A Molecular Simulation Study of CO2 Adsorption in Metal-Organic Frameworks

A molecular simulation study of CO2 adsorption in metal-organic frameworks

Pan YANG Master of Philosophy (Engineering)

Supervisor: Dr Qinghua Zeng Co-Supervisor: Dr Kejun Dong

School of Computing, Engineering and Mathematics Western Sydney University Sydney, Australia

May 2018

ACKNOWLEDGEMENTS

I would like to thank my principal supervisor Dr Qinghua Zeng for offering me the opportunity to do the molecular dynamics research on metal-organic frameworks. I really appreciate his patience, enthusiasm, encouragement and broad knowledge. I was continuously advised and supported by his guidance in all the time of research timeframe and writing of this thesis.

I am also grateful for the comments and feedbacks given by my co-supervisor Dr Kejun Dong during my 2-year research period and my thesis writing.

Also, I appreciate many friends and visiting scholars for their insightful views and valuable suggestions.

Lastly, thanks to the research service team of SCEM and HDR, Western Sydney University for organising conferences and providing administrative and technical support.

ABSTRACT

The increasing carbon dioxide density in the atmosphere has led to the global warming and other environmental issues. Such increase in carbon dioxide comes mainly from the combustion of thousands of tons of fossil fuels (coal, oil and natural gas). Thus, the development of novel materials for CO2 capture, separation and sequestration is becoming critically essential. Many materials including aqueous amine solvent, micro and mesoporous solid substances have been extensively investigated for CO2 absorption/adsorption. It is found that quite a lot of distinct metal-organic frameworks (MOFs) have remarkable CO2 adsorption capacity in room temperature, particularly HKUST-1 and MIL-68(In). HKUST-1 (Hong Kong University of Science and Technology) is a MOF made up of copper nodes with 1,3,5-benzenetricarboxylic acid struts between them. MIL-68(In) is an indium-based MOF made up of the chains of InO4(OH)2 octahedral units which are interconnected with terephthalate ligands to form central triangle and hexagon channels. To understand the micro- mechanisms and hence to further improve their adsorption capacities, grand canonical Monte

Carlo and molecular dynamics simulations have been employed in this study to explore CO2 adsorption uptake. The results show that both fluorinated HKUST-1 and MIL-68(In) have a superior CO2 adsorption performance than their unmodified structures. In addition, radial distribution function and mean square displacement analysis methods have been carried out to explore the CO2 adsorption sites and the self-diffusion within MOFs. The results indicate that CO2 molecules in HKUST-1 are more likely to attach to the metal sites with the pressure increase, however, the adsorbed CO2 in HKUST-1F and MIL-68F (In) are bound firmly to fluoride atoms, reducing their mobility.

Keywords: HKUST-1, MIL-68(In), fluorination, CO2 adsorption, diffusion, molecular dynamics simulation

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LIST OF FIGURES

Figure 1. Brief concept of carbon dioxide capture and storage/sequestration (CCS) (Lee & Park 2015)...... 5

Figure 2. General reaction mechanisms for the chemical absorption of CO2 in amine-based solvent (Bosoaga, Masek & Oakey 2009; Rochelle 2009)...... 7

Figure 3. (a) Solid adsorption mechanisms, (b) Adsorption process in packed beds, and (c) Adsorption process in fluidized beds (Yu, Huang & Tan 2012)...... 8

Figure 4. (a) Cu-BTC (dry) (b) 4wt. % hydrated Cu-BTC. Orange, red, and grey spheres represent Cu, O, and C atoms, respectively. The oxygen atom of the coordinated water molecule is omitted in blue (Yazaydın et al. 2009)...... 15

Figure 5. Periodic boundary condition...... 19

Figure 6. Schematic diagram of a basic MD code(Zeng, Yu & Lu 2010)...... 21

Figure 7. Mean square displacement...... 22

Figure 8. Radial distribution function...... 23

Figure 9. The snapshot of a CO2 adsorbed equilibrium MOF structure: CO2 adsorbed MOF

(upper), isolated MOF (lower left) and isolated adsorbed CO2 molecules (lower right)...... 24

Figure 10. Unit cells of HKUST-1 (a) and HKUST-1F (b), Colour: Copper in orange, oxygens in red, carbons in grey, hydrogen in white and fluorides are light blue...... 27

Figure 11. Adsorption isotherm of CO2 in HKUST-1 and HKUST-1F at 298K under the pressure of from 0.1 to 5bar...... 29

Figure 12. The CO2 (red spots) adsorption snapshot in HKUST-1, 1 bar (left) and 5 bar (right)...... 30

Figure 13. The CO2 (red spots) adsorption snapshot in HKUST-1F, 0.1 bar (left) and 5 bar (right)...... 31

Figure 14. Radial distribution function of atomic pairs from HKUST-1 and CO2 at 298 K and different pressure: (a) 0.1 bar, (b) 0.5 bar, (c) 1 bar (d) 3 bar and (e) 5 bar, respectively...... 34

Figure 15. Radial distribution function of atomic pairs from HKUST-1F and CO2 at 298 K and different pressure: (a) 0.1 bar, (b) 0.5 bar, (c) 1 bar (d) 3 bar and (e) 5 bar, respectively. 37

Figure 16. Mean square displacement over time of CO2 in HKUST-1and HKUST-1F...... 38

Figure 17. Unit cells of MIL-68 (a) and MIL-68F (In) (b), Colour: Indium in dark red, oxygens in red, carbons in grey, hydrogen in white and fluorides are light blue...... 41

Figure 18. Adsorption isotherm of CO2 in MIL-68(In) and MIL-68F (In) at 298K under the pressure of from 0.1 to 5 bar...... 43

Figure 19. Comparison of CO2 adsorption isotherms of CO2 in HKUST-1 and MIL-68(In). 44

Figure 20. The CO2 (red spots) adsorption snapshot of MIL-68(In), 0.1 bar (left) and 5 bar (right)...... 45

Figure 21. The CO2 (red spots) adsorption snapshot of MIL-68F (In), 0.1 bar (left) and 5 bar (right)...... 45

Figure 22. The radial distribution function for atomic pairs of MIL-68 and CO2 at 298 K and different pressure: (a) 0.1 bar, (b) 0.5 bar, (c) 1 bar (d) 3 bar and (e) 5 bar, respectively...... 48

Figure 23. Radial distribution function of atomic pairs from MIL-68F and CO2 at 298 K and different pressure: (a) 0.1 bar, (b) 0.5 bar, (c) 1 bar (d) 3 bar and (e) 5 bar, respectively...... 51

Figure 24. Mean squared displacement over time of CO2 in MIL-68(In) and MIL-68F (In). . 52

LIST OF TABLES

Table 1 The Atomic partial charge q (e) and force field of HKUST-1 and HKUST-1F ...... 28

Table 2 CO2 adsorption capacity on HKUST-1(Cu-BTC) under different conditions ...... 29

Table 3 Interaction potential energies (Kcal/mol) of CO2 adsorbed in HKUST-1 ...... 39

Table 4 Interaction potential energies (Kcal/mol) of CO2 adsorbed in HKUST-1F...... 39

Table 5 The Atomic partial charge q (e) and force field of MIL-68(In) and MIL-68F (In) ... 41

Table 6 Interaction potential energies (Kcal/mol) of CO2 adsorbed in MIL-68 (In) ...... 53

Table 7 Interaction potential energies (Kcal/mol) of CO2 adsorbed in MIL-68F (In)...... 53

LIST OF PUBLICATIONS

Conference paper: A molecular dynamics of CO2 adsorption in fluoridised cations and non- fluoridised HKUST-1. 1st International Conference on Mineral Engineering and Materials Science (iCMEMS2017), 20-22 Nov 2017, Sydney, Australia.

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TABLE OF CONTENTS

DECLARATION ...... i

ACKNOWLEDGEMENTS ...... ii

ABSTRACT ...... iii

LIST OF FIGURES ...... iv

LIST OF TABLES ...... vi

LIST OF PUBLICATIONS ...... vii

CHAPTER 1 INTRODUCTION ...... 1

1.1 Research background and objectives ...... 1

1.2 Thesis outline ...... 2

CHAPTER 2 LITERATURE SURVEY ...... 3

2.1 Global warming and carbon emissions ...... 3

2.2 Carbon dioxide capture and storage ...... 4

2.3 Amine absorption for CO2 emissions ...... 5

2.4 Metal-organic frameworks for CO2 adsorption and separation ...... 7

2.4.1 Experimental work for CO2 capture on MOFs ...... 9

2.4.2 Molecular simulations for CO2 capture on MOFs ...... 11

2.5 Current research summary and challenges ...... 14

CHAPTER 3 METHODOLOGY ...... 16

3.1 Input parameters...... 16

3.1.1 Atomic partial charge ...... 16

3.1.2 Force field ...... 17

3.2 Ensemble ...... 18

3.3 Periodic boundary condition and cut-off distance ...... 18

3.4 Principle of Grand Canonical Monte Carlo simulation ...... 19

3.5 Principle of molecular dynamics simulation...... 20

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3.6 Molecular dynamics analysis methods ...... 21

3.6.1 Mean square displacement ...... 22

3.6.2 Radial distribution function ...... 22

3.6.3 Adsorption strength ...... 23

3.7 Summary ...... 25

CHAPTER 4 CARBON DIOXIDE ADSORPTION IN HKUST-1 ...... 26

4.1 Model configuration...... 26

4.2 Grand Canonical Monte Carlo Simulation details ...... 27

4.2.1 Adsorption isotherms of CO2 in HKUST-1 and HKUST-1F ...... 28

4.2.2 Comparison of CO2 adsorption isotherms of CO2 in HKUST-1 ...... 29

4.3 Molecular dynamics simulation results ...... 30

4.3.1 Adsorption sites of CO2 in HKUST-1 and HKUST-1F ...... 30

4.3.2 Self-diffusivity of CO2 in HKUST-1 and HKUST-1F ...... 37

4.3.3 Interaction strength between CO2 and HKUST-1 ...... 38

4.4 Summary ...... 39

CHAPTER 5 CARBON DIOXIDE ADSORPTION IN MIL-68(In) ...... 40

5.1 Model configuration...... 40

5.2 Grand Canonical Monte Carlo Simulation details ...... 41

5.2.1 Adsorption isotherms of CO2 in MIL-68(In) and MIL-68F (In) ...... 42

5.2.2 Comparison of CO2 adsorption isotherms of CO2 in HKUST-1 and MIL-68(In) ...... 43

5.3 Molecular dynamics simulation results ...... 44

5.3.1 Adsorption sites of CO2 in MIL-68(In) and MIL-68F (In) ...... 44

5.3.2 Self-diffusivity of CO2 in MIL-68(In) and MIL-68F (In) ...... 51

5.3.3 Interaction strength between CO2 and MIL-68(In) ...... 52

5.4 Summary ...... 53

CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS ...... 54

6.1 Conclusions ...... 54

6.2 Recommendations on future work ...... 55

REFERENCES ...... 56

CHAPTER 1 INTRODUCTION

1.1 Research background and objectives

The global energy demand is increasingly being driven by the population boom and economic growth (Krausmann et al. 2009). Fossil fuels (e.g. coal, natural gas and oil) are playing a significant role in meeting such enormous demand since industrialization revolution; on the other hand, the combustion of fossil fuels is contributing to tonnes of greenhouse gases emissions (Quadrelli & Peterson 2007). The rising level of CO2 concentration in the atmosphere is one of the greatest environmental concerns facing our civilization today. This presents an urgent need for carbon dioxide emissions being mitigated from the thermoelectric power plants and other industrial manufactures (Cormos 2014).

Aqueous amine solvents are currently used in some power generation plants for CO2 capture and scrubbing, which could reduce the carbon emissions from the exhausted streams

(Rochelle, GT 2009). However, this technique is recognised to be energy intensive and will lead to a sharp decrease in plant efficiency (Rao & Rubin 2002). In order to lower the energy penalty, various porous solid materials (e.g. zeolites, metal oxides, activated carbons as well as metal-organic frameworks) have been synthesized and probed (Yu, Huang, & Tan 2012).

Among them, MOFs, emerging as a novel type of porous solid adsorbents, have attracted many research interests in the past two decades because of their outstanding physical and chemical properties for gas adsorption and separation (Yazaydin et al. 2009), For instance, the highest CO2 uptake of MOF-177 is reported to be 147 wt. % (Millward & Yaghi 2005).To date, there are more than 20,000 distinct MOFs being reported for the control of CO2 emissions (Banerjee 2012; Saha et al. 2012).

Great efforts have been made by the conventional experiments on CO2 capture and separation in MOFs such as MOF-5, MIL-101 and MOF-177 (Banerjee 2012; Liu, Wang & Zhou 2012; Samanta et al. 2012). However, it is impractical to synthesize and screen hundreds of MOFs

1 in laboratory, especially the MOFs having specific pore size/shape and functional groups (Torrisi, Bell & Mellot-Draznieks 2010). Moreover, the direct observations in laboratory of

CO2 diffusion and other dynamic properties are extremely difficult to achieve (Salles et al. 2008). In contrast, with the ever-growing computational technique, molecular simulations (e.g. Monte Carlo and molecular dynamic simulation) have become a more favorable method to understand the mechanisms of MOFs for gas capture or separation, particularly for CO2 adsorption performance and diffusion behavior (Sabouni, Kazemian & Rohani 2014).

The present work aims to investigate CO2 adsorption capacities in HKUST-1 and MIL-68(In) as well as their fluorinated structures (HKUST-1F and MIL-68F (In)) , identify CO2 binding sites and self-diffusion in these four MOFs and calculate adsorption energy between MOFs and CO2 via a combined grand canonical Monte Carlo (GCMC) & molecular dynamics (MD) simulations.

1.2 Thesis outline

The layout of this thesis is described as follows: Chapter 1 briefly introduces the background information and objectives; Chapter 2 reviews the development of techniques and materials for CO2 capture and separation, particularly in MOFs; Chapter 3 consists of theories of computational simulations and analysis methods; Chapter 4 presents the grand canonical

Monte Carlo and molecular dynamics simulations results of CO2 adsorption in HKUST-1 and its fluorinated structure (HKUST-1F); In Chapter 5 discusses grand canonical Monte Carlo and molecular dynamics simulations results of CO2 adsorption in MIL-68(In) and MIL-68F (In); Chapter 6 summarises the present outcomes, and some recommendations for future work are also proposed.

CHAPTER 2 LITERATURE SURVEY

2.1 Global warming and carbon emissions

The global energy demand is predicted to surge significantly in line with world population growth and the urbanization development. The emissions of greenhouse gases from the combustion streams of fossil fuels are playing a critical role in leading to global warming which makes our human beings endangered and the entire planet impaired (Achten, Almeida & Muys 2013; Lee et al. 2013). Unfortunately, due to the huge energy need for economic growth and industrialisation in more and more countries, these emissions are expected to have a non-stop increase in the short-to medium-term (Bains, Psarras & Wilcox 2017; Manan et al. 2017). What’s more, it seems unfeasible in the near future relying on some new resources or renewable energies, e.g. wind, solar, tidal, wave and offshore powers to meet the enormous energy demand. Therefore, fossil fuels will remain to be an overwhelming energy provider to fulfil the ever-growing global energy needs (Cherp et al. 2016). It is estimated that about 86% of greenhouse gas emissions are from the combustion of fossil fuels. In Australia, over 70% of the energy demand is contributed by the primary resources, particularly by coal and natural gas (Quadrelli & Peterson 2007). Moreover, among the exhausted combustion streams (e.g.,CO2, CO, NOx, SOX, etc.), CO2 contributes to over 60% of global warming and greenhouse gas effect (Bains, Psarras & Wilcox 2017). In 2012, statistics from World Metrological Organisation (WMO) alarmed that the concentration of

CO2 in the atmosphere had already crept up to 393.1 ppm (Fang et al. 2015; Fang et al. 2014). In April 2015, the United States National Oceanic and Atmospheric Administration (NOAA) announced that carbon dioxide concentration exceeded 400 ppm in the atmosphere and would reach 550 ppm by the year of 2050 (Schlör, Fischer & Hake 2015; Vujic & Lyubartsev

2017). Besides, the increased CO2 concentration in the atmosphere plays a negative effect on the radiative balance of the Earth leading to the destruction of the ecosystems (Barcza 2017; Jackson et al. 2017). The global warming leads to floods, droughts, heat waves as well as sea level rise (AghaKouchak et al. 2014; Levin et al. 2012). As a result, the economic loss due to climate change is predicted up to be 20% of the world’s gross domestic product (GDP) (Mohor & Mendiondo 2017; Takakura et al. 2017).

2.2 Carbon dioxide capture and storage

Owing to the concerns of the atmospheric carbon density and urgent needs for the carbon dioxide emissions mitigation, a wide range of technologies and strategies have been developed and promoted to combat the anthropogenic CO2 contributions from power plants and other industries (Boot-Handford et al. 2014; Pietzcker et al. 2014). Among them, carbon dioxide capture and storage/sequestration (CCS) is believed to be prospective for carbon emissions reduction, it was reported that the CCS technology could help to control approximately one third of the global CO2 emissions. The Intergovernmental Panel on Climate Change (IPCC) has established a number of academic and practical collaborations and programmes promoting the greenhouse emission reduction. IPCC estimates that the CCS applications can reduce up to 80% of CO2 emissions in the coal-fired thermoelectric power plants. Furthermore, the European Union (EU) predicts that there will be 30 billion tons of

CO2 captured and stored by CCS to the year of 2050-2060 (Azar et al. 2010; Shackley & Verma 2008). In general, CCS technique embodies a group of techniques, as shown in Figure 1: (I)

Compression of CO2, e.g., absorbing or adsorbing the carbon dioxide gases from the thermoelectric plants or other chemical industrials by aimed-based, alkaline solvents or advanced porous solid materials (Cormos 2012; de Coninck, Stephens & Metz 2009). (II)

Transportation of CO2, utilising the pipeline or ship to freight the captured or compressed

CO2 gases to the facilities for storage (Chrysostomidis et al. 2009; Jung et al. 2013). (III)

CO2 permanent storage, e.g. marine reserve in the deep sea bed, carbonates solidification underground deposit (oilfield and basins) and the storage post-management such as CO2 storage monitoring and observation (Eldevik et al. 2009; Randolph & Saar 2011). In addition,

CCS also embraces other strategies to reduce CO2 emissions, such as improving energy combustion efficiency and developing alternative powers (Hussain et al. 2013; Kowalczyk et al. 2013). In summary, the development of CCS schemes is of great importance in removing anthropogenic CO2 emissions and stabilizing carbon density in the atmosphere, CCS is a direct and feasible manner to ease the global warming and environmental degradation. Although efforts have been made, challenges still exist due to the different policies in developed and developing countries and difficulties of global collaborations on CO2 emissions reduction(Benson et al. 2012; Gibbins & Chalmers 2008) .

Figure 1. Brief concept of carbon dioxide capture and storage/sequestration (CCS) (Lee & Park 2015).

2.3 Amine absorption for CO2 emissions

Among the schemes of CCS, carbon dioxide capture technology is the most critical aspect which accounts for approximately 75% of the total cost. Furthermore, the regeneration energy requirement in the capture phase is regarded as the most key factor for the CCS technology commercialization. The improvement of CO2 capture will play a significant role in cutting down the total cost of CCS in near-term (Chu 2009). Since the thermoelectric power plants contribute to the largest proportion (nearly 45%) of the global carbon emissions, the CO2 capture from the post-combustion streams will have greatest potential for reducing the greenhouse gas emissions (Drage et al. 2012). In practical, aqueous amine absorbents have been employed industrially to scrub the post- combustion gaseous streams for over 50 years. This technique has been currently equipped in the coal or gas-fired electricity plants for the removal of CO2 and other acid gases such as

CO, NOX and SOx. This process involves the chemical reaction between aqueous amines and

CO2 to form bicarbonate, carbamate or carbonate under different conditions (e.g. temperature, H2O and PH), as shown in Figure 2 (Bosoaga, Masek & Oakey 2009; Rochelle 2009). And the regeneration of the amine-based adsorbents normally occurs at elevated

5 temperatures (approximately 373-403 K) to release the CO2 gases (D'Alessandro, Smit & Long 2010). The primary alkanol-amine, monoethanolamine (MEA) is one of the most used amine solvents utilized for CO2 capture industrially. A typical commercial process allow MEA absorption solution (25-30 wt.%) to react with exhausted gas streams to form the amine-CO2 liquid, then this amine-CO2 liquid will be pumped to stripper tower and heated at high temperature to regenerate the MEA and desorb the CO2 (Mangalapally et al. 2009). More recently, the secondary amines such as diethanolamine(DEA) and triethanolamine (TEA) , the tertiary amine N-mythyl-diethanolamine (MDEA) as well as other amine mixtures are also successfully implemented in the industrial manufactures to reduce the post-combustion

CO2 emissions (D'Alessandro, Smit & Long 2010; Garcia-Abuin, Gomez-Diaz & Navaza 2014; Pinto et al. 2014). The apparent benefit of aqueous amines is that the technology has been maturely and commercially equipped at a large number of power plants and other industrials for a couple of years, the extensive experiences of this technique have been obtained. However, there still exists a great deal of drawbacks and limitations using this technique such as relatively low

CO2 absorption capacity, the low mass transfer and reaction kinetics due to slow reaction between CO2 and solvent, chemical degradation caused by other by-products (e.g. NOx and SOx), corrosion and oxidative issues and, most importantly, the high energy demand of the regeneration process to recycle the amines and liberate CO2 gases at elevated temperature will sharply increase the total energy requirement by 25-40% of a power generation plant which is equivalent to an additional CO2 capture cost by $50-60/tonne (Abanades, Rubin & Anthony 2004; Le Moullec & Kanniche 2011; Lucquiaud & Gibbins 2011; Rochelle, G et al. 2011). Although advances and efforts have been made to overcome the limitations such as the low absorption capacity, for example, the MDEA possess a higher CO2 loading capacity compared with MEA, the significant energy penalties still present a great challenge for amine absorption technique.

Figure 2. General reaction mechanisms for the chemical absorption of CO2 in amine- based solvent (Bosoaga, Masek & Oakey 2009; Rochelle 2009).

2.4 Metal-organic frameworks for CO2 adsorption and separation

Given the obvious disadvantages of the amine-based solvent for post-combustion CO2 capture, especially the intensive energy penalties for regeneration process, research attempts have been turned on the diverse range of solid adsorbents for CO2 adsorption and separation. A great many of studies and papers have highlighted the advances and advantages of solids adsorbents for gas adsorption and separation, particularly the solid adsorbents have striking benefits such as low energy requirement in the regeneration process and chemical stability compared with aqueous amines (Abanades, Rubin & Anthony 2004; Samanta et al. 2012; Yu, CH, Huang, CH & Tan, CS 2012). The adsorption mechanism of solid adsorbents typically involves weak van der Waals (vdW) forces for physisorption, stronger covalent bonding for chemisorption or, in some cases, a hybrid sorption in packed or fluidized beds as shown in Figure 3 (Yu, C-H, Huang, C-H &

Tan, C-S 2012). The adsorption and removal (desorption) of CO2 is achieved by three main cycles, temperature swing adsorption (TSA), pressure swing adsorption (PSA) and vacuum swing adsorption (VSA). In practical, TSA and PAS approaches are often combined to accomplish the adsorption process by heating/increasing the temperature and reducing/lowing the pressure to recycle the adsorbents and liberate CO2 (Li et al. 2013; Liu, Wang & Zhou 2012; Nugent et al. 2013; Yu, Huang & Tan 2012).

Figure 3. (a) Solid adsorption mechanisms, (b) Adsorption process in packed beds, and (c) Adsorption process in fluidized beds (Yu, Huang & Tan 2012).

In general, the adsorption capacity is a one of the most significant factors evaluating the performance of solid adsorbents which describes the number or the amount of the gases can be captured in adsorbent. In addition to adsorption capacity, the affinity strength (bonding or packing interactions) and kinetic effect (diffusion or transport behaviors) are simultaneously playing a critical role in characterizing and assessing the adsorbents (Li, Kuppler & Zhou 2009).To date, tones of thousands of solid adsorbents which are microporous or mesoporous materials such as zeolites (e.g. zeolite 5A and 13X), silica materials, metal oxides (e.g. CaO and MgO), hydrotalcite-like compounds (HTLCs), carbon or carbonaceous adsorbents (e.g. MAXSORB) as well as metal-organic frameworks (MOFs)have been extensively considered and investigated for CO2 capture and separation under different conditions. However, there are some distinct limitations for these solid physical adsorbents. Firstly, the weak affinity and relatively low adsorption capacity such as activated carbons and carbon fibers. Secondly, the degradation phenomenon of the adsorbents, for example, the CaO is easily to degrade after several carbonation-calcination cycles at high temperature. Thirdly, zeolites and silica materials are moisture sensitive which is unavailable for wet streams. Lastly, due to the complex topology of HTLCs compounds and silica materials as well as the poor thermal stability, these adsorbents confront great challenges for recycling and renewal in the regeneration step. Thus, recent research attentions have been drawn to overcome these shortcomings (Alhamami, Doan & Cheng 2014; Chałupnik et al. 2013; Miller, Akbar & Morris 2014; Rossi, Campos & Souza 2016; Seema et al. 2014; Thommes & Cychosz 2014).

In the past two decades, metal-organic frameworks (MOFs), emerging as a new type of progressive materials, have attracted a great deal of research interests for gas adsorption and separation (Li, Y-W et al. 2017). A large variety of MOFs have been studied and reported in the journals and literatures on the basis of their outstanding structural diversities (e.g. pore size/shape and geometries), high surface area, prominent thermal stability, moisture insensitivity and distinct functionalization (Furukawa et al. 2013; Hu et al. 2014; Li, Kuppler & Zhou 2009). In general, MOFs are microporous crystalline substances with uniform pore size in the range of 3-20 Å which can be controlled and tailored. They are typically three- dimensional coordination networks consisting of inorganic nodes (metal-based single node or clusters) and surrounded by specific functional organic ligands or linkers (e.g. carboxylate, tribenzoate, imidazolate and pyridyl) (Maurin et al. 2017; Tudisco et al. 2016; Zhang, S et al. 2016). Today, MOFs materials have been widely investigated and explored for the gas adsorption and separation (e.g. H2, CH4, N2 and CO2), which presents both opportunities and challenges (Hu et al. 2014; Zhang, Z et al. 2013).

2.4.1 Experimental work for CO2 capture on MOFs

Due to the distinct advantages and favourable properties such as high void volume, remarkable thermal stability and low crystal density, MOFs have become one of the most prospective research candidates for gas capture and adsorption, particularly for small gas molecules. Intensive systematic research programmes have been implemented for the CO2 on MOFs both experimentally and numerically (Li et al. 2013; Li et al. 2015; Shi et al. 2014). The great progresses have been achieved in the last two decades for MOFs’ design, synthesis and characterization. Basically, MOFs includes two main categories: rigid and flexible, rigid MOFs often have stable pores such as MOF-177 and MOF-5, while the structure of flexible MOFs like MIL-53 and MIL-47 will have a great change during the adsorption process. Experimental test is recognized as the most direct approach to evaluate the adsorption performance of the MOFs by the observation of equilibrium isotherms. To date, several synthetic approaches have been significantly developed and a large scale of MOFs have been synthesized and explored for CO2 adsorption and separation in laboratories. (Canivet et al. 2014; Ghosh, Colon & Snurr 2014; Xian et al. 2015)

MOFs usually have exceptional CO2 adsorption capacity compared with zeolites and activated carbons due to the outstanding pore/surface properties, for example, the surface area of activated carbon is 400-1000 m2/g and zeolite of 1500 m2/g maximum, whereas MOFs has

2 a typical surface area of 1500-4500 m /g. As a result, a superior CO2 adsorption of MOF-177 with a surface area of 4746 m2/g was 147 wt. % which was reported by Yaghi’s team (Millward & Yaghi 2005). Moreover, It was demonstrated that MOF-210 (surface area 6240 m2/g) and MOF-200 (surface area 4530 m2/g) which are both zinc-based MOFs illustrated carbon dioxide uptake of 74.2 wt. % and 73.9 wt. %, respectively at 298K under a high pressure (50 bar) (Furukawa et al. 2010). In addition to the optimal pore/surface properties, the effects of open metal site in many MOFs have been extensively exploited. Wu and co-workers studied Mg-MOF-74 and

HKUST-1, they reported that both MOFs had a striking CO2 adsorption capacity which may probably attribute by the strong electrostatic interaction due to the correlatively unsaturated Mg and Cu cations (Wu et al. 2010). Millward and co-workers demonstrated that MOF-177 4+ contains open Zn metal sites could reach an excellent CO2 uptake which was much larger other adsorbents such as zeolite 13X and MAXSORB (activated carbon) (Hao, Li & Lu 2011).Furthermore, MIL-100 and MIL-101, containing unsaturated Cr3+ sites, revealed a superior uptake of CO2 over CH4 by the difference of (quadruple) polarity/ non-polarity, the mechanism involves a coordination of O=C=O and Cr3+ metal centre (Llewellyn et al. 2008).

Thus, the open metal site in MOFs is of great importance for CO2 adsorption exploration. More interestingly, some flexible MOFs possess a unique “gate-opening” or “breathing” phenomenon during the adsorption process; these MOFs usually have a small amount of adsorption capacity at the low pressure but show a sudden increase after a threshold pressure , temperature or gas loading (Alhamami, Doan & Cheng 2014; Millange et al. 2008). MIL-53 series are favourable candidates for the study of CO2 adsorption due to their phase transformation. For example, MIL-53(Cr) had an inferior CO2 uptake under 5bar, however, a remarkable adsorption increase in CO2 uptake occurred as the narrow-pore (NP) transformed to be large-pore (LP) (Hamon, L. et al. 2009). Similarly, the “gate” effect Kitagawa and co- workers illustrated that Cu (pyrdc) (bpp) (pyrdc=pyridine-2, 3-dicarboxylate, bpp=1, 3-bis (4- pyridyI) propane) had a distinct CO2 uptake increase at a comparatively low pressure(Maji, Ohba & Kitagawa 2005). Additionally, the chemical modification and functionalization on MOFs have simultaneously been investigated and probed to understand the CO2 adsorption capacity enhancement in recent years.(Feijani, Tavasoli & Mahdavi 2015; Luo et al. 2015). A great many of research attentions have been drawn on shafting the functional groups on the organic ligands or metal sites of MOFs (Ma, Abney & Lin 2009; Wang & Cohen 2009). In 2009, Botas et.al observed an outstanding 58.0 wt. % CO2 uptake at 273K and 10 bars on MOF-5 (Zn4OBDC,

BDC=1,4-benzenedicarboxylate) when doped with Co (Botas et al. 2010). Moreover, Yan et.al examined and compared the adsorption performances on the simple chemically modified and parent Cu-BTC (BTC=1,3,5-benezenetricaboxylate), it showed that the functionalized

MOF had a superior CO2 uptake than pristine Cu-BTC (Xie et al. 2012). Later, It was also reported that CO2 adsorption capacity increased significantly from 79.2 to 176 wt. % on MIL-

101 by the ethanol and NH4F treatment (Berdonosova et al. 2013).

Tremendous achievements and endeavours have been made for the CO2 adsorption on MOFs experimentally (Li, J-R et al. 2011). However, challenges still exist. Firstly, as metal-organic frameworks usually consist of various atoms, the array of metal clusters and organic ligands could be infinite, thus, it may be impractical to synthesize and characterize immense hypothesized and perceived MOFs in laboratory. Additionally, the experiment is time- consuming and cost-inefficient for MOFs sampling and testing, and the experiments under extreme high temperature or pressures may be quite difficult to implement. More importantly, the insight observations of kinetic and transport properties for adsorbate and adsorbent could not be directly viewed by experimental method. In contrast, with rapid-growing computational resources, computational simulations could provide anther optimal research option to understand CO2 adsorption and separation within MOFs (Duren, Bae & Snurr 2009; Park, Lively & Sholl 2017; Walton et al. 2008).

2.4.2 Molecular simulations for CO2 capture on MOFs

Computational simulations can provide the great help for researchers to screen and characterize not only current microporous materials but also construct the conceived substances’ architectures which are difficult to synthesize experimentally. In addition, owing to the limited experiment explorations on CO2 dynamics (diffusion) within MOFs, computational simulations are also able to supplement the shortcomings in this field(de Pablo et al. 2014; Yu et al. 2016). Conventional computational techniques mainly consist of quantum-based calculations (ab initio and density function theory) and molecular simulations (Grand Canonical Monte Carlo and Molecular Dynamics). These methods are widely utilized to understand structure-property relationships for porous materials (Evins 2013; Hafner 2007). The ab initio and density functional theory (DFT) are often employed to simulate the detailed chemical or physical interactions between solid adsorbents and small gases. In many porous materials such as activated carbons, the CO2 molecules are physically bonded within the

11 pores via van der Waals forces. Ab initio could accurately calculate such weakly bonded energy. One of the high level ab initio methods, second-ordered Moller-Plesset (MP2) shows quite reliable and accurate results in describing the noncovalent interactions, however, the major drawback is that MP2 method could only compute for small systems containing tens of atoms which may be deficient to characterize many state-of-art microporous material (Vogiatzis et al. 2009). In terms of DFT technique, it is more practical and feasible to investigate relatively “big” systems, and the B3LYP (a typical DFT method) has a high computational efficiency compared with MP2. Although B3LYP can provide a finer approximation of electron correlation effects, it has some apparent deficiencies when calculating the long-range electrostatic interactions and the short-term vdW potentials regarding in simulation of CO2 adsorption in MOFs (Dreizler & Gross 1990; Handy 1996; Orio, Pantazis & Neese 2009). Since molecular simulation force field is a more sensible approach to calculate the vdW interactions, it has been implemented as an alternative to compute the CO2 adsorption performance within MOFs(Tian, Dai & Jiang 2016). To date, molecular simulations method is regarded as the one of the most prospective technology to predict the gas adsorption properties within porous materials such as MOFs and zeolites. Molecular simulations mainly comprise of grand canonical Monte Carlo (GCMC) and molecular dynamics (MD). To date, GCMC simulation is extensively employed to computationally predict the gas uptake in porous materials, whereas MD simulation is more appropriate for the investigation of structural and dynamic properties of adsorbate molecules in a wide range of materials. In recent years, molecular simulations have become an increasingly critical instrument to explore and study not only the adsorption capacity but also dynamic properties between various MOFs and CO2 (Mosher et al. 2013; Torrisi, Bell & Mellot-Draznieks 2010). GCMC simulation is recognized as one of the most direct and efficient computational approach to examine the gas adsorption capacity in MOFs. In addition, GCMC can also provide a fine benchmark or verification for experimental results (Chen et al. 2014; Lawler, Hulvey & Forster 2015). Apart from the adsorption capacity, the diffusion explorations are simultaneously essential to understand the kinetic properties. In this respect, MD simulation plays a significant role to address the research gaps which is hard to achieve experimentally (Ciccotti, Ferrario & Schuette 2014). Yazaydin et al. compared the adsorption performance of dry Cu-BTC and hydrated Cu-BTC structures, as shown in Figure 4. The dry Cu-BTC (Figure 4a) illustrated a CO2 adsorption

12 capacity of 18.4 wt. % under 1 bar at 298K. Interestingly, when the Cu-BTC was hydrated with 4% of water molecular (Figure 4b), the capacity surged remarkably to 27 wt. %. The results of computer simulations were subsequently verified by experiments. They identified that the electric field created by water molecules could coordinate with the copper metal site to significantly enhance CO2 uptake (Yazaydın et al. 2009). In 2007, Yang’s group conducted a combined GCMC simulation on the subject of separation performance of CO2-containing gas mixtures on Cu-BTC, the simulation results demonstrated that the intense interactions 2+ between quadrupole moment of CO2 and Cu metals could improve the CO2 selectivity at low pressure (Yang et al. 2007). Yang and Zhong carried out a hybrid GCMC simulation and Ideal adsorbed solution theory (IAST) approach to examine the adsorption performance on a number of porous materials, revealing that MOFs exhibited advantageous CO2 adsorption capacity than zeolite and carbon adsorbents (Yang, Zhong & Chen 2008). In 2012, Vaesen and co-workers conducted a joint modelling and experiment approach to study the various gas uptake and separation on a series of MIL-68. They reported that the all of the CO2 uptakes in MIL-68s were higher than CH4 (Yang et al. 2012). In addition to the adsorption isotherms, the dynamic properties such as diffusion behaviour and permeability are of great significance characterizing and screening MOFs, which is favourable for pre-design and post-synthesis of the current and new MOFs (Jelfs & Cooper 2013; Meier, Laesecke & Kabelac 2001; Parkes et al. 2014).

In 2008, Babarao and Jiang studied the CO2 and methane diffusion in IRMOF-1, silicate and

C168 Schwarzite, and they reported decreased self-diffusivities of CO2 in these three adsorbents as the loading increased, which is a common tendency in many porous materials due to the steric hindrance(Babarao & Jiang 2008). Salles et al. observed the self and transport diffusivity of CO2 in MIL-47(V) by a combined MD simulations and quasi-elastic neutron scattering experiments which revealed that CO2 molecules in MIL-47(V) did not perform like an idea gas due to their intermolecular interactions(Salles, Fabrice et al. 2013). Sholl and co-workers employed a combined GCMC and MD simulation approach to probe the separation of CO2 and methane mixture in a membrane-based MOF. They proved that the diffusion of CO2 was less likely to be influenced by the slowly moving methane, more importantly, this results corrected the previous view that the membrane-based separation selectivity would be lower than adsorption-based separation(Keskin & Sholl 2007). Similarly,

Yang’s group demonstrated the adsorption and diffusion of CO2 would be at the same time and in the same direction during the adsorption process(Yang & Zhong 2006). Later, another study on bio-MOF-11 for CO2 and H2 adsorption (CO2/H2=15:85) illustrated that CO2

13 molecules were less close to the metal sites but preferentially binding to the organic linkers as reflected from the radial distribution function (RDF) results after the MD simulation(Atci, Erucar & Keskin 2011). In summary, molecular simulations can overcome some of experimental limitations, particularly the microscopic insight observations and characterizations at the aspects of dynamic and transport properties.

2.5 Current research summary and challenges

In this section, the great research efforts have been summarized and highlighted in the field of

CO2 capture within MOFs both experimentally and computationally. Researchers in laboratory have devoted to synthesize and screen hundreds of MOFs for gas adsorption and separation(Li, Kuppler & Zhou 2009), however, it may be quite impossible to compound such numerous MOFs’ models for experimental testing (Li et al. 2011). On the other hand, with ever-growing computational technology, GCMC and MD simulations are becoming more rational and efficient to evaluate the adsorption properties effectively and economically on MOFs. Moreover, the structural, kinetic and transport analysis methods could provide a microscopic guide to refine and design of the current and novel MOFs (Ciccotti, Ferrario & Schuette 2014; Parkes et al. 2014; Zhou, Long & Yaghi 2012). However, great research challenges and gaps still exist. The MOFs’ performance is usually determined by interplay factors such as porosity, thermodynamic equilibrium and kinetic effect. In practice, it is more often a combination of these effects caused by open metal site, structural flexibility and functional groups in MOFs (Luo et al. 2015; Lyndon et al. 2015; Xian et al. 2015; Yan et al. 2015; Zhou et al. 2015). The effect of the unsaturated metal sites in MOFs has been widely examined, for example, the enhanced CO2 adsorption uptake was 2+ (Bordiga et al. observed in the Cu-BTC (HKUST-1) by a coordinative interaction of CO2 and Cu 2007). In addition to the open metal site effect, the “gate-opening” or “breathing” mechanism often occurs in flexible MOFs, especially in MIL-53 serious. MIL-53(Cr) possesses an insignificant CO2 uptake below 5 bars, whereas a remarkable adsorption increase is observed as the structure restore after CO2 molecules entering into the specific pores (Bourrelly et al. 2005). Moreover, the surface functionalization on metal sites or organic ligands could play a critical role to enhance the affinity between CO2 and MOFs, but the synthesis and investigations on fluoride treated MOFs were quite rare. Few attentions have been drawn on the research of dual effects, for example, the coordination effect between structural flexibility/open metal site and chemical functionalization. To our best acknowledge, few

14 molecular simulations have been implemented for CO2 binding locations and self-diffusion on fluorinated MOFs, especially on HKUST-1 and MIL-68(In). Therefore, this present work, we constructed fluoride modified HKUST-1 and MIL-68(In) on their organic linkers, by using fluoride to replace all of the hydrogen atoms attached on the organic ligands, and then we examined CO2 adsorption capacities on these MOFs by GCMC simulation, we examined

CO2 binding sites after MD simulation by RDF and observed the CO2 self-diffusion by MSD method. Lastly, the energy interaction strength between MOFs and CO2 was calculated to verify the GCMC results.

CHAPTER 3 METHODOLOGY

In present research work, a combined GCMC and MD simulation approach is adopted to assess the CO2 adsorption performance on HKUST-1 and MIL-68(In) by Materials Studio software. The combined GCMC and MD simulation is a method to predict not only the gas adsorption capacity in the materials by grand canonical Monte Carlo simulation but also explore the adsorption dynamics of the adsorption process by molecular dynamics simulation. This method is, firstly carried out by GCMC simulation to numerically calculate the quantity of the adsorbed gas, and MD simulation will be implemented to study adsorption, mechanism of adsorbed gases such as diffusion properties of gases in MOFs. In present study, a combined GCMC and MD simulation allows a more precise and practical approach to achieve the research objectives. Materials Studio is a comprehensive modelling and simulation tool for materials and science engineering. It allows the researchers to better understand the materials’ properties at a microscopic level. The GCMC simulation is utilized to predict the CO2 capacity under a pressure swing adsorption, and the MD simulation is employed to observe the structural and dynamic properties of CO2 molecules within MOFs.

3.1 Input parameters

In all of the GCMC and MD simulations, the input parameters including atomic partial charge and force field are of great importance which should be determined and assigned prior to the simulations.

3.1.1 Atomic partial charge

In some early computational studies for CO2 adsorption within MOFs, the intramolecular interactions may be neglected in some cases. However, with the development of computational method, the potentials of chemical bonds in MOFs have become significant and may contribute to the computational accuracy. Therefore, it becomes essential to assign the atomic charges for the precise calculation of electrostatic potentials within MOFs. And due to the quadrupole moment of CO2, it is important to assign the atomic charges of O and C atoms to calculate the electrostatic interactions. Please note that these charges have never

16 been observed experimentally or quantum mechanically (Collins & Woo 2017; Hamad et al. 2015; Li, W et al. 2017; Wilmer & Snurr 2011). In general, there are a few approximations can characterize these charges. The most extensively adopted method is electrostatic potential (ESP) which is the quantum-based approach on the basis of DFT. However, this technique is often applied on non-periodic models. The Mulliken Population Analysis method which is another DFT-based calculation approach usually overestimates the charges. Very recently, a Molecular Charge Equilibration method, the molecular charge equilibration (QEq) technique is usually performed for molecular systems. Many studies have been carried out this method to obtain the atomic charges in MOFs because it takes into account the geometry and the electronegativities of the various atoms which is more precise for MOFs models with periodic boundary (Rappe & Goddard 1991).

3.1.2 Force field

Apart from the atomic partial charge, the force field is another critical parameter for the molecular simulations. In recent computational simulations, it is essential to describe all of the related interactions between the atoms in the systems (Rappe & Goddard 1991). Force field (FF) is known as a set of potential energies describing the bond (valence) and non-bond interactions. The valence term includes the intramolecular interactions (e.g., bond, angle, torsion and inversion). And the non-bond term generally consists of Lennard-Jones (LJ) and long-rang electrostatic potentials (Columbic interactions). To date, a number of force fields have been developed and adopted for the molecular simulations including DREIDING, UFF (universal force field), COMPASS (Condensed-phase Optimised Molecular Potentials for Atomistic Simulation Studies) and other specific force fields (Martin 2006; Pongsajanukul et al. 2017). Among them, the UFF is the most extensively utilized force field for molecular simulations as UFF has been rigorously tested by the Materials Studio researchers and results are in agreement with published work. UFF is a purely diagonal, harmonic force field. In UFF, the potential energy E is expressed as a sum of valence and nonbonded interactions in Eq 1,

E= ER + Eθ + Eɸ + Eω + Evdw + ECoulombic (1)

Where bond stretching (ER) is described by a harmonic term, angle bending (Eθ) by a three- term Fourier cosine expansion, and torsions (Eɸ) and inversions (Eω) by cosine-Fourier expansion terms, the van der Waals (Evdw) interactions are described by the Lennard-Jones

17 potential, and electrostatic interactions (ECoulombic) are described by atomic monopoles and a screened (distance-dependent) Coulombic term (Casewit, Colwell & Rappe 1992; Rappe et al. 1992). UFF is adopted for present study since UFF presents a good compromise in the molecular simulation in regarding to computational effectiveness and accuracy. In addition, UFF is developed in conjunction with QEq method, thus universal force field usually has a fine collaboration with the QEq charge equilibration method(Rappe & Goddard 1991).

3.2 Ensemble

An ensemble is a collection of all possible systems which have different microscopic states but have an identical macroscopic or thermodynamic state. In other words, an ensemble is a set of points in phase space satisfying the conditions of a particular thermodynamic state(Nosé 1984). Different macroscopic environment constraints lead to different types of ensembles with particular statistical characteristics (Adams 1975). Four ensembles are often used in molecular simulations: Grand canonical ensemble (µVT), which has fixed chemical potential (µ), volume (V) and temperature (T); Micro canonical ensemble (NVE), which has fixed number of atoms (N), volume (V) and energy (E);Canonical ensemble (NVT), which has fixed number of atoms (N), volumes (V) and temperature (T); Isothermal-isobaric ensemble (NPT), which has fixed number of atoms (N), a fixed pressure (P) and temperature (T) (Din & Michaelides 1997; Lamb & Jorgensen 1997). Besides, a numerical heat bath technique will be adopted to add or eliminate heat in the course of time in NVT or NPT ensembles. The Berendsen thermostat and Nosé-Hoover thermostat are two common used techniques for molecular simulations (Berendsen et al. 1984; Hoover 1985; Nosé 1984).

3.3 Periodic boundary condition and cut-off distance

The term of periodic boundary condition refers to the simulation of structures consisting of periodic lattices of identical motifs. This is crucial as it allows atoms or molecules in simulation box to maintain a continuous trajectory while still experiencing the same potential energy field (Makov & Payne 1995). For example, as shown in Figure 5, the central square (Yellow) is the simulated zone and the surrounding squares simulation boxes are virtual imagines. The particle (grey) in the central square moving out will reflect and emerge from

18 the neighbouring virtual simulation boxes to guarantee the force or potential energy can be assigned on the particle. All periodic boundary conditions imply a cut-off criterion to make a balance of computing accuracy and efficiency. For instance, when the distance of two atoms is beyond a certain range, the interactions between them can be ignored. Basically, the minimum image convention advises a maximum distance for this cut-off will be no more than half the cell dimensions (Pivkin & Karniadakis 2005).

Figure 5. Periodic boundary condition.

3.4 Principle of Grand Canonical Monte Carlo simulation

With well-defined force field to calculate the interactions, the grand canonical Monte Carlo (GCMC) simulation, one of the most common methods predicting the gas adsorption in porous materials, is adopted to examine the CO2 uptake in MOFs. In this method, the temperature T, volume V and the chemical potentials µ are kept fixed, and the particles or molecules are allowed to have trial moves (e.g., insertion and deletion) before the system reaches to the equilibrium phase (pressure or temperature), these movements have equal

19 probability to randomly translate or rotate an existing particles or molecules, then the adsorbed amount N of the particles or molecules will be calculated by a statistically approach after the equilibrium stage (Duren, Bae & Snurr 2009; Frenkel & Smit 2002). In most cases, the Metropolis is the most common used technique in GCMC simulation for CO2 adsorption in MOFs (Metropolis et al. 1953). The adsorption isotherm which is the most significant thermodynamic information could be obtained from the GCMC simulation. Such adsorption isotherm can provide a comparison for the experimental results. At the moment, GCMC simulation is a practical and sensible computational technique to investigate the adsorption properties of single component CO2 or mixtures in MOFs (Akkermans, Spenley & Robertson 2013; Satoh 2010).

3.5 Principle of molecular dynamics simulation

Up to date, the studies on diffusion properties of gust molecules in MOFs are quite limited compared with the adsorption achievements. Among various simulation methods, MD simulation, which is based on the Newton’s second law of motion, is feasible and effective to investigate the structural and dynamic properties of adsorbate molecules in microporous metal-organic frameworks (Parkes et al. 2014; Satoh 2010). MD simulation is solving the equations of motion or Newton’s second law. After force determination on each atom, the acceleration of each atom in the system is possible to calculate. And then, integration of the equations of motion could generate a trajectory of the atom positions, velocities and other information with time (Nose & Klein 1983). See Eq 2.

휕푈(풓1,풓2,⋯,풓푛) 푚푖풓̈ 푖 = − = 푭푖 (2) 휕푟푖

Where 푭푖 is the force on atom i, 푚푖 is the mass of atom i, and U is the potential energy of the system. Therefore, given the initial coordinates and velocities and other dynamic information at time t, the updated coordinates and velocities at a time 푡 + 훥푡 can be calculated and determined. In this respect, the Verlet algorithm uses position 풓풊and force 푭풊at time 푡 and the position from time 푡 − 훥푡 to calculate new position 풓풊 at time 푡 + 훥푡 and obtain the velocity 푽풊 (Grubmuller et al. 1991). See Eq 3 and 4,

푭풊(훥푡) 2 3 풓풊(푡 + 훥푡) = −풓풊(푡 − 훥푡) + 2풓풊(푡) + (훥푡) + 푂((훥푡) ) (3) 푚푖 푑풓 풓 (푡+훥푡)−풓 (푡−훥푡) 푽 (푡) = 풊 = 풊 풊 + 푂((훥푡)2) (4) 풊 푑푡 2훥푡

Note that the time step 훥푡 depends on the integration method as well as the system itself. In summary, MD simulation is a continuous trajectory describing displacement of all molecules and atoms. The principle of MD is simple, in each MD simulation step, the initial position r and velocity v of particle are defined, and then all the molecules’ equations of motion are solved numerically after the force field F is calculated. Consequently, the position and velocity are updated to generate a dynamic view of the system evolution (Andersen 1980; Ciccotti, Ferrario & Schuette 2014; Erpenbeck & Wood 1982; Haile 1992; Zeng, Yu & Lu 2010), as shown in Figure 6.

. Figure 6. Schematic diagram of a basic MD code(Zeng, Yu & Lu 2010).

3.6 Molecular dynamics analysis methods

Very recently, MD simulation has been extensively deployed to explore and determine the diffusion and dynamic properties including self-diffusivity, transport diffusivity and corrected diffusivity of CO2 in MOFs. The majority of the studies of molecules diffusion are obtained

21 by single MD or an experimental combined MD method (Atci, Erucar & Keskin 2011; Krishna & Van Baten 2006; Li, J-R, Kuppler & Zhou 2009; Parkes et al. 2014; Salles, Fabrice et al. 2013; Skoulidas & Sholl 2005).

3.6.1 Mean square displacement

Mean square displacement (MSD) analysis is a technique which is critical in characterizing the diffusion behavior of adsorbed molecules in the system. It determines the particles average displacement in a molecular dynamics simulation as shown in Figure 7 (Kärger

1992). In addition, self-diffusion constant (Ds) can be obtained by using the Einstein relation which is take the slope of MSD over time (Chandler 1975; Michalet 2010).

300

250

Pressure 0.1bar 200

150

100

Mean Square Displacement (Ds) Displacement Square Mean 50

0 0 100 200 300 400 500 Time(Ps)

Figure 7. Mean square displacement.

3.6.2 Radial distribution function

Besides, MD simulation provides a direct image to examine particles’ packing or ordering probability in the system by the radial distribution function (RDF) analysis. RDF is a pair correlation function which describes how atoms or particles in a system are radically packed around each other(Nijboer & Van Hove 1952). It could provide an effective way to describe

22 the overall structure properties of disordered molecular systems as shown in Figure 8(Waisman 1973). RDF can be expressed by the following equation (Eq 5) 푔(푟) = 푛(푟)/(휌4휋푟2d푟) (5) Where 푔(푟) is the RDF; 푛(푟) is the mean number of atoms in a shell of width d푟 at distance 푟, 휌 is the mean atom density. Moreover, the RDF results could provide a fine supplement with X-ray or neutron diffraction

(Bentley et al. 1990; Filipponi 1994; Li, F & Lannin 1990). Thus, the binding sites of CO2 molecules can be explored by RDF method after MD simulation.

Figure 8. Radial distribution function.

3.6.3 Adsorption strength

In addition, the adsorption energy (Ep) between MOFs and CO2 is quite difficult to assess both experimentally and numerically. However, it may be achieved in MD simulation by calculating the average value of CO2 adsorbed MOFs equilibrium frames, i.e. 6 frames from relatively stable 400 to 500 ps. Thus, the adsorption energy (Ep) could be calculated by the Eq 6,

Ep=Etotal-EMOF-ECO2 (6)

Where Etotal, EMOF and ECO2 are the energy of CO2 adsorbed MOF extracted MOF and extracted CO2 molecules, respectively, as shown in Figure 9. By exacting the CO2 adsorbed

MOFs equilibrium frames, the total potential energy Etotal can be calculated by the Forcite in

Materials Studio. After removing the all of the adsorbed CO2 in the structure, we are able to achieve the potential energy of the MOF at the equilibrium stage. We can also accomplish the potential energy calculation of the CO2 molecules by deleting the MOF structure in the frame, thus, the interaction strength (adsorption energy) could be measured by the Eq 6 to understand the adsorption strength between CO2 and MOF under specific pressure.

Figure 9. The snapshot of a CO2 adsorbed equilibrium MOF structure: CO2 adsorbed MOF (upper), isolated MOF (lower left) and isolated adsorbed CO2 molecules (lower right).

3.7 Summary

To summarize, this section highlights the main research approaches including the GCMC and MD simulations. Prior to the simulations, the input parameters such as atomic partial charges and force field are important which should be defined appropriately. With the intermolecular and intramolecular interactions are appropriately described by the force fields, the grand canonical Monte Carlo (GCMC) simulation is often implemented to predict the gas adsorption capacity in a periodic simulation box ,in which the temperature T, the volume V and chemical potentials are kept constant, only the number of adsorbed molecules N are allowed to fluctuate by randomly creating, rotating , deleting as well as translating/displacing in the system, and then the average amount of adsorbed molecules is statistically calculated when the system reached equilibrium stage. At the moment, GCMC simulation is a practical and popular computational technique to investigate the adsorption properties of single component CO2 or mixtures in MOFs. Moreover, MD simulation, based on the Newton’s equitation of motion, is becoming increasingly popular to investigate the kinetic and transport properties of the adsorbate in microporous materials, especially in MOFs. The RDF and

MSD analysis method will be adopted to explore the CO2 binding locations and its self- diffusion within MOFs. Lastly, the energy strength between CO2 and MOFs is calculated to reveal the related relationship.

CHAPTER 4 CARBON DIOXIDE ADSORPTION IN HKUST-1

4.1 Model configuration

HKUST-1, also known as Cu-BTC, is one of the most extensively investigated MOFs for gas adsorption and separation after it was first synthesized by Chui and co-workers in 1999(Chui et al. 1999). The model of HKSUT-1 is employed from the Crystallography Open Database (No.2300380). This structure of HKUST-1 was reported by Yakevenko and co-workers in 2013 (Yakovenko et al. 2013). HKUST-1 consists of paddlewheel-coordinated copper clusters and surrounded by 1, 3, 5- benzenetricarboxylate (BTC) organic linkers. Each metal node is composed of two copper atoms connected to the oxygens of four BTC ligands (see Figure 10a). In order to construct the fluorinated model (HKUST-1F), all of the hydrogen atoms on BTC organic linkers are manually replaced by fluoride elements (Figure 10b) by using the module of Materials Studio Visualizer. (a)

(b)

Figure 10 Unit cells of HKUST-1 (a) and HKUST-1F (b), Colour: Copper in orange, oxygens in red, carbons in grey, hydrogen in white and fluorides are light blue.

4.2 Grand Canonical Monte Carlo Simulation details

As described above, the atomic partial charges and force field are critical for molecular simulations. For this reason, all of the charges of HKSUT-1 and HKUST-1F were initially reset to be zero, and then the atomic partial charges in frameworks were obtained by QEq method (see the details in Table 1), and maintained in the following simulations. Prior to the GCMC simulation, the energy calculation and geometry optimization were applied on MOFs in order to obtain an optimal configuration. The force field of UFF was performed on HKUST-1 and HKUST-1F, Ewald technique was utilized to calculate the electrostatic interactions. Atom based method with a cut-off distance of 15.5 Å was used in the calculations of van der Waals (vdW) interactions.

Given the quadrupole movement of carbon dioxide, CO2 was modelled as a linear molecule with double C=O, and the atomic partial charge of O and C atom was calculated by QEq method which were -0.446e and 0.892e, respectively, when implemented the energy and geometry optimization on CO2, the UFF was determined and Atom based method was used for both vdW interactions electrostatic interactions calculation.

Table 1. The Atomic partial charge q (e) and force field of HKUST-1 and HKUST-1F. Element Atomic partial Force field type Hybridization charge Cu 2.388 Cu3+1 Trigonal-bipyramid

O -0.702 O_3 Trigonal

H 0.052 H_ No hybridization

Cu* 3.001 Cu3+1 Trigonal-bipyramid

O* -0.715 O_3 Trigonal

F* -0.8 F_ No hybridization

* is the element in HKUST-1F

Unlike the most simulation studies on HKUST-1 published before to keep the MOFs and CO2 as rigid, in present work, the MOFs and CO2 are allowed to be flexible during the GCMC simulations. The periodic boundary condition was applied. For HKUST-1, since the cell parameters were a=b=c=26.3 Å. we employed the unit cell as the simulation box, GCMC method was defined as normal Metropolis technique, the adsorption isotherms of HKUST-1 and the HKUST-1F were performed at 298 K by Sorption modules in Materials Studio with the pressure from 0.1 to 5 bars, and equilibration process of 1 × 106 steps followed by 1 × 107 production process steps with a cut-off radius of 18.5 Å. The Ewald technique was used to handle the electrostatic interactions.

4.2.1 Adsorption isotherms of CO2 in HKUST-1 and HKUST-1F

The GCMC adsorption isotherm is a significant feature to access MOF’s property and performance. Adsorption isotherms of CO2 in HKUST-1 and HKUST-1F are shown in Figure 11, which demonstrate that the adsorption capacity of HKUST-1F is much higher than that of non-fluorinated HKUST-1. For example, the adsorbed CO2 in HKUST-1F unit cell is 176 at 0.1 bar compared with 62 in HKUST-1unit cell. At 1 bar, HKUST-1F adsorbs 194 carbon dioxide molecules, which is almost the double of the HKUST-1. In addition, the uptake of

CO2 in HKUST-1 increases significantly with the pressure from 62 at 0.1 bar to 136 at 5 bars.

Yet, HKUST-1F reaches the saturated adsorption equilibrium at a relatively low pressure (approximately between 0.5-1bar).

Figure 11. Adsorption isotherm of CO2 in HKUST-1 and HKUST-1F at 298K under the pressure of from 0.1 to 5bar.

4.2.2 Comparison of CO2 adsorption isotherms of CO2 in HKUST-1

The comparison of CO2 adsorption isotherms in HKUST-1 under different conditions (experiment and simulation) are shown in Table 2. The results show that both HKUST-1 and HKUST-1F in this work have a superior adsorption capacity. In particular, fluorinated HKUST-1 has a much higher uptake than that of non-fluorinated structure, which may be attributed to the strong electrostatic interactions between fluoride and CO2.

Table 2. CO2 adsorption capacity on HKUST-1(Cu-BTC) under different conditions. MOFs Pressure (Bar) Temperature (K) Adsorption Adsorption capacity (mmol/g) capacity (wt %) HKUST-1 1 298 10.5 46.3

HKUST-1 5 298 14 61.7

HKUST-1F 1 298 17.2 75.5

MOFs Pressure (Bar) Temperature (K) Adsorption Adsorption capacity (mmol/g) capacity (wt %) HKUST-1F 5 298 17.3 76.3

HKUST-1a 10 327 7.19 31.6

HKUST-1b 15 298 12.7 -

HKUST-1c 1 273 11.6 51 (Ethanol+NH4Cl) Cu-BTCd 10 298 11.9 52.4 a (Ye et al. 2013),b (Liang, Marshall & Chaffee 2009),c (Yan, X et al. 2014), (Zhao et al. 2011).

4.3 Molecular dynamics simulation results

4.3.1 Adsorption sites of CO2 in HKUST-1 and HKUST-1F

The MD simulation not only gives an adsorption prediction of CO2 in MOFs but also provide an image of CO2 probability in MOFs, as shown in Figure 12 and 13 for HKUST-1 and

HKUST-1F. The results indicate that adsorbed CO2 in HKUST-1 at low pressure (1bar) are more close to paddle-wheel-coordinated Cu sites, and the additional adsorbed CO2 are more likely to appear at the central channel at high pressure (5 bar). For HKUST-1F, there is no remarkable change for CO2 adsorbed in MOF from low to high pressure.

Figure 12. The CO2 (red spots) adsorption snapshot in HKUST-1, 1 bar (left) and 5 bar (right).

Figure 13. The CO2 (red spots) adsorption snapshot in HKUST-1F, 0.1 bar (left) and 5 bar (right).

In order to verify the more accurate and precise binding sites of CO2 in MOFs, radial distribution function is implemented for analysis after MD simulation. The peaks indicate the most possible locations of an atom from the reference atom at a specific radius. In present study, RDF results of HKUST-1 and CO2 under different pressures are shown in Figure 14.

Due to the distinct orientations of oxygen atoms in CO2 molecule, the central carbon atom was selected to represent the entire CO2, thus the atomic pairs are determined to be Cu

(MOF)-C (CO2), O (MOF) - C (CO2), and H (MOF) - C (CO2). Figure 14a displays the shortest distance (first peak) is around 2.91 Å between H of HKUST-

1 and C of CO2, revealing the orientation of CO2 with C atoms toward H atoms of HKUST-1.

The next peak is located at 3.49 Å for O atoms of HKUST-1 and C atoms of CO2, followed by a sharp and narrow peak at 3.93 Å for Cu atoms of HKUST-1 and C atoms of CO2. We could predict that the CO2 molecules are more close to H atom (BTC linker) of HKSUT-1 at the pressure of 0.1 bar. With the pressure rise (Figure 14b, 3c, 3d and 3e), we found that the first peak of Cu- C pair is still situated at around 3.9 Å, and the peak of the O-C pair is at about 3.49 Å. However, the peak of H-C pair becomes flat and broad, indicating majority of additional CO2 adsorbed move toward Cu sites, which may be attributed by the open metal site effect.

Figure 14. Radial distribution function of atomic pairs from HKUST-1 and CO2 at 298 K and different pressure: (a) 0.1 bar, (b) 0.5 bar, (c) 1 bar (d) 3 bar and (e) 5 bar, respectively.

Similar RDF analyses are shown for HKUST-1F in Figure 15. The atomic pairs are Cu

(MOF)-C (CO2), O (MOF)-C (CO2) and F-C (CO2). RDF results of HKUST-1F indicate that there is a stronger interaction is between F atoms in MOF and the C atoms of CO2, reflected from the drastic and sharp first peak at 2.75 Å. The peaks are located at the same distance with the pressure rising to 5 bar, but peaks are becoming slightly wide for F-C atomic pair, demonstrating CO2 molecules are bound tightly to F atoms. The peak of Cu-C atomic pair has a slight decrease due to the amount of the carbon dioxide adsorbed increases and CO2 are more close to F sites.

Figure 15. Radial distribution function of atomic pairs from HKUST-1F and CO2 at 298 K and different pressure: (a) 0.1 bar, (b) 0.5 bar, (c) 1 bar (d) 3 bar and (e) 5 bar, respectively.

4.3.2 Self-diffusivity of CO2 in HKUST-1 and HKUST-1F

To further probe the diffusion property of adsorbed CO2 in MOFs, the mean square displacement (MSD) method has been performed to probe the diffusivity of CO2 in MOFs.

We take advantage of the carbon atom representing the CO2 as an approximation. Figure 16 shows the self-diffusivity of CO2 in HKUST-1 and HKUST-1F by taking the slope of the

MSD curves over time from 0 to 400 ps. The results indicate the self-diffusion of CO2 molecules in HKUST-1 and HKUST-1F declines with pressure increase. This is an expected result and can be explained by the increase in adsorbed CO2 and then the steric hindrance of movement for adsorbed CO2 molecules within MOFs’ structures. Note that the diffusivity

(MSD slope) of CO2 in HKUST-1F is much lower than that of non-fluorinated HKUST-1, suggesting adsorbed CO2 is attached firmly.

Figure 16. Mean square displacement over time of CO2 in HKUST-1and HKUST-1F.

4.3.3 Interaction strength between CO2 and HKUST-1

In order to obtain insight of the interaction strength between CO2 and MOFs, we extracted the structures of MOFs and adsorbed CO2 molecules, respectively, and then calculated the interaction energy (Ep). The results as shown in Table 3 and Table 4 indicate that interaction strength between MOFs and CO2 increases with pressure (i.e. CO2 loading). Moreover, the interaction is attractive in nature of CO2 due to the negative potential energy. The Ep of

HKUST-1F is higher than HKUST-1, reflected to the increased loading number of CO2

(adsorption capacity). The potential energy of extracted CO2 in HKUST-1 reduced significantly from positive (repulsion) to negative (attraction), suggesting the distance between CO2 increases (i.e. loosely adsorbed). For HKUST-1F, potential energy of CO2 is relatively low, which indicates CO2 molecules are stable and could hardly move.

Table 3. Interaction potential energies (Kcal/mol) of CO2 adsorbed in HKUST-1.

Pressure(bar) Loading(number Total Energy of MOF Energy of CO2 Ep per cell) Energy 0.1 62 7974.9 8505.1 49.1 -579.3

0.5 83 7863.9 8527.5 36.4 -700

1 99 7739.7 8520.3 13.8 -794.3

3 126 7557.9 8519.9 -29.1 -932.9

5 136 7488 8520.9 -54.2 -978.7

Table 4. Interaction potential energies (Kcal/mol) of CO2 adsorbed in HKUST-1F.

Pressure(bar) Loading(number Total Energy of MOF Energy of CO2 Ep per cell) Energy 0.1 176 -1469.3 919 7.94 -2396.6

0.5 189 -1526.5 956.3 3.1 -2486

1 194 -1538.6 975.2 2.13 -2515.9

3 195 -1558.6 961.9 2.08 -2522.9

5 198 -1549.2 994.9 2.09 -2546.3

4.4 Summary

In summary, results of the GCMC simulation demonstrate that HKUST-1 and HKUST-1F in

this study have an exceptional CO2 adsorption capacity up to 5bar. And the fluoride modified HKUST-1 has a higher uptake than that of non-fluorinated structure. The powerful

electrostatic interactions between fluoride and CO2 contribute to the superior gas uptake. MD

simulation has been performed to investigate the CO2 adsorption location and binding strength in HKUST-1 and HKUST-1F. The results demonstrate that HKUST-1F has a

superior adsorption capacity for CO2, particularly at low pressure. Moreover, adsorbed CO2 molecules are bound closely to fluoride atoms of BTC organic linkers in HKUST-1F, however, may hinder their diffusion to copper metal sites.

CHAPTER 5 CARBON DIOXIDE ADSORPTION IN MIL-68(In)

5.1 Model configuration

MIL-n(M) materials, Materials of Institute Lavoisier, are mainly connected with trivalent metal cations M3+ (e.g. Al3+, Cr3+, Fe3+, V3+, Ga3+ or In3+) and organic linkers of benzene dicarboxylic acid which could form three-dimensional networks such as MIL-53(M), MIL- 68(M), and MIL-101(M), etc. Interestingly, MIL-53(M) and MIL-68(M) have the same formula, same inorganic and organic units but show different framework features and properties. MIL-53 series have been widely investigated for gas adsorption due to its unique structure flexibility (Bourrelly et al. 2005; Hamon, Lomig et al. 2009; Lebedev et al. 2005; Millange et al. 2008). The model of MIL-68(In) is adopted from the Crystallography Open Database (No.4306785). MIL-68(In) is an Indium based metal-organic frameworks. The

MIL-68(In) is constructed with the chains of InO4 (OH) 2 octahedral units and interconnected with the terephthalate ligands to form central triangle and hexagon channels (see Figure 17a). In order to build up the fluorinated model which is MIL-68F (In), we utilized the Materials Visualizer to do the modification, i.e. hydrogen atoms on terephthalate linkers are all manually replaced by fluoride elements (Figure 17b) . (a)

(b)

Figure 17. Unit cells of MIL-68 (a) and MIL-68F (In) (b), Colour: Indium in dark red, oxygens in red, carbons in grey, hydrogen in white and fluorides are light blue.

5.2 Grand Canonical Monte Carlo Simulation details

Similarly, the atomic partial charges of MIL-68(In) and MIL-68F (In) were obtained by QEq method and the UFF force filed was assigned for both MOFs as shown in in Table 5, these parameters remain constant in the following simulations. Table 5. The Atomic partial charge q (e) and force field of MIL-68(In) and MIL-68F (In). Element Atomic partial Force field type Hybridization charge In 2.289 In3+3 Octahedral

O -0.519 O_3 Tetrahedral

H 0.1 H_ No hybridization

In* 2.697 In3+3 Octahedral

O* -0.498 O_3 Tetrahedral

F* -0.554 F_ No hybridization

* is the element in MIL-68F (In)

The energy calculation and geometry optimization were implemented on MIL-68(In) and MIL-68F (In) in order to seek an optative configuration for the following simulations. In these calculations, Ewald technique was used to calculate the electrostatic interactions. Atom based method with a cut-off distance of 15.5 Å was used in the calculations of van der Waals (vdW) interactions.

Likewise, the CO2 molecule was modelled as a linear molecule with double C=O, and the atomic partial charge of O and C atom were -0.446e and 0.892e which are the same as the HKUST-1 project. UFF was selected and Atom based method was used for both vdW interactions and electrostatic interactions calculation for energy and geometry optimization on CO2.

In present work, the MIL-68(In), MIL-68F (In) and CO2 are allowed to be flexible during the GCMC simulations. For HKUST-1, we employed a single unit cell as the simulation box; however, in this case, the simulation box amplified to 3 (1×1×3) of unit cells because the original cell parameters were a=21.73 Å, b=37.68 Å and c=7.23 Å, respectively. Likewise, GCMC method was defined as normal Metropolis technique; the adsorption isotherms of MIL-68(In) and the Mil-68F (In) were performed at 298 K by Sorption modules in Materials Studio by the pressure from 0.1 to 5 bars, and equilibration process of 1 × 106 steps followed by 1 × 107 production process steps with a cut-off distance of 15.5 Å which is smaller than of HKUST-1(18.5 Å). Atom based method was used to calculate vdW interactions and the Ewald technique was used to compute the Columbic interactions.

5.2.1 Adsorption isotherms of CO2 in MIL-68(In) and MIL-68F (In)

Figure 18 shows the adsorption isotherms of CO2 in MIL-68(In) and MIL-68F (In), indicating that the fluorinated MIL-68F (In) performs well than MIL-68(In) for CO2 uptake. For instance, at pressure of 0.1 bar, only 6 CO2 molecules are adsorbed in the MIL-68(In) unit cell, compared with 67 in the MIL-68 F (In). With the pressure increases to 1 bar, the number of CO2 in MIL-68(In) grows to 23, while the number of CO2 adsorbed in MIL-68F (In) is 92. After the pressure rise to 5 bars, the MIL-68(In) adsorption capacity escalates to 57 while the MIL-68(In) F elevates to 117. Given the tendency of adsorption curves, both MIL-68(In) and MIL-68 F (In) may not have reached to the adsorption equilibrium at 5 bars. It is expected

42 that MIL-68 and its fluorinated structures would have an advantageous CO2 adsorption capacity at higher pressures.

Figure 18. Adsorption isotherm of CO2 in MIL-68(In) and MIL-68F (In) at 298K under the pressure of from 0.1 to 5 bar.

5.2.2 Comparison of CO2 adsorption isotherms of CO2 in HKUST-1 and MIL-68(In)

The previous experimental studies reported the CO2 adsorption capacities in MIL-68(In) and

MIL-68(In)-NH2 were 4 and 8 wt. % at 1 atm and 298 K, respectively(Arstad et al. 2008), compared with present simulation results were 2.9 and 7.81 wt.% for MIL-68(In) and MIL- 68F (In) at 1 bar and 298 K, suggesting that the simulation result of MIL-68 is underestimated, but the results for amine and fluoride functionalized MIL-68 are more close. The results of the adsorption isotherms in MOFs, as shown is Figure 19, show that both HKUST-1 and HKUST-1F have a relatively high adsorption capacity. In addition, fluorinated

MOFs, HKUST-1F and MIL-68 F (In) perform well for CO2 adsorption than that of non- fluorinated structures, which may be attributed to the strong electrostatic interactions between fluoride atoms and CO2. Note that the unit cell of MIL-68 is triple of original cells.

Figure 19. Comparison of CO2 adsorption isotherms of CO2 in HKUST-1 and MIL- 68(In).

5.3 Molecular dynamics simulation results

5.3.1 Adsorption sites of CO2 in MIL-68(In) and MIL-68F (In)

The adsorption sites images of CO2 in MIL-68 (In) and MIL-68F (In) of MD simulations are displayed in Figures 20 and 21. It shows that adsorbed CO2 molecules in MIL-68(In) at 0.1 bar have great possibility arising in the central triangle pore, however, CO2 are more likely to emerge at hexagon channel at 5 bar, while, the appearance probability of CO2 in MIL-68F (In) varies insignificantly.

Figure 20. The CO2 (red spots) adsorption snapshot of MIL-68(In), 0.1 bar (left) and 5 bar (right).

Figure 21. The CO2 (red spots) adsorption snapshot of MIL-68F (In), 0.1 bar (left) and 5 bar (right).

A series of RDF results of MIL-68 and CO2 under different pressures are shown in Figure 22.

The atomic pairs are In (MOF)-C (CO2), O (MOF) - C (CO2), and H (MOF) - C (CO2). At pressure of 0.1 bar, as shown in Figure 22a, the relatively strongest interaction (first peak) is between O in MOF and the C of CO2, as located at 3.41 Å, and closely followed by the peak of H (MOF) - C (CO2), which is located at 3.61 Å. The peak of In–C pair is located 4.41

Å, revealed as a relatively intense and sharp peak. Thus, the adsorbed CO2 molecules are bound closely to O and H atoms in MIL-68(In), which is the triangle pore of the structure.

With pressure increasing to 0.5 bar, as shows in Figure 22b, the first peak appears at 3.15 Å, which is O-C pair, the next peak, H-C pair, is located at 3.85 Å, followed by a strong peak of In-C pair, which is located at 4.79 Å, indicating that additional adsorbed CO2 at 0.5 bar are more close to O atoms. When the pressure rises to 1 bar (see Figure 22c), the H-C and O- C atomic pairs are becoming wide and fewer intense, while, the peak of In-C remains sharp, probably suggesting that additional adsorbed CO2 molecules become to emerge in hexagon void of MIL-68(In) structure. With the pressure continues to creep up to 3 and 5 bars, the

CO2 molecules are more close to the H atoms, however, they reside in a more widespread area, and majority of molecules may probably be adsorbed in the hexagon area, revealed as three broad peaks in the Figures 22d and 22e.

Figure 22. The radial distribution function for atomic pairs of MIL-68 and CO2 at 298 K and different pressure: (a) 0.1 bar, (b) 0.5 bar, (c) 1 bar (d) 3 bar and (e) 5 bar, respectively.

In terms of MIL-68F (In), Figure 23 displays the RDF results of CO2 in this structure. The atomic pairs are In (MOF)-C (CO2), O (MOF)-C (CO2) and F-C (CO2). The trend of these curves is quite similar to that of HKUST-1F. The results demonstrate a forceful interaction between F atoms of MOF and the C atoms of CO2, which corresponds to a drastic first peak at 2.85 Å, and with the pressure rising to 5 bars; the radius of first peak which is F-C atomic pair has no change. In addition, the G(r) of F-C peak reduces slightly because more CO2 molecules are binding closely to F atoms with the pressure increasing. The peaks of O-C and

In-C pairs are extensive and wide, indicating that CO2 are less likely to be bound with them.

Figure 23. Radial distribution function of atomic pairs from MIL-68F and CO2 at 298 K and different pressure: (a) 0.1 bar, (b) 0.5 bar, (c) 1 bar (d) 3 bar and (e) 5 bar, respectively.

5.3.2 Self-diffusivity of CO2 in MIL-68(In) and MIL-68F (In)

Likewise, the MSD analysis technique was carried out to observe CO2 self-diffusivity in MIL-68 (In) and MIL-68F (In), as shown in Figure 24.

Interestingly, MSD results unfold that self-diffusivity of CO2 in MIL-68F (In) is much lower, demonstrating that CO2 are adsorbed strongly within the structure, which is quite similar to

HKUST-1F. For MIL-68(In), the diffusion of CO2 is slow at low pressure (0.1bar), with the pressure increase to 1 bar, the diffusion of CO2 has a remarkable surge, and however, when the pressure rise to 5 bar, the movement of CO2 molecules becomes difficult. Since the MIL- n (M) materials have been extensively studied on their structure flexibility such as “gate- opening” or “breathing” phenomenon, we may infer that the pore diameter in MIL-68 may have a relatively significant increase at 1 bar, resulting in the high self-diffusivity coefficient of CO2 molecules. However, we did not observe a noticeable rise in the GCMC adsorption isotherm at 1 bar. Further analysis must be implemented to examine and probe on pore diameters variation.

Figure 24. Mean squared displacement over time of CO2 in MIL-68(In) and MIL-68F (In).

5.3.3 Interaction strength between CO2 and MIL-68(In)

Likewise, Tables 6 and 7 illustrate that interaction strength between MOFs and CO2 increases with pressure/CO2 loading. The strength of fluorinated structure is much higher than non- fluorinated one, corresponding to the uptake amount of CO2. Besides, the potential energy of

CO2 molecules in MIL-68 and MIL-68 (In) F increases notably from 0.1 to 3 bars, demonstrating the average distance of adsorbed CO2 reduces, however, the CO2 potentials decline with the pressure increasing to 5 bars, suggesting CO2 molecules are becoming loose in the MOFs.

Table 6. Interaction potential energies (Kcal/mol) of CO2 adsorbed in MIL-68 (In).

Pressure(bar) Loading(number Total Energy of MOF Energy of CO2 Ep per cell) Energy 0.1 6 -22305.1 -22258 5.72 -52.84

0.5 16 -22384.5 -22272.3 20.23 -132.41

1 23 -22442.4 -22230 34.85 -177.24

3 43 -22447.5 -22191.4 48.65 -304.77

5 57 -22378.8 -22022.5 32.89 -385.23

Table 7. Interaction potential energies (Kcal/mol) of CO2 adsorbed in MIL-68F (In).

Pressure(bar) Loading(number Total Energy of MOF Energy of CO2 Ep per cell) Energy 0.1 67 -33261.6 -32664.4 -1.35 -595.86

0.5 84 -33256.3 -32623.7 34.95 -667.56

1 92 -33224.6 -32584 35.1 -695.78

3 107 -33074.7 -32394.8 67.31 -747.13

5 117 -2824.8 -32103.7 53.77 -774.85

5.4 Summary

In conclusion, the MD simulation results of MIL-68 (In) and its fluorinated structure reveal that adsorption strength of MIL-68F (In) is much higher than the untreated MOF, which is in

line with the adsorption loading of GCMC results. In addition, the tight binding of CO2 molecules to fluoride functional organic ligands not only restricts the movement of the molecules but also may have no effect on the structure flexibility of MIL-68 (In).

CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS

6.1 Conclusions

In this thesis, a hybrid GCMC and MD simulation method is adopted to explore the CO2 adsorption performances on two typical and distinct MOFs, HKUST-1 and MIL-68(In). Previous studies have paid numerous attentions on the single predominant factor or effect such as open metal site, structure flexibility and group functionalization. In addition, the kinetic and transport properties of CO2 in MOFs are less studied either. In present work, the intense literature surveys have been done to understand the experimental and computational accomplishments of CO2 adsorption behaviour on HKUST-1 and MIL-

68(In). It is evident that there is few work on the fluorinated MOFs for CO2 capture and adsorption, especially on HKUST-1 and MIL-68(In). Moreover, the study on CO2 diffusion properties is also scant compared with the tremendous programmes on the adsorption capacity. In order to investigate the performances of fluorinated HKUST-1 and MIL-68(In) for CO2 capture. The fluoride modification of structures of HKUST-1 and MIL-68(In) are carried out by Material Studio Visualizer. With the atomic partial charges and the force fields (UFF) being assigned, the GCMC simulation is employed to predict CO2 adsorption isotherms in MOFs. The MD simulation is implemented to explore the CO2 adsorption sites in MOFs and the CO2 self-diffusion during the adsorption process. Some major findings have been achieved: (I) the GCMC results exhibit that the both fluorinated MOFs have a significant improved CO2 adsorption capacity than that of non-fluorinated structures, indicating that the fluoride modification treatment is favourable to enhance CO2 adsorption, particular the HKUST-1. (II) MD simulation results including RDF and MSD measurement of HKUST-1F demonstrate the interaction between fluoride and CO2 is dominant at the low pressure which will restrict the diffusion of CO2 molecules, however, may be negative for the CO2 adsorption at the high pressure or CO2 desorption for adsorbent regeneration. (III) In the case of MIL-68, although MIL-68 and its fluorinated structure do not perform as well as HKUST-1 for CO2 adsorption under the pressure of 5 bars, it is expected to increase at higher pressures according to the adsorption isotherm tendency. Besides, the functional group (fluoride modification) may not be effective for the structural “open-up” of MIL-68 as there is no obviously significant CO2 adsorption increase from 0.1 to 5 bar.

6.2 Recommendations on future work

The further studies can be considered from this work; firstly, GCMC simulations can be implemented up to 20-30 bars to examine the CO2 adsorption on MIL-68(In) and its fluorinated structure to explore the adsorption increase at a specific threshold pressure. Secondly, the various fluoride modification sites on the organic linkers could be exploited, in this case, all of the H atoms have been replaced by fluoride, however, there are still many replacement options to compromise and enhance the CO2 adsorption capacity and self- diffusivity in MOFs. For example, the number of the fluoride modification can be one or two on the organic ligands and operational location can be 1.3, 1.5 or 3.5. Thirdly, with regard to the refinement of force filed, although UFF is a good approximation to describe the interactions in many MOFs and in this work, it may not be accurate for fluorinated HKUST- 1and MIL-68F (In) owing to the intense electrostatic field generated by the fluoride atoms.

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