per each material and each condition. HQ-75-Au AFM cantilevers with frequency 푓 = 75 푘퐻푧, and spring constant 푘 = 2.5 were used at scanning frequency 0.5 Hz to obtain images of 2.5x2.5 µm and 5x5 µm area scans. Tapping mode was performed in air at room temperature. KPFM scans were taken with ASYELEC.01-R2 AFM Probes at 1 Hz and 2.25 Hz to create 2.5x2.5 µm, 5x5 µm and 5x5 nm charge distribution maps. Generated images and charge distribution maps were processed using Asylum Research software (v. 13.01.68) bundled into Igor Pro (v. 6.22) and RMS roughness and surface charge values were extracted for comparison among all samples. XPS characterization: Kratos XPS was utilized to collect all the data as detailed in a number of other studies.[302–304] Wide survey scans were collected for samples using 160 eV pass energy. High resolution data was collected for: Ga 3d, Ga 2p, C 1s, N 1s, O 1s at pass energy of 20 eV. Atomic compositions of elements and peak fits were generated in Casa XPS software package (v. 2.3.19). Peak calibrations performed by setting the adventitious Carbon 1s equal to 284.8 eV. Contact angle: Contact angle measurements were recorded on Ramé-hart automated dispensing system. 1.25 µl Di water droplets were deposited onto GaNm and GaNh wafer samples. Ramé-Hart Model 200 F4 series standard goniometer was used to collect images. OnScreenProtractor Java-applet (v. 0.5) was employed to analyze the droplet images. ICP-MS: Solutions were submitted for analysis with a Thermo Element XR instrument. 200 μL aliquots were taken from each sample and each condition where P.aeruginosa samples were autoclaved before the analysis. All ICP-MS analysis was performed by the Environmental and Testing Services at NC State University. Obtained Ga concentrations were analyzed using Origin Pro 2017 (v. 9.4.1.354). Statistical analysis: For each material and each condition at least 3 samples were prepared and analyzed. For surface analysis tests, at least 3 random spots were scanned(imaged) on each sample. For assay tests, at least 3 96-well plates with full set of solutions were used. Data analysis

was carried out using 1-way, 2-way and 3-way ANOVA statistical method embedded into the Origin Pro 2017 (v. 9.4.1.354). 3.4 Conclusions In summary, we formed and analyzed GaN-Pseudomonas aeruginosa biofilms on variably doped semiconductor surfaces. We detected P.aeruginosa responses as a result of changes in interfacial conditions (i.e. change in surface potential of the semiconductor). UV-irradiation of GaN samples of different doping concentration levels not only can trigger surface chemistry changes but also alter the behavior of the P.aeruginosa within the biofilm. Furthermore, the bioassays carried out for Ca2+ and catalase showed that the magnitude of the P.aeruginosa changes are dependent on the GaN doping. The results of this work can be used to tune the function of biointerfaces that incorporate living microorganisms.

CHAPTER 4

Evaluating Stress-Triggered Intracellular ROS Generation of Saccharomyces Cerevisiae

Cultures on Inorganic Semiconducting Substrates

4.1 Introduction and Motivation The logical continuation of work was to utilize a set of slightly different microorganisms to expand the portfolio of biological entities involved in the studies of inorganic semiconducting substrate-microorganism biointerfacial studies. The biointerfaces created were examined to better understand their behavior in altered external conditions. Furthermore, certain Salt Overly Sensitive (SOS)-signaling channels of the biofilms were analyzed. Specifically, the generation of intracellular ROS was in our particular interest. Saccharomyces cerevisiae (S.cerevisiae) is one of the most popular and well-studies yeast types for biologically-focused research.[386–388] This culture is known for centuries and came from the food industry, since it is extensively used in baking, winemaking, and brewing. At the same time, this culture does not require special conditions or sophisticated procedures for handling, maintaining the culture, and thus rending experiment designs to be very straightforward and efficient. Furthermore, there is an impressive set of genetically modified strains of S.cerevisiae, which allows testing of cellular responses and morphological, physiological, genetical characteristics of cultures or biointerfaces exposed to various external conditions or stress factors.[389] Two types of S.cerevisiae cell lines (TBR1 and TBR5) were used in this study. Both strains are haploid type, which means the strain possesses only one set of unpaired chromosomes, so these cultures are the basic ones and express standard reactions. TBR 1is a wild type S.cerevisiae yeast, so it was not modified or altered. At the same time, TBR 5 is a mutant strain (Delta Flo11). This haploid strain is missing the adhesion protein Flo11 and grows at a slower rate. Examining the culture growing at a slower rate and comparing its reactions with a wild type should allow one to detect cellular reactions in smaller steps, where the responses occur at a lower rate, thus making the observation process more precise and descriptive. Similar to previous studies, the substrates are made of Ga-polar GaN, since this material was in the focus of prior research due to its unique electronic properties and outstanding stability of this material for biological applications.[120] Two types of GaN samples were utilized, as the

samples expressing the most distinguishable response among configurations available for our studies. Particularly, sapphire wafers with GaN thin films of two carrier concentrations: 1x1018 cm-3 and 2x1019 cm-3 (medium and high doping concentration respectively) allowed us to form resilient and mature S.cerevisiae biofilms. The analysis of ROS tests[121] performed previously (Chapter 3 of this dissertation), unveiled certain limitations of the plate reader technique that will be advantageous to overcome. As it was noticed, the detection limits of the equipment we were using did not allow us to see a significant difference between equilibrated and UV-treated samples, as well as detect any initial concentrations of ROS species on the substrate surfaces. Utilizing fluorescence microscopy and established image analysis techniques (special software, machine learning algorithms) opens a wide path for visualization of the processes occurring within the biointerface as well as increases the accuracy of the data gathering and further interpretation of results. 4.2 Results and Discussion In this chapter we will use two fluorescence techniques simultaneously, and the goal is to 1) to obtain the intracellular ROS concentrations for a new type of biofilm culture (yeast) and correlate these results with the data from our previous experiments; 2) to gain ROS concentrations in S.cerevisiae cultures using the fluorescence microscopy, thus visually evaluating the amounts and areas of possible accumulation/generation of ROS species as well as quantifying their intensities; 3) to compare these two approaches and make some general conclusions regarding their effectiveness and usefulness for our research project. Validating this will enable researchers to choose appropriate techniques, configurations, and parameters for even more sophisticated biointerface studies. The first stage of the experiment involved formulation of the working yeast cell suspensions, and preparation of the biointerfaces, as explained in the Experimental section of this Chapter. In brief, the yeast suspension droplets were spread across the surface of YPD plates and GaN samples (equilibrated at a dark and UV-treated) of Medium (M) and High(H) doping concentrations (also denoted as GaNm and GaNh in the text) were placed onto YPD nutrient surface and incubated. The biointerfaces were further tested with the plate reader to determine the concentration of ROS for each type of samples and conditions. Cell suspensions for plate reader tests were prepared with the use of 2’,7’-Dichloroflyorescin diacetate dye. As previously, the positive control was Hydrogen peroxide and negative control – blank solution of the dye providing

a baseline fluorescence. As it was discussed in Chapter 3, the positive control allows to track the stability of ROS-sensitive dye. The another negative control (cells grown on the surfaces of SiOx wafers) shows cells not experiencing stresses since these surfaces are quite neutral and do not express alterations in their properties due to UV-light exposure. The results of plate reader characterization were obtained as fluorescence values and are represented on the Figure 4.1. ns 25 ns ns ** *** ns 20 ns **** *

15 ns,* ... **** ns

10 ns

% fluorescence % increase control from -5

UV- UV+ UV- UV+ UV- UV+ UV- UV+ UV- UV+ UV- UV+ TBR1 TBR5 TBR1 TBR5 TBR1 TBR5

GaN_M GaN_H SiOx

Figure 4.1 ROS plate reader studies of S.cerevisiae cultures on GaN samples. Note: the information represented as a % increase of GaN of medium(M) and high (H), SiOx fluorescence above the control – blank solution, which is a baseline fluorescence. TBR1 and TBR5 are two strains of yeast used in our studies.

As it can be seen, all the samples expressed certain degree of response. In addition, for all of them the increase from baseline fluorescence was observed. Particularly, SiOx samples showed an increase from 1.57% (TBR1, UV-treated samples) to 6.17% (TBR5, UV-treated). As for GaN, the increase range was broader and depended on conditions: from 10.10% for GaNm (TBR1, not UV-treated) and 12.66% GaNh (TBR5, no UV irradiation), to 17.30% for GaNm samples (TBR5, UV-treated) and 18.18% for GaNh samples (TBR1, UV-irradiated). As it can be seen, there is a clear trend increasing the amount of signal for semiconducting samples irradiated with the UV

light, whereas this behavior is not confirmed for SiOx samples. This is quite logical, since cells on neutral surfaces were not experiencing stress levels offered by the charged surfaces of GaN. At the same time, statistical analysis cannot confirm whether these stresses are strong enough, since all the differences between samples are considered to be non-significant. This again points out the limitations of the detection capabilities of the used plate reader technique. Thus, we carried out the second stage of the experiment, which is the examination of biointerfaces with fluorescence microscopy. According the procedure explained in the Experimental section, the biointerfaces were prepared for further treatment with the ROS-sensitive dye (2’,7’-Dichloroflyorescin diacetate) and microscope examination. The representative examples of optical microscopy scans may be seen on Figure 4.2 below.

Figure 4.2 Representative optical microscopy scans of S.cerevisiae biofilms. Note: (A) TBR1 strain biofilm segments grown on GaN of high (H) and (B) TBR5 cluster grown on GaN of medium(M) doping concentrations.

One needs to note that this time the biolayers were not removed from the surfaces of substrates, rather the entire biointerface is studied. This should, theoretically, provide more accurate assessment, since the biointerface is less disturbed during the sample preparation. The biofilms were imaged and analyzed to extract the mean fluorescence intensity for a certain number of cells, which provides an extensive statistical data for analysis. The SiOx samples are taken as

controls allowing to see the basic fluorescence of cells on neutral surfaces that are not inducing stresses. The summarized results of the fluorescence data comparison and statistical analysis can be observed in the Figure 4.2 below.

Figure 4.3 ROS fluorescence microscopy studies of S.cerevisiae cultures. Note: the information presented for GaN of medium(M) and high (H), SiOx samples and for TBR1 and TBR5 strains of S.cerevisiae yeast used in our studies. Statistical analysis can be observed in the table (caption).

As it can be seen, in the case of fluorescence microscopy there is a significant difference between UV-treated and non-treated GaNh and GaNm samples. This behavior is observed for both types of yeast strains (TBR1 and TBR5). It is important to notice, that there is no statistically significant difference between all SiOx samples that were chosen as a control group. Also, there is no significant difference between the control group and all GaN samples that were not exposed to the UV light. At the same time, we see the significant difference (increase of fluorescence) for all

UV-treated samples from the control group (SiOx) and from corresponding non-UV-treated samples. The degree of this response can range from 82.15% increase (GaNm, TBR5) for UV- irradiated samples to 118.63% increase (GaNm and GaNh, TBR1) in UV-treated substrates. This perfectly confirms our previous findings and conclusions regarding the behavior of semiconducting samples exposed to UV light. At the same time, we did not observe a significant

difference between GaNm and GaNh samples, which suggests the doping levels play less significant role in the reaction. This may be explained by the fact that both of them provide approximately same amount of surface potential, as it was discussed in previously[121,146] (Chapter 3, Figure 3.1). Also, it is important to note that TBR1 samples overall show a trend of higher fluorescence levels (however, these differences are not significant), and this may be due to the fact that the TBR5 strain are mutants with decreased cell attachment and slower growth rate. Overall, the fluorescence microscopy technique and further data analysis expands and confirms the initial findings that were observed by plate reader. The detected statistically significant reactions of GaN-S.cerevisiae biointerface suggest that the response of the biointerface is mainly due to substrate properties (physical, electronical, chemical) and are not limited to a particular type of microorganism interfaced with this material. 4.3 Experimental section Materials and Supplies: All commercial materials and supplies were used as received according to the manufacturer’s instructions: mature refrigerated colonies of S.cerevisiae TBR1 and TBR5 obtained from LaJeunesse lab at UNCG; dry YPD (Yeast extract, Peptone, Dextrose), 500g (Cat. 50843466) from Fisher Scientific; HyClone™ DDPBS (Dubelcco’s Phosphate- Buffered Saline)/Modified, 500 mL (Cat.SH30028.02) ordered from GE Healthcare Life Sciences; DMSO (Dimethyl Sulfoxide), 250mL (Ref.25-950-CQC) ordered from Corning Mediatech; Fisherbrand™ Petri Dishes with Clear Lid (Cat.FB0875714) from Thermo Fisher Scientific; Corning® 96 Well Clear Polystyrene Microplate (Mfr.#Corning3358) ordered from Sigma- Aldrich; 25 x 75mm, 1.00 mm thick VWR micro slides (Cat.48300-025) from Sigma;24x50mm, #1.5 Slip-Rite® Cover glass (Cat. 152450) from ThermoScientific; 2’,7’-Dichloroflyorescin diacetate (crystalline), 250 mg (Cas.76-54-0) ordered from Sigma-Aldrich; Hydrogen peroxide (30%), 500ml. (Cat. BP2633500) ordered from Fisher Scientific; UVP Mini UV Lamp (365 nm wavelength), (Cat.U-2221-13) purchased from Analytik Jena. GaN samples with Ga-polar GaN (Medium) and (High) types was grown as previously reported in our laboratories.[285,383,384] Biointerface sample preparation: Liquid YPD media and YPD agar plates were prepared according to a standard protocol. Using a method adapted from Foster et al.[291], we transfer 5 ml of working yeast suspensions (TBR1 and TBR5) and add some liquid YPD media to obtain a suspension of approximately 2 x 108 CFU/ml. The concentration confirmed with Biomate3 Spectrophotometer (ThermoElectron Corporation) at O.D. 600 nm. A droplet (40 µL) of yeast

suspension put onto the YPD agar plates and spread across the area with a sterile plastic spreader. Prepared clean inorganic substrate samples of test materials were put onto the agar to form a mature biolayer by further incubation. The plates with the samples then placed into 27°C incubator for 8 hours. UV-irradiated samples prior biofilm formation were equilibrated in a dark and then exposed to the UV lamp for a period of 1 hr. Plate reader yeast tests: 2’,7’-Dichloroflyorescin diacetate stock solution was prepared according to adapted protocols available.[385] The dye working solution was also prepared according to procedures discussed in Chapter 3. Positive controls were prepared by loading a dose of 0.5% solution of Hydrogen into the appropriate wells with ROS dye. GaN ans SiOx with TBR1 and TBR5 biofilms were put into working 1 μM dye solution, vortexed for 2 minutes and incubated in the dark at 27°C for 1 hour. These yeast solutions were transferred into 96-well plates, equilibrated for 20-30 minutes, and loaded into the plate reader. The readings were performed using Tecan GENios microplate reader with 492 nm excitation and a 535 nm emission filters. m h Optical microscopy yeast tests: The prepared biointerface samples (GaN and GaN , SiOx) with both TBR1 and TBR5 yeast cultures were transferred and fixed(glued) on glass slides. A small droplet of working ROS dye solution (2’,7’-Dichloroflyorescin diacetate) was added on the surface of each sample and excess of liquid was removed using a plastic pipette. The samples were incubated at a dark at 27°C for 30 minutes (dry cycle), and then covered with glass coverslips and equilibrated for 30 minutes at RT at a dark. The fluorescence microscopy scans of biofilms were obtained using Olympus IX83 microscope with Xylis X-cite fluorescence setup. 10x, 40x and 60x oil lenses were used to obtain scans at different magnification. ROS FLou channel was used with 470nm excitation wave and 508nm emission wave filters, and the same exposure times were installed for tested samples. Further analysis and average per-number-of-cells quantification was carried out using CellSens Dimension (v.2.3.18987) software package provided by Olympus. The scans or areas were chosen to provide 40-60 cells per each imaged area. Statistical analysis: For each material and each condition at least 3 samples were prepared and analyzed. For plate reader tests, at least 3 96-well plates with full set of solutions were used. In case of fluorescence microscopy tests at least 3 areas were scanned on each sample, and at least 3 samples for each condition, substrate type and yeast strain were examined. All the collected datasets were analyzed statistically by using 2-way and 3-way ANOVA methods that are embedded into Origin Pro 2017 (v. 9.4.1.354).

4.4 Conclusions In this study we were able to use two fluorescence techniques allowing to detect amounts of fluorescence produced by the generation of intracellular ROS species. Yeast culture S.cerevisiae served as test microorganism and two strains (wild type TBR1 and genetically modified TBR5) showed a distinct reaction to irradiation of semiconducting GaN samples regardless of their doping levels. At the same time, the control group of SiOx samples expresses no reaction to UV-irradiation and overall exhibited stable neutral behavior. The observed responses confirm an idea that the properties of substrates used in a biointerface play a crucial role in the biointerface behavior. The plate reader allows to detect only significant amounts of fluorescence produced by intracellular ROS and thus does not permit an evaluation of the full degree of the response. As for fluorescence microscopy, it offers better sensibility along with visualization abilities, but is more labor-intensive. Since both techniques have advantages and disadvantages, the performed comparison may be extremely useful for gaining hands-on experience in the research process optimization.

CHAPTER 5

Conclusions and Future Directions

5.1 Conclusions In this dissertation, a conception of an advanced bio-applicable material (particularly, GaN- microorganism biointerface) was offered, developed, and characterized. This characterization allowed us to unveil factors triggering various responses of formulated biointerfacial structures to altered substrate properties and changes in external conditions. The dissertation consists of five chapters, and each details a particular stage of the research project. The first chapter presents our preliminary findings in the area of bioengineering and defines possible direction of the research project. The current status of advanced microorganism-inorganic biointerfaces is summarized along with types of responses that can be observed in such hybrid systems. Chapter 1 identifies promising inorganic material types and target microorganisms that will be critical for future research on programmable biointerfacial structures. Due to unique electronic properties and proven biocompatibility, Gallium-based semiconducting compound (GaN of different configurations) was chosen as the most promising material for the formation of interfaces with various types of microorganisms. Chapter 2 identifies Pseudomonas aeruginosa as one of the most promising candidates for a biointerface formulation and further characterization. In this part of the work, the changes of the surface properties of used inorganic substrates (Au, GaN and SiOx) after UV light irradiation were utilized to actively influence the process of formation of Pseudomonas aeruginosa films. The interfacial properties of the substrates were characterized by XPS and AFM. The changes in the P.aeruginosa films properties were accessed by analyzing adhesion force maps and quantifying intracellular Ca2+ concentration. The collected data indicates that the alteration of the inorganic materials’ surface chemistry can lead to differences in biofilm formation and variable response from P.aeruginosa cells. Next step was to access more substrate parameters and explore more paths of possible stress responses in the created biointerfaces. In Chapter 3, the changes in surface charge and chemistry after exposure to UV light were studied as way to alter the behavior of P.aeruginosa films. Similar to previous experiments, the properties of GaN (medium and high doping

concentrations) surfaces were characterized by AFM, KPFM and XPS. The P.aeruginosa film responses were quantified by analyzing changes in the amount of catalase, reactive oxygen species and intracellular Ca2+ concentrations. The comprehensive analysis performed in this part of the project supports the notion that the response of P.aeruginosa biofilms can be controlled by the properties of the interface and the amount of time the film is in contact with it. At the final stage of this dissertation project, two strains of S.cerevisiae yeast were chosen to be interfaced with GaN (medium and high doping concentrations ) substrates. The SOS- signaling channels were identified by evaluation of intracellular ROS concentrations in the formulated biointerfacial structures. Two fluorescence techniques used in the Chapter 4 allowed us to compare the results obtained and make some assumptions regarding the measurements accuracy. This approach also confirmed the conclusions made in Chapter 3 to be valid for a different test microorganism. Thus, UV-irradiation of semiconducting substrates causes a distinct stress reaction in the examined microorganisms. In conclusion, the findings and experimental outcomes described in Chapters 1 through 4 develop better understanding of fundamental processes occurring within inorganic substrate- microorganism biointerfacial structures as well as highlight the factors and parameters that may influence the properties or trigger response reactions in such biointerfaces. 5.2 Future Directions & Perspectives Looking at the further continuation of this research project and its prospective development, one can consider the utilization of new semiconducting materials that were not examined previously. Possibilities include but are not limited to AlGaN and InGaN. Furthermore, it is promising to offer significant expansion of a nomenclature of microorganisms that may be interfaced with the inorganic materials identified in Chapter 1. This is a quite logical continuation of our studies and does not require a lot of changes in established protocols. At the same time, there is a need to better understand the mechanisms that cause the cellular responses we were able to observe. To develop smart programmable materials, there is a need in reliable and effective pathways that allow to predict and (or) control the reactions of these materials. In case of biointerfaces, it is necessary to look deeper into genetic machinery of utilized microorganisms and cells. The next level of sophistication of our research project suggests working on the examining the genetic structure of biofilms. Doing such, it will be possible to observe what signaling systems

are involved in the reaction, what mutations took or may take place and other types of genetic responses that might be experienced by the biointerface in the future. This knowledge, as it was discussed previously, will allow us to develop a controllable behavior of formulated biointerfaces. Furthermore, DNA structure deconvolution and further analysis will open paths for verification of long-term stability of a structure, which serves as another important characteristics of a programmable material. Implementation of novel approaches in data gathering and analysis brings multiple benefits to any modern research group. Following the logic expressed in Chapter 4, there is a need in usage of multiple techniques, further comparison of results and identification of the most effective tools. Routine usage of Data Analysis and Machine Learning approaches in a research group may help in this. For example, AFM or optical microscopy techniques can benefit from Machine Learning by increasing an accuracy of measurements, predicting properties of the material with characteristics on-demand, and advancing the data processing. Additionally, for an effective, fast, and accurate analysis of the gathered results one would like to have access to raw data from previous (or even old) experiments. To do so, it will be necessary to establish a centralized database that will contain the results for all samples and techniques used. These results should be saved in a standardized manner and the database has to be universal, with an ability to accommodate the data for experiments that may be potentially performed in the future by multiple researchers (and where some parameters can be changed, new factors introduced or removed). Ideally, databases like this should serve as a container not only for results of peers in research groups, but even for collaborators from various institutions working on the same project. Possibility of linking or uploading the database into hubs (like resources offered by Citrine, ProQuest and other) will be beneficial for the entire international research community. Possessing such an extensive dataset, one can proceed with further application of ML algorithms (like binary trees or random forests, recursive feature elimination and similar methods) that will be able to predict the accuracy of planned experiments or derive optimal regimes and parameters. Successful implementation of this approach will allow researchers to avoid performing unnecessary and labor-demanding activities and significantly increase the effectiveness and speed of the research.

BIBLIOGRAPHY

[1] M. Li, N. A. Ostrovsky-Snider, M. Sitti, F. G. Omenetto, MRS Adv. 2019, 4, 2787.

[2] T. J. White, D. J. Broer, Programmable and adaptive mechanics with liquid crystal

polymer networks and elastomers. Nat. Mater. 2015, 14, 1087.

[3] C. Gilbert, T. Ellis, ACS Synth. Biol. 2019, 8, 1.

[4] X. Fan, J. Y. Chung, Y. X. Lim, Z. Li, X. J. Loh, Review of Adaptive Programmable

Materials and Their Bioapplications. ACS Appl. Mater. Interfaces 2016, 8, 33351.

[5] J. R. Peralta-Videa, L. Zhao, M. L. Lopez-Moreno, G. de la Rosa, J. Hong, J. L. Gardea-

Torresdey, Nanomaterials and the environment: A review for the biennium 2008-2010. J.

Hazard. Mater. 2011, 186, 1.

[6] Z. Yang, J. Tian, Z. Yin, C. Cui, W. Qian, F. Wei, Carbon nanotube- and graphene-based

nanomaterials and applications in high-voltage supercapacitor: A review. Carbon N. Y.

2019, 141, 467.

[7] S. Suresh, Nanosci. Nanotechnol. 2013, 3, 62.

[8] T. H. Tran, V. T. Nguyen, Int. Sch. Res. Not. 2014, 2014, 1.

[9] C. Zhu, G. Yang, H. Li, D. Du, Y. Lin, Electrochemical sensors and biosensors based on

nanomaterials and nanostructures. Anal. Chem. 2015, 87, 230.

[10] P. Si, Y. Huang, T. Wang, J. Ma, Nanomaterials for electrochemical non-enzymatic

glucose biosensors. RSC Adv. 2013, 3, 3487.

[11] K. H. Roh, M. Yoshida, J. Lahann, Materwiss. Werksttech. 2007, 38, 1008.

[12] T. Y. Liu, S. H. Hu, D. M. Liu, S. Y. Chen, I. W. Chen, Biomedical nanoparticle carriers

with combined thermal and magnetic responses. Nano Today 2009, 4, 52.

[13] J. Shi, P. W. Kantoff, R. Wooster, O. C. Farokhzad, Cancer nanomedicine: Progress,

challenges and opportunities. Nat. Rev. Cancer 2017, 17, 20.

[14] A. Kar, K. S. Raja, M. Misra, Surf. Coatings Technol. 2006, 201, 3723.

[15] S. H. Lee, H. W. Kim, E. J. Lee, L. H. Li, H. E. Kim, J. Biomater. Appl. 2006, 20, 195.

[16] P. J. P. Espitia, N. de F. F. Soares, J. S. dos R. Coimbra, N. J. de Andrade, R. S. Cruz, E.

A. A. Medeiros, Zinc Oxide Nanoparticles: Synthesis, Antimicrobial Activity and Food

Packaging Applications. Food Bioprocess Technol. 2012, 5, 1447.

[17] A. J. Haider, R. H. Al-Anbari, G. R. Kadhim, C. T. Salame, In Energy Procedia; 2017;

119, 332.

[18] P. Cai, W. R. Leow, X. Wang, Y. L. Wu, X. Chen, Adv. Mater. 2017, 29, 1605529.

[19] Q. Zhao, Y. Wang, H. Cui, X. Du, Bio-inspired sensing and actuating materials. J. Mater.

Chem. C 2019, 7, 6493.

[20] H. H. Tuson, D. B. Weibel, Soft Matter 2013, 9, 4368.

[21] Y. Jiang, J. L. Carvalho-De-Souza, R. C. S. Wong, Z. Luo, D. Isheim, X. Zuo, A. W.

Nicholls, I. W. Jung, J. Yue, D. J. Liu, Y. Wang, V. De Andrade, X. Xiao, L.

Navrazhnykh, D. E. Weiss, X. Wu, D. N. Seidman, F. Bezanilla, B. Tian, Nat. Mater.

2016, 15, 1023.

[22] F. Patolsky, G. Zheng, O. Hayden, M. Lakadamyali, X. Zhuang, C. M. Lieber, Proc. Natl.

Acad. Sci. U. S. A. 2004, 101, 14017.

[23] K. K. Sakimoto, A. B. Wong, P. Yang, Science (80-. ). 2016, 351, 74.

[24] S. H. Lee, M. K. Gupta, J. B. Bang, H. Bae, H. J. Sung, Adv. Healthc. Mater. 2013, 2,

908.

[25] J. Ping, Z. Fan, M. Sindoro, Y. Ying, H. Zhang, Recent Advances in Sensing Applications

of Two-Dimensional Transition Metal Dichalcogenide Nanosheets and Their Composites.

Adv. Funct. Mater. 2017, 27, 1605817.

[26] J. Guo, R. J. Padilla, W. Ambrose, I. J. De Kok, L. F. Cooper, Biomaterials 2007, 28,

5418.

[27] A. Ayati, A. Ahmadpour, F. F. Bamoharram, B. Tanhaei, M. Mänttäri, M. Sillanpää, A

review on catalytic applications of Au/TiO2 nanoparticles in the removal of water

pollutant. Chemosphere 2014, 107, 163.

[28] Z. Gagnon, S. Senapati, H. C. Chang, Electrophoresis 2010, 31, 666.

[29] A. Agarwal, Y. Lvov, R. Sawant, V. Torchilin, J. Control. Release 2008, 128, 255.

[30] R. Matsuno, K. Ishihara, Integrated functional nanocolloids covered with artificial cell

membranes for biomedical applications. Nano Today 2011, 6, 61.

[31] S. J. H. Soenen, M. Hodenius, M. De Cuyper, Magnetoliposomes: Versatile innovative

nanocolloids for use in biotechnology and biomedicine. Nanomedicine 2009, 4, 177.

[32] H. Mirzaei, M. Darroudi, Zinc oxide nanoparticles: Biological synthesis and biomedical

applications. Ceram. Int. 2017, 43, 907.

[33] J. W. Rasmussen, E. Martinez, P. Louka, D. G. Wingett, Zinc oxide nanoparticles for

selective destruction of tumor cells and potential for drug delivery applications. Expert

Opin. Drug Deliv. 2010, 7, 1063.

[34] Z. Huang, X. Zheng, D. Yan, G. Yin, X. Liao, Y. Kang, Y. Yao, D. Huang, B. Hao,

Langmuir 2008, 24, 4140.

[35] R. Brayner, R. Ferrari-Iliou, N. Brivois, S. Djediat, M. F. Benedetti, F. Fiévet, Nano Lett.

2006, 6, 866.

[36] M. K. Rai, S. D. Deshmukh, A. P. Ingle, A. K. Gade, Silver nanoparticles: The powerful

nanoweapon against multidrug-resistant bacteria. J. Appl. Microbiol. 2012, 112, 841.

[37] I. Sondi, B. Salopek-Sondi, J. Colloid Interface Sci. 2004, 275, 177.

[38] Y. Yuan, J. Ding, J. Xu, J. Deng, J. Guo, In Journal of Nanoscience and Nanotechnology;

2010; 10, 4868.

[39] A. S. Hayward, N. R. Cameron, S. A. Przyborski, In Biointerfaces: Where Material Meets

Biology; Hutmacher, D., Ed.; The Royal Society of Chemistry: London, England, 2014;

pp. 133–150.

[40] H. S. Dhowre, S. Rajput, N. A. Russell, M. Zelzer, Responsive cell-material interfaces.

Nanomedicine 2015, 10, 849.

[41] E. V Skorb, D. V Andreeva, Adv. Funct. Mater. 2013, 23, 4483.

[42] Y. Zhang, K. Chu, S. He, B. Wang, W. Zhu, F. Ren, Mater. Sci. Eng. C 2020, 106,

110165.

[43] J. Wang, S. Kong, F. Chen, W. Chen, L. Du, W. Cai, L. Huang, C. Wu, D. W. Zhang,

Anal. Chim. Acta 2019, 1079, 73.

[44] E. Danielson, V. Dhamodharan, A. Porkovich, P. Kumar, N. Jian, Z. Ziadi, P.

Grammatikopoulos, V. A. Sontakke, Y. Yokobayashi, M. Sowwan, Sci. Rep. 2019, 9,

17370.

[45] X. Duan, T. M. Fu, J. Liu, C. M. Lieber, Nano Today 2013, 8, 351.

[46] K. Qu, S. M. Kondengaden, J. Li, X. Wang, M. D. Sevilla, L. Li, X. Zeng, Appl. Surf. Sci.

2019, 486, 561.

[47] K. Dashtian, M. Ghaedi, S. Hajati, Biosens. Bioelectron. 2019, 132, 105.

[48] P. Mali, N. Bhattacharjee, P. C. Searson, Nano Lett. 2006, 6, 1250.

[49] K. E. Broaders, S. Grandhe, J. M. J. Fréchet, J. Am. Chem. Soc. 2011, 133, 756.

[50] S. R. Marek, N. A. Peppas, AIChE J. 2013, 59, 3578.

[51] E. C. Wu, J. H. Park, J. Park, E. Segal, F. Cunin, M. J. Sailor, ACS Nano 2008, 2, 2401.

[52] L. Lin, Y. Xu, S. Zhang, I. M. Ross, A. C. M. Ong, D. A. Allwood, ACS Nano 2013, 7,

8214.

[53] L. Lin, Y. Xu, S. Zhang, I. M. Ross, A. C. M. Ong, D. A. Allwood, Small 2014, 10, 60.

[54] S. Xu, D. Li, P. Wu, Adv. Funct. Mater. 2015, 25, 1127.

[55] A. M. Abdullah, X. Li, P. V Braun, J. A. Rogers, K. J. Hsia, Adv. Mater. 2018, 30,

1801669.

[56] J. A. Lv, Y. Liu, J. Wei, E. Chen, L. Qin, Y. Yu, Nature 2016, 537, 179.

[57] X. Xiao, J. Hu, Sci. Rep. 2016, 6, 26393.

[58] Z. Q. Liu, D. Jiao, Z. F. Zhang, Biomaterials 2015, 65, 13.

[59] I. Idziak, D. Avoce, D. Lessard, D. Gravel, X. X. Zhu, Macromolecules 1999, 32, 1260.

[60] R. Merindol, G. Delechiave, L. Heinen, L. H. Catalani, A. Walther, Nat. Commun. 2019,

10, 528.

[61] W. C. Huang, C. J. Bettinger, K. Rhee, C. J. Bettinger, Biomacromolecules 2017, 18,

1162.

[62] M. Zhang, Y. Ma, Z. Wang, Z. Han, W. Gao, Q. Zhou, Y. Gu, ACS Appl. Mater.

Interfaces 2019, 11, 5851.

[63] J. Guan, H. He, D. J. Hansford, L. J. Lee, J. Phys. Chem. B 2005, 109, 23134.

[64] N. A. Peppas, K. B. Keys, M. Torres-Lugo, A. M. Lowman, J. Control. Release 1999, 62,

81.

[65] J. H. Park, G. Von Maltzahn, L. L. Ong, A. Centrone, T. A. Hatton, E. Ruoslahti, S. N.

Bhatia, M. J. Sailor, Adv. Mater. 2010, 22, 880.

[66] C. Lu, Y. Yang, J. Wang, R. Fu, X. Zhao, L. Zhao, Y. Ming, Y. Hu, H. Lin, X. Tao, Y. Li,

W. Chen, Nat. Commun. 2018, 9, 752.

[67] T. Xu, J. Zhang, M. Salehizadeh, O. Onaizah, E. Diller, Sci. Robot. 2019, 4, eaav4494.

[68] P. Zhang, M. Behl, X. Peng, M. Y. Razzaq, A. Lendlein, Macromol. Rapid Commun.

2016, 37, 1897.

[69] E. Niiyama, K. Tanabe, K. Uto, A. Kikuchi, M. Ebara, Fibers 2019, 7, 20.

[70] X. Luo, P. T. Mather, Soft Matter 2010, 6, 2146.

[71] S. Park, N. Baugh, H. K. Shah, D. P. Parekh, I. D. Joshipura, M. D. Dickey, Adv. Sci.

2019, 6, 1901579.

[72] Q. Shen, S. Trabia, T. Stalbaum, V. Palmre, K. Kim, I. K. Oh, Sci. Rep. 2016, 6, 24462.

[73] U. N. Kumar, K. Kratz, M. Heuchel, M. Behl, A. Lendlein, Adv. Mater. 2011, 23, 4157.

[74] A. B. Laursen, S. Kegnæs, S. Dahl, I. Chorkendorff, Molybdenum sulfides - Efficient and

viable materials for electro - And photoelectrocatalytic hydrogen evolution. Energy

Environ. Sci. 2012, 5, 5577.

[75] Y. Yan, B. Xia, Z. Xu, X. Wang, Recent development of molybdenum sulfides as

advanced electrocatalysts for hydrogen evolution reaction. ACS Catal. 2014, 4, 1693.

[76] W. Qiao, S. Yan, X. Song, X. Zhang, Y. Sun, X. Chen, W. Zhong, Y. Du, RSC Adv. 2015,

5, 97696.

[77] R. A. Siegel, I. Johannes, C. A. Hunt, B. A. Firestone, Pharm. Res. An Off. J. Am. Assoc.

Pharm. Sci. 1992, 9, 76.

[78] T. S. Kelby, M. Wang, W. T. S. Huck, Adv. Funct. Mater. 2011, 21, 652.

[79] A. W. Feinberg, A. Feigel, S. S. Shevkoplyas, S. Sheehy, G. M. Whitesides, K. K. Parker,

Science (80-. ). 2007, 317, 1366.

[80] S. Zhao, P. Tseng, J. Grasman, Y. Wang, W. Li, B. Napier, B. Yavuz, Y. Chen, L.

Howell, J. Rincon, F. G. Omenetto, D. L. Kaplan, Adv. Mater. 2018, 30, 1800598.

[81] B. Mahler, V. Hoepfner, K. Liao, G. A. Ozin, J. Am. Chem. Soc. 2014, 136, 14121.

[82] C. Tan, L. Zhao, P. Yu, Y. Huang, B. Chen, Z. Lai, X. Qi, M. H. Goh, X. Zhang, S. Han,

X. J. Wu, Z. Liu, Y. Zhao, H. Zhang, Angew. Chemie - Int. Ed. 2017, 56, 7842.

[83] X. Zhang, C. L. Pint, M. H. Lee, B. E. Schubert, A. Jamshidi, K. Takei, H. Ko, A. Gillies,

R. Bardhan, J. J. Urban, M. Wu, R. Fearing, A. Javey, Nano Lett. 2011, 11, 3239.

[84] H. H. Jeong, E. Choi, E. Ellis, T. C. Lee, J. Mater. Chem. B 2019, 7, 3480.

[85] X. Liu, T. C. Tang, E. Tham, H. Yuk, S. Lin, T. K. Lu, X. Zhao, Proc. Natl. Acad. Sci. U.

S. A. 2017, 114, 2200.

[86] M. A. English, L. R. Soenksen, R. V Gayet, H. de Puig, N. M. Angenent-Mari, A. S. Mao,

P. Q. Nguyen, J. J. Collins, Science (80-. ). 2019, 365, 780.

[87] D. Hutmacher, W. Chrzanowski, Biointerfaces; Smart Materials Series; The Royal Society

of Chemistry, 2015.

[88] Q. Liu, E. Liu, J. Li, Y. Qiu, R. Chen, Catal. Today 2020, 339, 391.

[89] F. He, S. Liu, Talanta 2004, 62, 271.

[90] C. Merz, W. Knoll, M. Textor, E. Reimhult, Biointerphases 2008, 3, FA41.

[91] G. Fu, P. S. Vary, C. T. Lin, J. Phys. Chem. B 2005, 109, 8889.

[92] M. Z. Guo, T. C. Ling, C. S. Poon, In Cement and Concrete Composites; 2013; Vol. 36,

pp. 101–108.

[93] J. Rodríguez, F. Paraguay-Delgado, A. López, J. Alarcón, W. Estrada, Thin Solid Films

2010, 519, 729.

[94] O. Akhavan, M. Mehrabian, K. Mirabbaszadeh, R. Azimirad, J. Phys. D. Appl. Phys.

2009, 42, 225305.

[95] Y. Liu, X. Wang, F. Yang, X. Yang, Microporous Mesoporous Mater. 2008, 114, 431.

[96] Z. X. Lu, L. Zhou, Z. L. Zhang, W. L. Shi, Z. X. Xie, H. Y. Xie, D. W. Pang, P. Shen,

Langmuir 2003, 19, 8765.

[97] O. Akhavan, E. Ghaderi, Surf. Coatings Technol. 2010, 204, 3676.

[98] O. Akhavan, E. Ghaderi, J. Phys. Chem. C 2009, 113, 20214.

[99] A. G. Rincón, C. Pulgarin, Appl. Catal. B Environ. 2004, 49, 99.

[100] K. Thongsuriwong, P. Amornpitoksuk, S. Suwanboon, Adv. Powder Technol. 2013, 24,

275.

[101] X. Li, Y. Xing, Y. Jiang, Y. Ding, W. Li, Int. J. Food Sci. Technol. 2009, 44, 2161.

[102] K. H. Park, G. D. Han, K. C. Neoh, T. S. Kim, J. H. Shim, H. D. Park, Chem. Eng. J.

2017, 328, 988.

[103] M. Vähä-Nissi, M. Pitkänen, E. Salo, E. Kenttä, A. Tanskanen, T. Sajavaara, M.

Putkonen, J. Sievänen, A. Sneck, M. Rättö, M. Karppinen, A. Harlin, Thin Solid Films

2014, 562, 331.

[104] H. J. Choi, B. J. Park, J. H. Eom, M. J. Choi, S. G. Yoon, ACS Appl. Mater. Interfaces

2016, 8, 997.

[105] D. Thomas, J. Abraham, K. K. Sadasivuni, In AIP Conference Proceedings; 2019; Vol.

2162, p. 20166.

[106] A. Dhanalakshmi, A. Palanimurugan, B. Natarajan, Carbohydr. Polym. 2017, 168, 191.

[107] X. Wen, M. L. Schuette, S. K. Gupta, T. R. Nicholson, S. C. Lee, W. Lu, IEEE Sens. J.

2011, 11, 1726.

[108] P. J. Snyder, D. R. Lajeunesse, P. Reddy, R. Kirste, R. Collazo, A. Ivanisevic, Nanoscale

2018, 10, 11506.

[109] H. Kim, P. E. Colavita, K. M. Metz, B. M. Nichols, B. Sun, J. Uhlrich, X. Wang, T. F.

Kuech, R. J. Hamers, Langmuir 2006, 22, 8121.

[110] J. Zhang, X. Wang, X. Suo, X. Liu, B. Liu, M. Yuan, G. Wang, C. Liang, H. Shi, ACS

Nano 2019, 13, 2004.

[111] P. Wu, J. A. Imlay, J. K. Shang, Biomaterials 2010, 31, 7526.

[112] P. Carvalho, P. Sampaio, S. Azevedo, C. Vaz, J. P. Espinós, V. Teixeira, J. O. Carneiro,

Appl. Surf. Sci. 2014, 307, 548.

[113] A. M. Gallardo-Moreno, M. A. Pacha-Olivenza, L. Saldaña, C. Pérez-Giraldo, J. M.

Bruque, N. Vilaboa, M. L. González-Martín, Acta Biomater. 2009, 5, 181.

[114] V. Vadillo-Rodríguez, M. A. Pacha-Olivenza, M. L. Gõnzalez-Martín, J. M. Bruque, A.

M. Gallardo-Moreno, J. Biomed. Mater. Res. - Part A 2013, 101 A, 1397.

[115] S. Perni, P. Prokopovich, Soft Matter 2013, 9, 1844.

[116] R. Müller, S. Ruhl, K. A. Hiller, G. Schmalz, H. Schweikl, J. Biomed. Mater. Res. - Part

A 2008, 84, 817.

[117] J. Männik, R. Driessen, P. Galajda, J. E. Keymer, C. Dekker, Proc. Natl. Acad. Sci. U. S.

A. 2009, 106, 14861.

[118] P. Amézaga-Madrid, R. Silveyra-Morales, L. Córdoba-Fierro, G. V. Nevárez-Moorillón,

M. Miki-Yoshida, E. Orrantia-Borunda, F. J. Solís, J. Photochem. Photobiol. B Biol. 2003,

70, 45.

[119] S. N. Pleskova, I. S. Golubeva, Y. K. Verevkin, Mater. Sci. Eng. C 2016, 59, 807.

[120] A. V. Gulyuk, D. R. Lajeunesse, R. Collazo, A. Ivanisevic, Langmuir 2018, 34, 10806.

[121] A. V Gulyuk, D. R. LaJeunesse, P. Reddy, R. Kirste, R. Collazo, A. Ivanisevic, ACS Appl.

Electron. Mater. 2019, 1, 1641.

[122] A. Cochis, B. Azzimonti, C. Della Valle, E. De Giglio, N. Bloise, L. Visai, S. Cometa, L.

Rimondini, R. Chiesa, Biomaterials 2016, 80, 80.

[123] L. G. Harris, S. Tosatti, M. Wieland, M. Textor, R. G. Richards, Biomaterials 2004, 25,

4135.

[124] T. P. T. Cushnie, P. K. J. Robertson, S. Officer, P. M. Pollard, R. Prabhu, C. McCullagh,

J. M. C. Robertson, J. Photochem. Photobiol. A Chem. 2010, 216, 290.

[125] H. Hu, W. Zhang, Y. Qiao, X. Jiang, X. Liu, C. Ding, Acta Biomater. 2012, 8, 904.

[126] S.-M. Kang, H.-N. Lee, H.-E. Kim, B.-I. Kim, J. Dent. Hyg. Sci. 2014, 14, 319.

[127] X. Wang, J. C. Yu, H. Y. Yip, L. Wu, P. K. Wong, S. Y. Lai, Chem. - A Eur. J. 2005, 11,

2997.

[128] A. Devigne, L. Meyer, C. B. de la Tour, N. Eugénie, S. Sommer, P. Servant, DNA Repair

(Amst). 2019, 73, 144.

[129] H. Lu, H. Chen, G. Xu, A. M. U. H. Shah, Y. Hua, DNA Repair (Amst). 2012, 11, 139.

[130] S. Chatterjee, N. Biswas, A. Datta, P. K. Maiti, PLoS One 2019, 14, e0214192.

[131] A. B. Tesler, P. Kim, S. Kolle, C. Howell, O. Ahanotu, J. Aizenberg, Nat. Commun. 2015,

6, 8649.

[132] I. Banerjee, R. C. Pangule, R. S. Kane, Antifouling coatings: Recent developments in the

design of surfaces that prevent fouling by proteins, bacteria, and marine organisms. Adv.

Mater. 2011, 23, 690.

[133] L. Cai, D. Wu, J. Xia, H. Shi, H. Kim, Sci. Total Environ. 2019, 671, 1101.

[134] C. Falcón García, F. Stangl, A. Götz, W. Zhao, S. A. Sieber, M. Opitz, O. Lieleg,

Biomater. Sci. 2019, 7, 220.

[135] O. Rzhepishevska, S. Hakobyan, R. Ruhal, J. Gautrot, D. Barbero, M. Ramstedt,

Biomater. Sci. 2013, 1, 589.

[136] K. Anselme, P. Davidson, A. M. Popa, M. Giazzon, M. Liley, L. Ploux, The interaction of

cells and bacteria with surfaces structured at the nanometre scale. Acta Biomater. 2010, 6,

3824.

[137] L. Badihi Hauslich, M. N. Sela, D. Steinberg, G. Rosen, D. Kohavi, Clin. Oral Implants

Res. 2013, 24, 49.

[138] X. L. Hu, B. Ho, C. T. Lim, C. S. Hsu, J. Dent. Res. 2011, 90, 1451.

[139] E. Estephan, C. Larroque, F. J. G. Cuisinier, Z. Bálint, C. Gergely, J. Phys. Chem. B 2008,

112, 8799.

[140] T. Il Kim, J. G. McCall, Y. H. Jung, X. Huang, E. R. Siuda, Y. Li, J. Song, Y. M. Song,

H. A. Pao, R. H. Kim, C. Lu, S. D. Lee, I. S. Song, G. Shin, R. Al-Hasani, S. Kim, M. P.

Tan, Y. Huang, F. G. Omenetto, J. A. Rogers, M. R. Bruchas, Science (80-. ). 2013, 340,

211.

[141] G. Shin, A. M. Gomez, R. Al-Hasani, Y. R. Jeong, J. Kim, Z. Xie, A. Banks, S. M. Lee, S.

Y. Han, C. J. Yoo, J. L. Lee, S. H. Lee, J. Kurniawan, J. Tureb, Z. Guo, J. Yoon, S. Il

Park, S. Y. Bang, Y. Nam, M. C. Walicki, V. K. Samineni, A. D. Mickle, K. Lee, S. Y.

Heo, J. G. McCall, T. Pan, L. Wang, X. Feng, T. il Kim, J. K. Kim, Y. Li, Y. Huang, R.

W. Gereau, J. S. Ha, M. R. Bruchas, J. A. Rogers, Neuron 2017, 93, 509.

[142] T. Huang, NNIN REU Res. Accompl. 2013, 218.

[143] Y. Dai, T. Sun, Z. Zhang, Z. J. Zhang, J. Li, Biointerphases 2017, 12, 11002.

[144] K. Sunada, Y. Kikuchi, K. Hashimoto, A. Fujishima, Environ. Sci. Technol. 1998, 32,

726.

[145] K. Sunada, T. Watanabe, K. Hashimoto, J. Photochem. Photobiol. A Chem. 2003, 156,

227.

[146] D. Iyer, A. V Gulyuk, P. Reddy, R. Kirste, R. Collazo, D. R. Lajeunesse, A. Ivanisevic,

ACS Appl. Bio Mater. 2019, 2, 4044.

[147] D. Perera-Costa, J. M. Bruque, M. L. González-Martín, A. C. Gómez-García, V. Vadillo-

Rodríguez, Langmuir 2014, 30, 4633.

[148] D. J. Balazs, K. Triandafillu, Y. Chevolot, B. O. Aronsson, H. Harms, P. Descouts, H. J.

Mathieu, Surf. Interface Anal. 2003, 35, 301.

[149] R. R. Maddikeri, S. Tosatti, M. Schuler, S. Chessari, M. Textor, R. G. Richards, L. G.

Harris, J. Biomed. Mater. Res. - Part A 2008, 84, 425.

[150] Y. J. Oh, N. R. Lee, W. Jo, W. K. Jung, J. S. Lim, Ultramicroscopy 2009, 109, 874.

[151] K. Manabe, S. Nishizawa, S. Shiratori, ACS Appl. Mater. Interfaces 2013, 5, 11900.

[152] M. R. Nejadnik, H. C. van der Mei, W. Norde, H. J. Busscher, Biomaterials 2008, 29,

4117.

[153] X. Ding, C. Yang, T. P. Lim, L. Y. Hsu, A. C. Engler, J. L. Hedrick, Y. Y. Yang,

Biomaterials 2012, 33, 6593.

[154] K. Shatalin, E. Shatalina, A. Mironov, E. Nudler, Science (80-. ). 2011, 334, 986.

[155] R. Bayston, L. E. Fisher, K. Weber, Biomaterials 2009, 30, 3167.

[156] A. Ghalayani Esfahani, B. Lazazzera, L. Draghi, S. Farè, R. Chiesa, L. De Nardo, F. Billi,

J. Appl. Microbiol. 2019, 126, 87.

[157] M. Taillefert, J. S. Beckler, E. Carey, J. L. Burns, C. M. Fennessey, T. J. DiChristina, J.

Inorg. Biochem. 2007, 101, 1760.

[158] C. Liu, Q. Zhao, Langmuir 2011, 27, 9512.

[159] M. Simões, M. O. Pereira, M. J. Vieira, Water Res. 2005, 39, 5142.

[160] S. H. Flint, J. D. Brooks, P. J. Bremer, J. Food Eng. 2000, 43, 235.

[161] X. Wang, S. Meng, J. Han, J. Microbiol. 2017, 55, 619.

[162] D. R. Absolom, The role of bacterial hydrophobicity in infection: Bacterial adhesion and

phagocytic ingestion. Can. J. Microbiol. 1988, 34, 287.

[163] K. Colville, N. Tompkins, A. D. Rutenberg, M. H. Jericho, Langmuir 2010, 26, 2639.

[164] L. Ploux, S. Beckendorff, M. Nardin, S. Neunlist, Colloids Surfaces B Biointerfaces 2007,

57, 174.

[165] T. Cao, H. Tang, X. Liang, A. Wang, G. W. Auner, S. O. Salley, K. Y. S. Ng, Biotechnol.

Bioeng. 2006, 94, 167.

[166] D. G. Davies, A. M. Chakrabarty, G. G. Geesey, Appl. Environ. Microbiol. 1993, 59,

1181.

[167] R. García-Contreras, B. Pérez-Eretza, E. Lira-Silva, R. Jasso-Chávez, R. Coria-Jiménez,

A. Rangel-Vega, T. Maeda, T. K. Wood, Pathog. Dis. 2014, 70, 95.

[168] S. Hou, H. Gu, C. Smith, D. Ren, Langmuir 2011, 27, 2686.

[169] X. Xin, X. Jin, Y. Wang, J. Yuan, J. Shen, Mater. Lett. 2018, 218, 186.

[170] R. Wang, K. G. Neoh, E. T. Kang, J. Colloid Interface Sci. 2015, 438, 138.

[171] N. Saha, C. Monge, V. Dulong, C. Picart, K. Glinel, Biomacromolecules 2013, 14, 520.

[172] C. Zhu, N. R. Bao, S. Chen, J. N. Zhao, Appl. Surf. Sci. 2016, 389, 7.

[173] K. Shellenberger, B. E. Logan, Environ. Sci. Technol. 2002, 36, 184.

[174] L. Nan, W. Yang, Y. Liu, H. Xu, Y. Li, M. Lu, K. Yang, J. Mater. Sci. Technol. 2008, 24,

197.

[175] J. Li, W. Liu, X. Zhang, P. K. Chu, K. M. C. Cheung, K. W. K. Yeung, Mater. Today

2019, 22, 35.

[176] L. G. Harris, R. G. Richards, J. Mater. Sci. Mater. Med. 2004, 15, 311.

[177] Y. Liu, J. Wen, Y. Gao, T. Li, H. Wang, H. Yan, B. Niu, R. Guo, Appl. Surf. Sci. 2018,

436, 624.

[178] W. R. Bowen, A. S. Fenton, R. W. Lovitt, C. J. Wright, Biotechnol. Bioeng. 2002, 79,

170.

[179] T. Cao, H. Tang, X. Liang, A. Wang, G. W. Auner, S. O. Salley, K. Y. S. Ng, In Methods

in Molecular Biology; 2011; 736, 379.

[180] M. P. DeLisa, J. J. Valdes, W. E. Bentley, J. Bacteriol. 2001, 183, 2918.

[181] F. Song, H. Koo, D. Ren, Effects of material properties on bacterial adhesion and biofilm

formation. J. Dent. Res. 2015, 94, 1027.

[182] R. S. Friedlander, H. Vlamakis, P. Kim, M. Khan, R. Kolter, J. Aizenberg, Proc. Natl.

Acad. Sci. 2013, 110, 5624 LP.

[183] A. Chebolu, B. Laha, M. Ghosh, Nagahanumaiah, Micro Nano Lett. 2013, 8, 280.

[184] C. Fabret, V. A. Feher, J. A. Hoch, Two-component signal transduction in Bacillus

subtilis: How one organism sees its world. J. Bacteriol. 1999, 181, 1975.

[185] K. Papenfort, B. L. Bassler, Quorum sensing signal-response systems in Gram-negative

bacteria. Nat. Rev. Microbiol. 2016, 14, 576.

[186] D. Beier, R. Gross, Regulation of bacterial virulence by two-component systems. Curr.

Opin. Microbiol. 2006, 9, 143.

[187] Y. Gotoh, Y. Eguchi, T. Watanabe, S. Okamoto, A. Doi, R. Utsumi, Two-component

signal transduction as potential drug targets in pathogenic bacteria. Curr. Opin. Microbiol.

2010, 13, 232.

[188] P. L. Foster, Stress-induced mutagenesis in bacteria. Crit. Rev. Biochem. Mol. Biol. 2007,

42, 373.

[189] J. E. Patrick, D. B. Kearns, Swarming motility and the control of master regulators of

flagellar biosynthesis. Mol. Microbiol. 2012, 83, 14.

[190] O. Uziel, I. Borovok, R. Schreiber, G. Cohen, Y. Aharonowitz, J. Bacteriol. 2004, 186,

326.

[191] O. Vidal, R. Longin, C. Prigent-Combaret, C. Dorel, M. Hooreman, P. Lejeune, J.

Bacteriol. 1998, 180, 2442.

[192] T. R. Aunins, K. A. Eller, C. M. Courtney, M. Levy, S. M. Goodman, P. Nagpal, A.

Chatterjee, ACS Biomater. Sci. Eng. 2019, 5, 4206.

[193] K. Yamamoto, A. Ishihama, Mol. Microbiol. 2005, 56, 215.

[194] M. Palma, D. DeLuca, S. Worgall, L. E. N. Quadri, J. Bacteriol. 2004, 186, 248.

[195] T. C. Zahrt, V. Deretic, J. Bacteriol. 2000, 182, 3832.

[196] V. Sperandio, A. G. Torres, J. B. Kaper, Mol. Microbiol. 2002, 43, 809.

[197] K. Perron, O. Caille, C. Rossier, C. Van Delden, J. L. Dumas, T. Köhler, J. Biol. Chem.

2004, 279, 8761.

[198] I. Marchand, L. Damier-Piolle, P. Courvalin, T. Lambert, Antimicrob. Agents Chemother.

2004, 48, 3298.

[199] D. S. Cayley, H. J. Guttman, M. T. Record, Biophys. J. 2000, 78, 1748.

[200] A. Manara, G. Dalcorso, C. Baliardini, S. Farinati, D. Cecconi, A. Furini, J. Proteome

Res. 2012, 11, 4169.

[201] E. V Demidova, T. N. Goryachkovskaya, I. A. Mescheryakova, T. K. Malup, A. I.

Semenov, N. A. Vinokurov, N. A. Kolchanov, V. M. Popik, S. E. Peltek, IEEE Trans.

Terahertz Sci. Technol. 2016, 6, 435.

[202] W. R. Schwan, J. L. Lee, F. A. Lenard, B. T. Matthews, M. T. Beck, Infect. Immun. 2002,

70, 1391.

[203] G. P. Munson, D. L. Lam, F. W. Outten, T. V O’Halloran, J. Bacteriol. 2000, 182, 5864.

[204] R. Lange, C. Wagner, A. De Saizieu, N. Flint, J. Molnos, M. Stieger, P. Caspers, M.

Kamber, W. Keck, K. E. Amrein, Gene 1999, 237, 223.

[205] H. D. Kulasekara, I. Ventre, B. R. Kulasekara, A. Lazdunski, A. Filloux, S. Lory, Mol.

Microbiol. 2005, 55, 368.

[206] M. Kleerebezem, L. E. N. Quadri, O. P. Kuipers, W. M. De Vos, Mol. Microbiol. 1997,

24, 895.

[207] D. J. Kim, S. Forst, Microbiology 2001, 147, 1197.

[208] S. M. Moskowitz, R. K. Ernst, S. I. Miller, J. Bacteriol. 2004, 186, 575.

[209] I. A. MacDonald, M. J. Kuehna, J. Bacteriol. 2013, 195, 2971.

[210] S. Fraud, A. J. Campigotto, Z. Chen, K. Poole, Antimicrob. Agents Chemother. 2008, 52,

4478.

[211] L. Friedman, R. Kolter, J. Bacteriol. 2004, 186, 4457.

[212] M. Hobbs, E. S. R. Collie, P. D. Free, S. P. Livingston, J. S. Mattick, Mol. Microbiol.

1993, 7, 669.

[213] A. M. Luján, A. J. Moyano, R. A. Martino, S. Feliziani, M. Urretavizcaya, A. M. Smania,

Environ. Mol. Mutagen. 2019, 60, 594.

[214] Y. Kawai, R. A. Daniel, J. Errington, Mol. Microbiol. 2009, 71, 1131.

[215] J. Mostertz, C. Scharf, M. Hecker, G. Homuth, Microbiology 2004, 150, 497.

[216] Y. Weinrauch, R. Penchev, E. Dubnau, E. Smith, D. Dubnau, Genes Dev. 1990, 4, 860.

[217] M. J. Horsburgh, M. O. Clements, H. Crossley, E. Ingham, S. J. Foster, Infect. Immun.

2001, 69, 3744.

[218] K. C. Quon, G. T. Marczynski, L. Shapiro, Cell 1996, 84, 83.

[219] S. Narayanan, L. Kumar, S. K. Radhakrishnan, Biochim. Biophys. Acta - Gene Regul.

Mech. 2018, 1861, 952.

[220] S. I. Nakayama, H. Watanabe, J. Bacteriol. 1998, 180, 3522.

[221] M. Li, C. Wang, Y. Feng, X. Pan, G. Cheng, J. Wang, J. Ge, F. Zheng, M. Cao, Y. Dong,

D. Liu, J. Wang, Y. Lin, H. Du, G. F. Gao, X. Wang, F. Hu, J. Tang, PLoS One 2008, 3,

e2080.

[222] K. Filcek, K. Vielfort, S. Muraleedharan, J. Henriksson, R. H. Valdivia, P. M. Bavoil, B.

S. Sixt, PLoS One 2019, 14, e0224324.

[223] E. Pagán-Ramos, J. Song, M. McFalone, M. H. Mudd, V. Deretic, J. Bacteriol. 1998, 180,

4856.

[224] H. Shimomura, S. Hayashi, K. Yokota, K. Oguma, Y. Hirai, FEMS Microbiol. Lett. 2006,

237, 407.

[225] K. A. Cornell, W. E. Swarts, R. D. Barry, M. K. Riscoe, Biochem. Biophys. Res. Commun.

1996, 228, 724.

[226] T. H. Han, J. H. Lee, M. H. Cho, T. K. Wood, J. Lee, Res. Microbiol. 2011, 162, 108.

[227] L. Ponsonnet, M. Boureanu, N. Jaffrezic, A. Othmane, C. Dorel, P. Lejeune, Mater. Sci.

Eng. C 2008, 28, 896.

[228] D. Georgellis, O. Kwon, E. C. C. Lin, Science (80-. ). 2001, 292, 2314.

[229] J. D. Partridge, C. Scott, Y. Tang, R. K. Poole, J. Green, J. Biol. Chem. 2006, 281, 27806.

[230] J. H. Chou, J. T. Greenberg, B. Demple, J. Bacteriol. 1993, 175, 1026.

[231] S. Kim, J. Ha, H. Lee, S. Lee, J. Lee, Y. Choi, H. Oh, Y. Yoon, K. H. Choi, J. Food Prot.

2019, 82, 1278.

[232] J. D. Winkler, C. Garcia, M. Olson, E. Callaway, K. C. Kao, Appl. Environ. Microbiol.

2014, 80, 3729.

[233] K. A. Sochacki, I. A. Shkel, M. T. Record, J. C. Weisshaar, Biophys. J. 2011, 100, 22.

[234] G. Grass, C. Rensing, J. Bacteriol. 2001, 183, 2145.

[235] K. Moon, S. Gottesman, Mol. Microbiol. 2009, 74, 1314.

[236] J. Serment-Guerrero, M. Breña-Valle, M. Aguilar-Moreno, M. Balcázar, Appl. Radiat.

Isot. 2012, 71, 66.

[237] G. Gao, B. Tian, L. Liu, D. Sheng, B. Shen, Y. Hua, DNA Repair (Amst). 2003, 2, 1419.

[238] S. Jordan, M. I. Hutchings, T. Mascher, Cell envelope stress response in Gram-positive

bacteria. FEMS Microbiol. Rev. 2008, 32, 107.

[239] K. Poole, Stress responses as determinants of antimicrobial resistance in Gram-negative

bacteria. Trends Microbiol. 2012, 20, 227.

[240] R. M. Shelake, D. Pramanik, J. Y. Kim, Evolution of plant mutagenesis tools: a shifting

paradigm from random to targeted genome editing. Plant Biotechnol. Rep. 2019, 13, 423.

[241] J. F. Watson, J. García-Nafría, J. Biol. Chem. 2019, 294, 15271.

[242] A. Rondelet, A. Pozniakovsky, M. Leuschner, I. Poser, A. Ssykor, J. Berlitz, N. Schmidt,

A. A. Hyman, A. W. Bird, bioRxiv 2019, 1, 844282.

[243] Q. Yu, Y. Li, B. Wu, W. Hu, M. He, G. Hu, Novel mutagenesis and screening

technologies for food microorganisms: advances and prospects. Appl. Microbiol.

Biotechnol. 2020, 104, 1517.

[244] D. E. Nelson, K. D. Young, J. Bacteriol. 2000, 182, 1714.

[245] F. Mika, R. Hengge, Genes Dev. 2005, 19, 2770.

[246] K. Yamamoto, K. Hirao, T. Oshima, H. Aiba, R. Utsumi, A. Ishihama, J. Biol. Chem.

2005, 280, 1448.

[247] A. Coornaert, A. Lu, P. Mandin, M. Springer, S. Gottesman, M. Guillier, Mol. Microbiol.

2010, 76, 467.

[248] K. Nishino, A. Yamaguchi, J. Bacteriol. 2001, 183, 1455.

[249] M. Kleerebezem, W. Crielaard, J. Tommassen, EMBO J. 1996, 15, 162.

[250] X. Li, X. Liu, Nanoscale 2017, 9, 7320.

[251] B. Piro, G. Mattana, S. Reisberg, Biosensors 2018, 8, 65.

[252] E. Katz, Biocomputing - tools, aims, perspectives. Curr. Opin. Biotechnol. 2015, 34, 202.

[253] M. L. Simpson, G. S. Sayler, J. T. Fleming, B. Applegate, Whole-cell biocomputing.

Trends Biotechnol. 2001, 19, 317.

[254] C. N. Bedbrook, K. K. Yang, A. J. Rice, V. Gradinaru, F. H. Arnold, PLoS Comput. Biol.

2017, 13.

[255] R. W. Bradley, M. Buck, B. Wang, J. Mol. Biol. 2016, 428, 862.

[256] M. Amos, I. M. Axmann, N. Blüthgen, F. de la Cruz, A. Jaramillo, A. Rodriguez-Paton, F.

Simmel, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2015, 373, 20140218.

[257] J. Zhang, J. Huang, C. Say, R. L. Dorit, K. T. Queeney, J. Colloid Interface Sci. 2018,

519, 203.

[258] B. Ramalingam, T. Parandhaman, S. K. Das, ACS Appl. Mater. Interfaces 2016, 8, 4963.

[259] O. Purcell, T. K. Lu, Synthetic analog and digital circuits for cellular computation and

memory. Curr. Opin. Biotechnol. 2014, 29, 146.

[260] R. Lahoz-Beltra, J. Navarro, P. C. Marijuán, Bacterial computing: A form of natural

computing and its applications. Front. Microbiol. 2014, 5.

[261] S. Rolsma, D. W. Frank, J. T. Barbieri, S. Rolsma, D. W. Frank, J. T. Barbieri, In The

Comprehensive Sourcebook of Bacterial Protein Toxins; Elsevier, 2015; 160.

[262] W. Wu, Y. Jin, F. Bai, S. Jin, In Molecular Medical Microbiology; Elsevier, 2015; 767.

[263] J. Yayan, B. Ghebremedhin, K. Rasche, PLoS One 2015, 10, e0139836.

[264] R. E. W. Hancock, Clin. Infect. Dis. 1998, 27, S93.

[265] N. Mitik-Dineva, J. Wang, V. K. Truong, P. Stoddart, F. Malherbe, R. J. Crawford, E. P.

Ivanova, Curr. Microbiol. 2009, 58, 268.

[266] P. Amézaga-Madrid, R. Silveyra-Morales, L. Córdoba-Fierro, G. Nevarez-Moorillon, M.

Miki-Yoshida, E. Borunda, F. J. P. Solis, TEM evidence of ultrastructural alteration on

Pseudomonas aeruginosa by photocatalytic TiO2 thin films; 2003; 70, 45.

[267] K. Sauer, A. K. Camper, G. D. Ehrlich, J. W. Costerton, D. G. Davies, J. Bacteriol. 2002,

184, 1140.

[268] N. C. Caiazza, J. H. Merritt, K. M. Brothers, G. A. O’Toole, J. Bacteriol. 2007, 189, 3603.

[269] A. Touhami, M. H. Jericho, J. M. Boyd, T. J. Beveridge, J. Bacteriol. 2006, 188, 370.

[270] S. L. Kuchma, G. A. O’Toole, Curr. Opin. Biotechnol. 2000, 11, 429.

[271] L. Kashefi-Kheyrabadi, A. Noori, M. A. Mehrgardi, Application of Bioconjugated

Nanoporous Gold Films in Electrochemical Biosensors; 2014, 11, 345.

[272] G. Gupta, V. Rajendran, P. Atanassov, Electroanalysis 2003, 15, 1577.

[273] G. López Muñoz, M. ‐Carme. Estévez, M. Vázquez‐García, M. Berenguel Alonso, J.

Alonso‐Chamarro, A. Homs Corbera, L. Lechuga, Gold/Silver/Gold Trilayer Films On

Nanostructured Polycarbonate Substrates For Direct And Label‐Free Nanoplasmonic

Biosensing; 2018, 115, 5344.

[274] G. Roberts, Van Nostrand’s Scientific Encyclopedia; 2005.

[275] A. Vilan, O. Yaffe, A. Biller, A. Salomon, A. Kahn, D. Cahen, Adv. Mater. 2010, 22, 140.

[276] A. Lidow, J. Strydom, M. de Rooij, D. Reusch, GaN Transistors Effic. Power Convers.

2014, 266.

[277] H. Jia, L. Guo, W. Wang, H. Chen, Adv. Mater. 2009, 21, 4641.

[278] G. A. Garrett, P. Rotella, H. Shen, M. Wraback, D. A. Haeger, R. B. Chung, N. Pfaff, E.

C. Young, S. P. Denbaars, J. S. Speck, D. A. Cohen, Phys. Status Solidi Basic Res. 2012,

249, 507.

[279] L. E. Bain, R. Kirste, C. A. Johnson, H. T. Ghashghaei, R. Collazo, A. Ivanisevic, Mater.

Sci. Eng. C 2016, 58, 1194.

[280] P. J. Snyder, R. Kirste, R. Collazo, A. Ivanisevic, RSC Adv. 2016, 6, 97873.

[281] H. E. Lee, J. Choi, S.-H. Lee, M. Jeong, J. Ho Shin, D. Joe, D. Kim, C. Wan Kim, J. H.

Park, J. Hee Lee, D. Kim, C. Shin, J. Lee, Monolithic Flexible Vertical GaN Light‐

Emitting Diodes for a Transparent Wireless Brain Optical Stimulator; 2018, 55, 454.

[282] F. Templier, J. Soc. Inf. Disp. 2016, 24, 669.

[283] M. A. Curiel, N. Nedev, D. Nesheva, J. Soares, R. Haasch, M. Sardela, B. Valdez, B.

Sankaran, E. Manolov, I. Bineva, I. Petrov, Mater. Sci. Eng. B 2010, 174, 132.

[284] C. Youtsey, I. Adesida, L. T. Romano, G. Bulman, Appl. Phys. Lett. 1998, 72, 560.

[285] R. Collazo, S. Mita, A. Aleksov, R. Schlesser, Z. Sitar, In Journal of Crystal Growth;

2006, 287, 586.

[286] J. A. Méndez, J. A. Aznárez, J. L. Sacedón, M. Aguilar, Phys. Rev. B - Condens. Matter

Mater. Phys. 1999, 60, 2720.

[287] H. Hoffmann, J. Vancea, Thin Solid Films 1981, 85, 147.

[288] M. Aguilar, A. I. Oliva, P. Quintana, J. L. Pen˜a, Surf. Sci. 1997, 380, 91.

[289] R. D. Boyd, J. Verran, M. V. Jones, M. Bhakoo, Langmuir 2002, 18, 2343.

[290] L. C. Hsu, J. Fang, D. A. Borca-Tasciuc, R. W. Worobo, C. I. Moraru, Appl. Environ.

Microbiol. 2013, 79, 2703.

[291] H. A. Foster, D. W. Sheel, P. Sheel, P. Evans, S. Varghese, N. Rutschke, H. M. Yates, J.

Photochem. Photobiol. A Chem. 2010, 216, 283.

[292] J. Yayan, B. Ghebremedhin, K. Rasche, PLoS One 2015, 10, e0139836.

[293] J. A. Lemire, J. J. Harrison, R. J. Turner, Antimicrobial activity of metals: Mechanisms,

molecular targets and applications. Nat. Rev. Microbiol. 2013, 11, 371.

[294] D. H. K. Nguyen, V. T. H. Nguyen, V. K. Truong, I. Sbarski, J. Wang, A. Balčytis, S.

Juodkazis, D. E. Mainwaring, R. Crawford, E. Ivanova, Role of topological scale in the

differential fouling of Pseudomonas aeruginosa and Staphylococcus aureus bacterial cells

on wrinkled gold-coated polystyrene surfaces; 2018; 10, 5089.

[295] H. Cao, H. Qin, Y. Zhao, G. Jin, T. Lu, F. Meng, X. Zhang, X. Liu, Sci. Rep. 2016, 6.

[296] S. Rudakowski, http://www.circuitnet.com. pp. 1–6.

[297] D. Hook, J. A. Olhausen, J. Krim, M. Dugger, Evaluation of Oxygen Plasma and UV

Ozone Methods for Cleaning of Occluded Areas in MEMS Devices; 2011; 19, 1292.

[298] P. Cumpson, N. Sano, Metrologia 2013, 50, 27.

[299] T. Smith, J. Colloid Interface Sci. 1980, 75, 51.

[300] S. B. Brachetti-Sibaja, M. A. Dom??nguez-Crespo, A. M. Torres-Huerta, E. Onofre-

Bustamante, W. De La Cruz-Hern??ndez, J. Mex. Chem. Soc. 2014, 58, 393.

[301] K. M. Tracy, W. J. Mecouch, R. F. Davis, R. J. Nemanich, J. Appl. Phys. 2003, 94, 3163.

[302] C. M. Foster, R. Collazo, Z. Sitar, A. Ivanisevic, Langmuir 2013, 29, 216.

[303] N. Kruse, S. Chenakin, Appl. Catal. A Gen. 2011, 391, 367.

[304] J. F. Moulder, J. Electron Spectros. Relat. Phenomena 2018, 669, 418.

[305] V. G. Antunes, C. A. Figueroa, F. Alvarez, Appl. Surf. Sci. 2018, 448, 502.

[306] A. Moldovan, F. Feldmann, G. Krugel, M. Zimmer, J. Rentsch, M. Hermle, A. Roth-

Fölsch, K. Kaufmann, C. Hagendorf, In Energy Procedia; 2014; 55, 834.

[307] D. E. King, J. Vac. Sci. Technol. A Vacuum, Surfaces, Film. 1995, 13, 1247.

[308] Y. F. Dufrêne, Curr. Opin. Microbiol. 2003, 6, 317.

[309] M. Baclayon, W. H. Roos, G. J. L. Wuite, Mol. Cell. Proteomics 2010, 9, 1678.

[310] G. Longo, L. Rio, R. Charles, A. Trampuz, A. Bizzini, G. Dietler, S. Kasas, Force volume

and stiffness tomography investigation on the dynamics of stiff material under bacterial

membranes; 2012; 25, 278.

[311] T. J. Günther, M. Suhr, J. Raff, K. Pollmann, RSC Adv. 2014, 4, 51156.

[312] X. García-Massó, M. Huber, J. Friedmann, L. M. González, S. Schiller, J. L. Toca-

Herrera, Automated detection of protein unfolding events in atomic force microscopy force

curves; 2016; 79, 345.

[313] J. Arfsten, S. Leupold, C. Bradtmöller, I. Kampen, A. Kwade, Colloids Surfaces B

Biointerfaces 2010, 79, 284.

[314] M. Baniasadi, Z. Xu, L. Gandee, Y. Du, H. Lu, P. Zimmern, M. Minary, Nanoindentation

of Pseudomonas aeruginosa bacterial biofilm using atomic force microscopy; 2014;1,

045411.

[315] I. E Ivanov, E. N Kintz, L. A Porter, J. B Goldberg, N. Burnham, T. Camesano, Relating

the Physical Properties of Pseudomonas aeruginosa Lipopolysaccharides to Virulence by

Atomic Force Microscopy; 2011; 193, 1259.

[316] C. J. Sullivan, S. Venkataraman, S. T. Retterer, D. P. Allison, M. J. Doktycz,

Ultramicroscopy 2007, 107, 934.

[317] D. Sicard, S. Cecioni, M. Iazykov, Y. Chevolot, S. E. Matthews, J.-P. Praly, E.

Souteyrand, A. Imberty, S. Vidal, M. Phaner-Goutorbe, Chem. Commun. 2011, 47, 9483.

[318] A. Coutable, C. Thibault, J. Chalmeau, J. M. FrancÌois, C. Vieu, V. Noireaux, E.

Trévisiol, Langmuir 2014, 30, 3132.

[319] A. Kulp, M. Kuehn, Biological Functions and Biogenesis of Secreted Bacterial Outer

Membrane Vesicles; 2010; 64, 163.

[320] E. Jauvert, E. Palleau, E. Dague, L. Ressier, ACS Appl. Mater. Interfaces 2014, 6, 21230.

[321] M. A. Beckmann, S. Venkataraman, M. J. Doktycz, J. P. Nataro, C. J. Sullivan, J. L.

Morrell-Falvey, D. P. Allison, Ultramicroscopy 2006, 106, 695.

[322] V. Vadillo-Rodríguez, B. E. Logan, Environ. Sci. Technol. 2006, 40, 2983.

[323] R. He, I. Di Bonaventura, R. Visini, B.-H. Gan, Y. Fu, D. Probst, A. Luscher, T. Kohler,

C. van Delden, A. Stocker, W. Hong, T. Darbre, J.-L. Reymond, Chem. Sci. 2017, 8,

7464.

[324] A. Atabek, T. A. Camesano, J. Bacteriol. 2007, 189, 8503.

[325] I. Gammoudi, M. Mathelie-guinlet, F. Morote, L. Beven, D. Moynet, C. Grauby-heywang,

T. Cohen-bouhacina, Morphological and nanostructural surface changes in Escherichia

coli over time, monitored by atomic force microscopy. Colloids Surfaces B Biointerfaces

2016, 141, 355.

[326] V. Vadillo-Rodriguez, S. R. Schooling, J. R. Dutcher, J. Bacteriol. 2009, 191, 5518.

[327] A. Mularski, J. J. Wilksch, E. Hanssen, R. A. Strugnell, F. Separovic, Biochim. Biophys.

Acta - Biomembr. 2016, 1858, 1091.

[328] E. Takai, K. D. Costa, A. Shaheen, C. T. Hung, X. E. Guo, Ann. Biomed. Eng. 2005, 33,

963.

[329] M. Guragain, M. M. King, K. S. Williamson, A. C. Pérez-Osorio, T. Akiyama, S.

Khanam, M. A. Patrauchan, M. J. Franklin, J. Bacteriol. 2016, 198, 951.

[330] S. A. Sarkisova, S. R. Lotlikar, M. Guragain, R. Kubat, J. Cloud, M. J. Franklin, M. A.

Patrauchan, PLoS One 2014, 9.

[331] D. C. Dominguez, Calcium signalling in bacteria. Mol. Microbiol. 2004, 54, 291–297.

[332] M. Guragain, D. L. Lenaburg, F. S. Moore, I. Reutlinger, M. A. Patrauchan, Cell Calcium

2013, 54, 350.

[333] I. Atwater, C. M. Dawson, B. Ribalet, E. Rojas, J. Physiol. 1979, 288, 575.

[334] M. R. Duchen, P. A. Smith, F. M. Ashcroft, Biochem. J. 1993, 294, 35.

[335] S. Ramstad, C. M. Futsaether, A. Johnsson, J. Photochem. Photobiol. B Biol. 1997, 40,

141.

[336] C. S. Lindestam Arlehamn, V. Pétrilli, O. Gross, J. Tschopp, T. J. Evans, J. Biol. Chem.

2010, 285, 10508.

[337] F. Kaess, S. Mita, J. Xie, P. Reddy, A. Klump, L. H. Hernandez-Balderrama, S.

Washiyama, A. Franke, R. Kirste, A. Hoffmann, R. Collazo, Z. Sitar, J. Appl. Phys. 2016,

120, 062102.

[338] J. Hudzicki, Kirby-Bauer disk diffusion susceptibility test protocol. … -Kirby-Bauer-Disk-

Diffusion-Susceptibility-Test-Protocol. 2009, 1.

[339] C. Decker, Polym. Int. 1998, 45, 133.

[340] K. Esumi, A. Suzuki, N. Aihara, K. Usui, K. Torigoe, Langmuir 2002, 14, 3157.

[341] K. K. Pandey, Polym. Degrad. Stab. 2005, 90, 9.

[342] J. Gassan, V. S. Gutowski, In Composites Science and Technology; 2000; 60, 2857.

[343] A. Oláh, H. Hillborg, G. J. Vancso, Appl. Surf. Sci. 2005, 239, 410.

[344] M. Li, K. G. Neoh, E. T. Kang, T. Lau, E. Chiong, Adv. Funct. Mater. 2014, 24, 1631.

[345] E. A. Sosnin, E. Stoffels, M. V. Erofeev, I. E. Kieft, S. E. Kunts, IEEE Trans. Plasma Sci.

2004, 32, 1544.

[346] L. Parmegiani, A. Accorsi, G. E. Cognigni, S. Bernardi, E. Troilo, M. Filicori, Fertil.

Steril. 2010, 94, 1525.

[347] S. Iwaguch, K. Matsumura, Y. Tokuoka, S. Wakui, N. Kawashima, Colloids Surfaces B

Biointerfaces 2002, 25, 299.

[348] D. J. Riley, V. Bavastrello, U. Covani, A. Barone, C. Nicolini, Dent. Mater. 2005, 21,

756.

[349] I. A. Ansari, A. K. Datta, Food Bioprod. Process. 2003, 81, 57.

[350] J. A. Guerrero-Beltrán, G. V. Barbosa-Cánovas, Review: Advantages and limitations on

processing foods by UV light. Food Sci. Technol. Int. 2004, 10, 137.

[351] M. Mori, A. Hamamoto, A. Takahashi, M. Nakano, N. Wakikawa, S. Tachibana, T.

Ikehara, Y. Nakaya, M. Akutagawa, Y. Kinouchi, Med. Biol. Eng. Comput. 2007, 45,

1237.

[352] N. G. Reed, The history of ultraviolet germicidal irradiation for air disinfection. Public

Health Rep. 2010, 125, 15.

[353] G. Williams, B. Seger, P. V. Kamt, ACS Nano 2008, 2, 1487.

[354] R. F. Howe, M. Grätzel, J. Phys. Chem. 1987, 91, 3906.

[355] D. Zhang, S. M. Dougal, M. S. Yeganeh, Langmuir 2000, 16, 4528.

[356] N. Tohge, K. Shinmou, T. Minami, J. Sol-Gel Sci. Technol. 1994, 2, 581.

[357] M. Ouyang, C. Yuan, R. J. Muisener, A. Boulares, J. T. Koberstein, Chem. Mater. 2000,

12, 1591.

[358] C. Decker, Polym. Int. 2002, 51, 1141.

[359] A. Lendlein, H. Jiang, O. Junger, R. Langer, O. Jünger, R. Langer, Nature 2005, 434, 879.

[360] M. Awokola, W. Lenhard, H. Löffler, C. Flosbach, P. Frese, Prog. Org. Coatings 2002,

44, 211.

[361] N. Reddy, R. Reddy, Q. Jiang, Crosslinking biopolymers for biomedical applications.

Trends Biotechnol. 2015, 33, 362.

[362] B. K. Lee, J. J. Kim, Curr. Appl. Phys. 2009, 9, 404.

[363] Y. Y. Noh, D. Y. Kim, K. Yase, J. Appl. Phys. 2005, 98, 043501.

[364] P. J. Snyder, P. Reddy, R. Kirste, D. R. LaJeunesse, R. Collazo, A. Ivanisevic, RSC Adv.

2018, 8, 36722.

[365] P. J. Snyder, R. Kirste, R. Collazo, A. Ivanisevic, Small 2017, 13, 1700481.

[366] Y. V. Lee, B. Tian, Nano Lett. 2019, 19, 2189.

[367] Y. Cheng, G. Feng, C. I. Moraru, Micro-and nanotopography sensitive bacterial

attachment mechanisms: A review. Front. Microbiol. 2019, 13, 8475.

[368] P. J. Snyder, R. Kirste, R. Collazo, A. Ivanisevic, RSC Adv. 2016, 6, 97873.

[369] P. Feng, I. Mönch, S. Harazim, G. Huang, Y. Mei, O. G. Schmidt, Nano Lett. 2009, 9,

3453.

[370] S. Dilger, M. Wessig, M. R. Wagner, J. S. Reparaz, C. M. Sotomayor Torres, L. Qijun, T.

Dekorsy, S. Polarz, Cryst. Growth Des. 2014, 14, 4593.

[371] C. Chen, W. Ma, J. Zhao, Semiconductor-mediated photodegradation of pollutants under

visible-light irradiation. Chem. Soc. Rev. 2010, 39, 4206.

[372] X. Chen, S. Shen, L. Guo, S. S. Mao, Chem. Rev. 2010, 110, 6503.

100

[373] A. P. West, I. E. Brodsky, C. Rahner, D. K. Woo, H. Erdjument-Bromage, P. Tempst, M.

C. Walsh, Y. Choi, G. S. Shadel, S. Ghosh, Nature 2011, 472, 476.

[374] X.-H. Yao, H. Min, Z.-M. Lv, Biomed. Environ. Sci. 2006, 19, 309.

[375] A. Ray Chowdhuri, S. Tripathy, S. Chandra, S. Roy, S. K. Sahu, RSC Adv. 2015, 5,

49420.

[376] D. Ortiz de Orué Lucana, I. Wedderhoff, M. R. Groves, J. Signal Transduct. 2011, 2012,

[377] M. P. Brynildsen, J. A. Winkler, C. S. Spina, I. C. MacDonald, J. J. Collins, Nat.

Biotechnol. 2013, 31, 160.

[378] M. D. Zurbriggen, N. Carrillo, M.-R. Hajirezaei, Plant Signal. Behav. 2010, 5, 393.

[379] T. Nyström, Arch. Microbiol. 2001, 176, 159.

[380] P. Díaz-Rosales, M. Chabrillón, S. Arijo, E. Martinez-Manzanares, M. A. Moriñigo, M. C.

Balebona, J. Fish Dis. 2006, 29, 355.

[381] N. J. Wood, J. Sørensen, FEMS Microbiol. Ecol. 2001, 38, 53.

[382] I. Kobayashi, T. Tamura, H. Sghaier, I. Narumi, S. Yamaguchi, K. Umeda, K. Inagaki, J.

Biosci. Bioeng. 2006, 101, 315.

[383] R. Kirste, S. Mita, L. Hussey, M. P. Hoffmann, W. Guo, I. Bryan, Z. Bryan, J. Tweedie, J.

Xie, M. Gerhold, R. Collazo, Z. Sitar, Appl. Phys. Lett. 2013, 102, 133517.

[384] F. Liu, R. Collazo, S. Mita, Z. Sitar, G. Duscher, S. J. Pennycook, Appl. Phys. Lett. 2007,

91, 261106.

[385] M. Macvanin, D. Hughes, In Antibiotic Resistance Protocols: Second Edition; Gillespie,

S. H.; McHugh, T. D., Eds.; Humana Press: Totowa, NJ, 2010; 95.

[386] G. C. Johnston, J. R. Pringle, L. H. Hartwell, Exp. Cell Res. 1977, 105, 79

101

[387] M. Werner-Washburne, E. Braun, G. C. Johnston, R. A. Singer, Stationary phase in the

yeast Saccharomyces cerevisiae. Microbiol. Rev. 1993, 57, 383.

[388] G. G. Perrone, S. X. Tan, I. W. Dawes, Reactive oxygen species and yeast apoptosis.

Biochim. Biophys. Acta - Mol. Cell Res. 2008, 1783, 1354.

[389] P. Moradas-Ferreira, V. Costa, Adaptive response of the yeast Saccharomyces cerevisiae

to reactive oxygen species: Defences, damage and death. Redox Rep. 2000, 5, 277.

102

APPENDICES

103

Appendix A

GENERAL ABBREVIATIONS

(abbreviations not related to a particular chapter)

AFM – Atomic force microscopy CNT – Carbon nanotubes DI – Deionized DMEM – Dulbecco’s Modified Eagle Medium F.U. – Fluorescence units (fluorescence arbitrary units) ICP-MS – Inductively coupled plasma mass spectrometry KPFM – Kelvin probe force microscopy ML – Machine Learning MOCVD – Metalorganic chemical vapor deposition NP – Nanoparticle (less than 100nm in diameter) OD – Optical density PBS – Phosphate-buffered saline medium RMS – Root-mean-square ROS – Reactive oxygen species SEM – Scanning electron microscopy SOS – Salt overly sensitive (signaling pathway) UV – Ultraviolet XPS – X-ray photoelectron spectroscopy YPD – Yeast extract peptone medium

104