Applied Science, Faculty of
Chemical and Biological Engineering, Department of
DSpace
UBCV
Imbault, Alexander Luis
Master of Applied Science - MASc
University of British Columbia
Biofuels make up to 10wt% of gasoline sold in the United States of America and Canada, the largest portion of these biofuel additives is ethanol. Past research working with MoP/SiO₂ using syn gas (H₂:CO 1:1) as a feed have shown how an effective oxygenate catalyst can be promoted with K to become more selective towards ethanol. In this thesis, various metal phosphide catalysts including CoP, Co₂P, RuP and Fe₂P (all supported on SiO₂) are investigated using molecular modelling and activity tests with syn gas to find an effective oxygenate catalyst which can be understood and ultimately converted into an effective ethanol catalyst. Molecular modelling is performed on metal phosphide catalysts to determine the CO adsorption strengths of several metal phosphide catalysts. The correlation between the adsorption energies and oxygenate formation is then examined. Fe₂P is shown to be a catalyst of interest for its ability to create sizable amounts of methanol and other oxygenates, and it has a moderate CO adsorption strength. The second focus of this thesis is on the effects of passivation on two metal phosphide catalysts, MoP/SiO₂ and Ni₂P/SiO₂. This is investigated through measuring H₂ uptake during TPR and CO uptake during CO chemisorption after different passiviiation techniques. The results of these measurements finds that Ni₂P/SiO₂ and MoP/SiO₂ react very differently after being kept out of contact with air, passivated in a low flow of O₂ or being exposed to ambient air after reduction.
Alcohol Synthesis Using Metal Phosphides by Alexander Luis Imbault B. Sc., McMaster University, 2011 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Chemical and Biological Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2015 © Alexander Luis Imbault, 2015 ii Abstract Biofuels make up to 10wt% of gasoline sold in the United States of America and Canada, the largest portion of these biofuel additives is ethanol. Past research working with MoP/SiO2 using syn gas (H2:CO 1:1) as a feed have shown how an effective oxygenate catalyst can be promoted with K to become more selective towards ethanol. In this thesis, various metal phosphide catalysts including CoP, Co2P, RuP and Fe2P (all supported on SiO2) are investigated using molecular modelling and activity tests with syn gas to find an effective oxygenate catalyst which can be understood and ultimately converted into an effective ethanol catalyst. Molecular modelling is performed on metal phosphide catalysts to determine the CO adsorption strengths of several metal phosphide catalysts. The correlation between the adsorption energies and oxygenate formation is then examined. Fe2P is shown to be a catalyst of interest for its ability to create sizable amounts of methanol and other oxygenates, and it has a moderate CO adsorption strength. The second focus of this thesis is on the effects of passivation on two metal phosphide catalysts, MoP/SiO2 and Ni2P/SiO2. This is investigated through measuring H2 uptake during TPR and CO uptake during CO chemisorption after different passiviiation techniques. The results of these measurements finds that Ni2P/SiO2 and MoP/SiO2 react very differently after being kept out of contact with air, passivated in a low flow of O2 or being exposed to ambient air after reduction. iii Preface All of the work presented henceforth was conducted in the Department of Chemical and Biological Engineering at the University of British Columbia, Point Grey campus. This dissertation is original, unpublished, independent work by the author, Alexander Luis Imbault. Dr. Kevin J. Smith was the supervisor of this research, involved throughout the project in brainstorming and thesis edits. iv Table of Contents Abstract ..................................................................................................................................... ii Preface ...................................................................................................................................... iii Table of Contents ....................................................................................................................... iv List of Tables ............................................................................................................................vii List of Figures ............................................................................................................................ ix List of Symbols and Abbreviations .............................................................................................. x Acknowledgements ...................................................................................................................xii Chapter 1 Introduction 1.1 Introduction ..................................................................................................................... 1 1.2 Importance of Ethanol and Current Production ................................................................ 4 1.3 Ethanol Production Processes .......................................................................................... 6 1.4 Objective ......................................................................................................................... 8 1.5 Approach ......................................................................................................................... 9 1.6 Phosphide Catalysts ....................................................................................................... 10 1.7 Density Functional Theory............................................................................................. 14 1.8 Computational Calculations ........................................................................................... 17 Chapter 2 Literature Review 2.1 Global Warming and the Demand for New Fuels ........................................................... 19 2.2 Alcohol Catalysts ........................................................................................................... 21 2.3 Metal Phosphide Catalysts ............................................................................................. 25 v 2.4 Modelling of Catalysts ................................................................................................... 28 Chapter 3 Experimental 3.1 Phosphide Catalyst Synthesis ......................................................................................... 33 3.2 Catalyst Activity Analysis .............................................................................................. 36 3.3 Characterization............................................................................................................. 39 3.4 Calculations ................................................................................................................... 40 Chapter 4 Catalysis Preparation and the Effect of Passivation 4.1 Results and Discussion .................................................................................................. 41 4.2 Conclusions ................................................................................................................... 53 Chapter 5 Synthesis Gas Conversion Over Metal Phosphide Catalysts 5.1 Results and Discussion .................................................................................................. 54 5.2 Conclusions ................................................................................................................... 67 Chapter 6 Conclusion 6.1 Conclusion .................................................................................................................... 68 Bibliography ............................................................................................................................. 71 Appendices Appendix A ............................................................................................................................... 83 Appendix B ............................................................................................................................... 97 vi Appendix C ............................................................................................................................. 106 Appendix D............................................................................................................................. 124 vii List of Tables Table 1.1 The synthesis gas conversion alcohol selectivity of selected Catalysts .................... 2 Table 1.2 Sulfur’s adsorption on molybdenum metal, carbide, nitride and phosphide ........... 10 Table 2.1 Copper cobalt chromium catalysts promoted with potassium ................................ 22 Table 2.2 Energy of adsorption for CO ................................................................................ 30 Table 3.1 Metal and phosphide precursor required for the synthesis of 15wt% metal phosphide catalysts ................................................................................................................................................ 34 Table 3.2 Reagent and support information .............................................................................. 35 Table 4.1 TPR and CO uptake data for 15wt% Ni2P/SiO2 and 15wt% MoP/SiO2 ................. 41 Table 4.2 BET surface analysis of compounds interest at various loading percents .............. 51 Table 5.1 Metal phosphide CO adsorption energy .................................................................. 56 Table 5.2 Summary of metal phosphide CO adsorption energies .......................................... 57 Table 5.3 CO adsorption of metal surfaces in literature ........................................................ 57 Table 5.4 Results of synthesis gas conversion on Co2P ........................................................ 59 Table 5.5 Results of synthesis gas conversion on CoP .......................................................... 61 Table 5.6 Results of synthesis gas conversion on Fe2P ......................................................... 61 viii Table 5.7 Surface area and crystal size summary ................................................................. 62 Table 5.8 Summary of Computed CO Adsorption Energies, Activity and Selectivity to CH3OH and C2+ oxygenates....................................................................................................... 66 ix List of Figures Figure 2.1 Paramagnetic (terpyridine)NiMe ......................................................................... 31 Figure 4.1 TPR of MoP ....................................................................................................... 46 Figure 4.2 TPR of Ni2P........................................................................................................ 47 x List of Symbols and Abbreviations Å = Angstrom (1x10-10m) BET = Brunaeuer Emmet Teller surface area analysis CHx = Methane fragment of varying amount of hydrogen between 1-4 DFT = Density functional theory DMol3 = A unique density functional theory (DFT) quantum mechanical code GC = Gas chromatograph GC-MS = Gas chromatograph with mass spectrometer GHG = Greenhouse gases Ĥ = Hamiltonian operator for the total energy of a system Ha = Hartree, Atomic energy unit (1Ha=627.5kcal/mol) HDA = Hydrodearomatization HDN = Hydrodenitrogenation HDS = Hydrosulfurization K = Kelvin (unit of temperature) Syn gas = Short for synthesis gas, mixture of CO, CO2 and H2 xi T = Temperature (oC or K) TCD = Thermal conductivity detector TPR = Temperature programmed reduction XPS = X-ray photoelectron spectroscopy XRD = X-ray diffraction Ψ = Wave function xii Acknowledgements For every time I open the door to our laboratory, place an order for research supplies or use an instrument I owe Dr. Kevin J. Smith a small thank you and they have built up over my wonderful time here at UBC so I would like to offer a very large thank you to Dr. Kevin J. Smith. He has been available whenever I needed guidance or suggestions and I have strived to follow his example in my work. I also wish to thank Dr. Anthony Lau and Dr. Heather Trajano for being part of my examination committee. The staff in the Chemical and Biological Engineering Department at UBC have been a great help to me especially the CHBE workshop technicians who have helped me an unreasonable number of times with experimental issues. My fellow group mates have helped and taught me a lot so thank you to Victoria Whiffen, Farnaz Sotoodeh, Shahin Goodarznia, Hooman Rezaei, Ramin Gholami, Pooneh Ghasvareh, Xu Zhao, Lucie Solnickova, Mina Alyani, Majed Alamoudi, Ali Alzaid, Shida Liu, Haiyan Wang, and Ross Kukard (who I definitely pestered for the most help). I would also like to thank Dr. Sharif Zaman who has often helped and pointed me in the right direction. The Natural Sciences and Engineering Research Council (NSERC) of Canada has my deep and heartfelt thanks for funding this project. I would like to thank my mother, Susan Bamford, who I can never thank enough and my little sister Constance Imbault for shaping me into who I am. I would also like to thank Xinyue Zhang for her encouragement and help with my terrible memory. 1 1 Introduction 1.1 Introduction Concern about sustainability and greenhouse gases has created a search for new fuels1. One fuel which is used as a fuel additive in gasoline blends and in its pure form to fuel cars in Brazil is ethanol2. Ethanol is considered to be a sustainable and carbon neutral fuel when produced from biomass1. For example standard gasoline sold in Brazil contains at least 25% ethanol produced from sugarcane that is grown on otherwise arable land. This process is very useful but not ideal as it creates a trade-off between using arable land used to grow food and using arable land to grow fuel. Another method of producing ethanol from biomass exists, using biomass to create syn gas (a mixture of H2 and CO) and then using the syn gas to create ethanol3. Several catalysts in commercial use can convert syn gas to ethanol however they do not have high selectivity4,5. The most selective catalyst is Rh, however Rh has a high cost and limited availability and thus is prohibitive to commercialization4,5. Several examples of ethanol and other alcohol producing catalysts are shown in Table 1.1. There are several less effective catalysts that have been created from non-precious metals such as MoS2, Co-Cu, Cu-Zn-Al and Cr-K4,5. 2 Table 1.1 The synthesis gas conversion alcohol selectivity of selected catalysts4,5 Catalyst CO Conv. T P H2:CO ratio Methanol Selectivity Ethanol Selectivity Alcohol Selectivity mol% K MPa wt% wt% wt% Rh 14 473-573 5.1 1.4 29 61 90 MoS2 10-40 473-573 3.4-20.6 1.1-1.2 30-70 0 85 Cu-CO 12-18 533-593 5.9-10.0 1-2 0 0 70-75 Zn-Cr-K 17 533-693 18.0-26.4 0.5-3 20-40 0 85 Cu-Zn-Al 20-60 523-693 5.0-10.0 1-1.2 53.5 0 53.5 Understanding what properties affect catalyst conversion and selectivity towards ethanol is an important step to refining and improving ethanol catalysts and thus the production of ethanol. One route to a selective ethanol catalyst is to assume that a selective alcohol catalyst is translatable to a selective ethanol catalyst. For example using K promotion3 or another method, an alcohol catalyst could be tuned to provide a high ethanol selectivity. By sampling a wide range of catalysts for alcohol selectivity and obtaining activity data for various catalysts, where the properties of the catalysts are also known, a correlation between the alcohol activity data and the properties of the catalyst may be developed. With these data one can tune properties in alcohol catalysts to favour ethanol production. Syn gas conversion over MoP has been investigated and has been shown to possess increased selectivity towards higher carbon alcohols with K promotion6,7. The goal of the present study is to determine if other metal phosphides are potential catalysts for syn gas conversion to alcohols. 3 If they show promise in alcohol synthesis, they could be further investigated and optimized to increase ethanol selectivity in a way similar to how MoP was optimized. 4 1.2 Current Production and Uses of Ethanol Ethanol is the most commonly produced biofuel for gasoline additives and the majority of the ethanol produced is for fuel8, however it is also used in antiseptics9, solvents and industrial feedstocks8,10. Ethanol production for industrial purposes has recently reached 148 million litres per day in the United States of America1 and 0.7 million litres per day in Canada11. The price for ethanol is volatile because of its link to volatile gasoline prices (all gasoline sold in the United States contains ethanol)12,13. Despite volatility in the price of ethanol, its production has grown steadily from 80,000 barrels per day in 1997 to over 400,000 barrels per day in 200712. The profit per gallon of ethanol is also volatile, ranging from a net loss per litre of ethanol produced in January of 2013 to a net profit of $7.56 per litre produced in March of 2014 before experiencing another dip in September of 2014 due to a drop in demand13. Ethanol prices are much more sensitive to demand shocks (lower consumer demand for ethanol) than to changes in subsidy levels or production costs (i.e. corn prices)12. The changes in subsidy or production costs can be transferred to higher prices and less to changes in production volume because of a very inelastic demand for ethanol that is fixed by regulations of gasoline12. In the United States of America the federal subsidy for ethanol has changed from $1.51/L to $2.04/L from 1997 to 200612. Although the primary use for ethanol is in biofuels as previously shown ethanol is important in several other industrial processes. With the addition of an alkali or alkali earth metal ethanol can become deprotonated (CH3CH2O-) and form a stable salt which is useful in organic chemistry reactions as a Grignard reagent14. The use of Grignard reagents in forming carbon-carbon bonds 5 is an important part of the synthesis of some drugs like Tamoxifen which is used to treat breast cancer15. Another use for ethanol is saponification (soap making) in the formation of ethyl esters which can be produced by a carboxylic acid and ethanol in the presence of a strong acid catalyst16. This reaction produces ethyl esters along with water, however the water needs to be removed as it is produced16,17. 6 1.3 Ethanol Production Processes The most typical method of producing ethanol (fermentation and distillation) is controversial for its use of arable land, potentially raising the price of food and requiring corn (or sugarcane) to be grown and harvested on otherwise arable land that could be producing food2. The use of the latter method for ethanol production is already in use in Brazil where hydrous ethanol is used as a fuel source for the majority of cars2. Fermentation uses yeast to metabolize glucose and sucrose into ethanol and carbon dioxide18. This is the primary method of synthesizing ethanol for fuel and for alcoholic beverages18. This method can only produce approximately 18% ethanol by volume before the yeast die18. Ethylene hydration which commonly uses phosphoric acid on a support to hydrate the double bond on ethylene in the presence of excess steam at 573K and 60-70 atmospheres to form ethanol, is a method for producing non-consumable ethanol18. The process was pioneered by Shell Oil Company in 1947 and is performed using an excess of pressurized steam18. The process relies on a petrochemical feedstock for ethylene18. This process has several issues, each pass over the phosphoric acid catalyst has low conversion (~5%) and after each pass the ethanol must be removed by condensation18,19. However in this condensation the steam also condenses and similar to fermentation, distillation is needed to recover the ethanol18,19. Higher ethanol-water mixtures from both ethylene hydration and fermentation need to be produced by distillation19. In water the highest volume percent ethanol azeotrope attainable is 95.6%, however ethanol with other hydrocarbons (i.e. benzene) forms a hydrocarbon-ethanol ternary azeotrope and can be used to produce ethanol with only trace amounts of hydrocarbon 7 left over19. There are other techniques for removing water using reverse osmosis, molecular sieves or desiccants allowing for the removal of water19. 8 1.4 Objective The objective of this thesis is to explore the properties and activities of several metal phosphide catalysts for syn gas conversion to alcohols. Data from the study will be used to understand the relationships between activity and chemical properties of metal phosphides, emphasizing the relationship between the strength of the CO adsorption on metal phosphide surfaces and the catalyst activity. This study will aim to produce a selective alcohol catalyst which in future work could be tuned properly to make a selective ethanol catalyst. The most important requirement of the catalyst is to produce alcohols selectively over hydrocarbons. Stability and cost of production are also important factors although less than alcohol selectivity. 9 1.5 Approach Both a theoretical and experimental approach was taken in this study. Experimental techniques were used to quantify catalyst properties and catalyst performance (activity and selectivity). Theoretical studies were done to obtain CO-metal phosphide surface adsorption data. Comparison of the theoretical and experimental data was used to identify relationships between catalyst properties and performance in syn gas conversion. There may be other steps in the conversion of syn gas to C2+ oxygenates however understanding the CO adsorption is an important first step to better understanding the mechanism and how to improve selectivity towards C2+ oxygenates. CO adsorption strength is obtained through molecular simulations by DFT of the metal phosphide surfaces, CO and CO chemisorbed on the surfaces. By taking the difference shown in equation 1.1 we can obtain the amount of energy needed to remove the CO molecule from the surface of interest. ECO-Surface=ECO on Surface-ECO-ESurface (Equation 1.1, CO-Surface bond strength equation) This thesis will present this information in a structured format using chapters starting with an introduction. The introduction will give the reader important background information on the relevant subjects. This will then lead into a selective literature review which will explain prior research that was done on the relevant topics and how the research in this thesis fits into the contemporary literature of this subject. The experimental data will then be presented describing catalyst preparation and experimental procedures. Then there will be a display of the results 10 obtained and a discussion about the meaning and relationships between the properties and activities of the metal phosphide catalysts that are being discussed. 11 1.6 Phosphide Catalysts Metal phosphides have been previously investigated in the catalysis of a variety of important processes3,20. A study of sulfur’s adsorption energy on MoC, MoN and MoP shows that catalytic potential increases in the order Mo<MoC~MoN<MoP based on observed chemical activity for HDN (hydrodenitrogenation) and HDS (hydrodesulfurization) reactions21. The reason that sulfur adsorption energy is used to model HDN reactions as well as HDS is the assumption that both HDN and HDS rely on an electrophilic species interacting with the surface21. However the bond strengths of sulfur to the surface follow a trend of MoN~MoC<MoP<Mo as shown in Table 1.2 below. Table 1.2 Sulfur’s adsorption on molybdenum metal, carbide, nitride and phosphide21 Surface Energy of Adsorption of Sulfur kJ/mol MoC(001) -618 MoN(001) -603 MoP(001) -647 Mo(001) -677 The relationship between chemical activity for HDN/HDS reactions and sulfur’s energy of adsorption is not direct because of Sabatier’s principle, which posits that the ideal catalyst’s 12 interaction with the substrate is neither too strong nor too weak22. Assuming there is an optimal S adsorption energy then the farther positive or negative that the real catalyst’s energy of adsorption with S is from the optimal value, the less chemically active the catalyst will be22. For example Mo interacts too strongly with S, conversely MoC and MoN interact too weakly with S and MoP is closest to the optimum adsorption energy for a desired interaction involving S and the surface. It is difficult to say if it is below or above the optimal value without further information probing at the chemical activity and a broader range of S adsorption energies on surfaces. A plot of the activity or selectivity of a catalyst versus the parameter that is being tuned is often called a volcano plot because of its mountain like appearance22. A similar study with CO is examined in Table 2.2 in Chapter 2.4. One goal of research into metal phosphide catalysts is to create a volcano plot of the catalyst selectivity to alcohols or C2+ oxygenates against CO-surface bond strength (or other parameter). The volcano plot should display a maxima for the most active catalyst and give an idea of the optimum value of the parameter being tuned (for a specific purpose), allowing future catalysts to be developed that achieve the optimum value. Supported metal phosphide catalysts that are synthesized in this report and in literature sources23 have a few steps in common. To deposit the metal phosphide precursor dropwise deposition is performed by dissolving the precursor to the metal phosphide in enough water to fill the pores of the support. This can sometimes be done in two steps because the precursor is insoluble in water, thus the metal precursor is deposited followed by a drying and calcining step and then the phosphide precursor is deposited followed by a drying and a calcining step. The drying and calcining steps are simply heating the catalyst in air to drive off water on the surface or in the 13 pores and deposit the precursor. The precursor is then reduced under heated H2 to form the desired metal phosphide. For activity studies and some characterization (i.e. chemisorption analysis) the catalyst is reduced and analyzed in situ without exposure to air, however, for many characterization methods it is impractical to analyze the reduced metal phosphide so a passivation step is required. The passivation step is done by cooling the sample to room temperature after the reduction and exposing it to a controlled amount of air for a period of time. This is done to protect the sample from uncontrolled oxidation in ambient air so that the characterization performed on the sample that was oxidized in a controlled way still relates to the reduced sample that is used for activity measurements as much as possible. However, the effects of passivation and allowing the sample to oxidize in an uncontrolled way in ambient air are not well understood despite being very important in relating the information obtained from characterization of the passivated metal phosphide to the properties of the reduced metal phosphide. 14 1.7 Density Functional Theory The Schrödinger wave equation is a partial differential equation which describes how systems evolve with time24. The Schrödinger wave equation is the basis of quantum mechanics and is the quantum mechanical analogue of Newton's laws for classical mechanics. Quantum mechanics differs from classical mechanics in that it can accurately describe the behavior of small slow systems by allowing for wave behavior from objects that are treated as particles in classical mechanics24. Classical mechanics accurately describes the mechanics of large slow systems and similarly special relativity describes fast large systems25 and relativistic quantum mechanics describes small fast systems26. The time dependent and time independent Schrödinger equation are shown in equations 1.2 and 1.3 respectively. In equation 1.2 the i is the imaginary unit (the square root of negative 1), the ħ is Plank’s constant divided by 2π, commonly referred to as ―h bar‖, Ψ is the wave function which when squared the absolute value (|Ψ|2) is equal to the probability density of finding a particle at a certain place at a certain time and when summed over all space is equal to 1 (the particle has to be somewhere, there is 0 probability of the particle being nowhere), Ĥ is the Hamiltonian operator which takes the total energy of any wave function and a simple 1 particle time dependent Hamiltonian operator is shown in equation 1.4. The time independent Schrödinger equation is an eigenvalue equation where the E represents the energy of the wave function Ψ. The time dependent Hamiltonian operator shown in equation 1.4 is an operator consisting of two parts, the first part represents the kinetic energy of the system and the second is the potential energy of the system. In equation 1.4, µ represents the reduced mass shown in equation 1.5 where m is the mass of the given particle, ∇ is the Laplacian operator a derivative with respect to 15 each special dimension, V(r,t) is the potential energy as it varies with r, the radius or relevant dimensions and t , the time. 𝑖ħ𝜕𝜕𝑡𝛹 = Ĥ𝛹 (Equation 1.2, Time dependent Schrödinger equation) 𝐸𝛹 = Ĥ𝛹 (Equation 1.3, Time independent Schrödinger equation) Ĥ = [−ħ22𝜇𝛻2 + 𝑉(𝑟, 𝑡)] (Equation 1.4, Time dependent Hamiltonian operator) 𝜇 =𝑚1𝑚2𝑚1 +𝑚2 (Equation 1.5, Reduced mass equation) The Born-Oppenheimer approximation was created by Max Born and Julius Robert Oppenheimer and allows for the nuclei and electrons to be deconvoluted27. This is an extremely important approximation in quantum mechanical calculations like density functional theory because it allows many body many interaction problems to be simplified to a solvable form27. Instead of each nuclei and electron interacting with every other nuclei and electron as a set of complex bodies all readjusting to any movement of the electron of interest it allows every nuclei and electron to see other nuclei as fixed and other electrons as a diffuse cloud27. This approximation relies on the assumption that nuclei are much more massive than electrons8 and 16 are comparatively stationary from the viewpoint of electrons27. Without the Born-Oppenheimer approximation the biggest analytically solvable system is the H atom with a single electron and single proton. With the Born-Oppenheimer an H2+ ion can be solved since the protons can be treated as stationary and there are no electron-electron interactions27. Electron-electron interactions are not analytically solvable and are what necessitate various approximations and basis sets to simplify multi-electron systems. Density functional theory is a way of simplifying the time independent Schrödinger wave equation28,29. Density functional theory was first developed by Llewellyn Thomas and Enrico Fermi in the Thomas-Fermi model in 1927 and was helped by Walter Kohn and Hohenberg Pierre by the Hohenberg-Kohn theorems in 196430. The original Hohenberg-Kohn theorems were only valid in the absence of a magnetic field and with no degenerate ground states but have since been generalized to include both these conditions28-30. Using approximations to solve equation 1.3 allows the energy of various systems to be ascertained and compared. By knowing the energy of various states we can understand kinetic and thermodynamic driving forces for a reaction (by calculating transition state energies and ground state energies). Specifically for catalysis this would allow the comparison of different CO-surface systems with the surface and CO separately giving the CO-surface adsorption energy allowing us to understand and measure another characteristic of catalysts. 17 1.8 Computational Calculations Computational calculations are an important part of modern chemical research and are often referred to as molecular modeling simulations. Calculations can be used to solve or help a wide number of problems ranging from protein modeling to materials science to drug design31-33. Molecular modeling can be broken up into three types of calculations ab initio, empirical and semi-empirical34. Ab initio (Latin ―from the beginning‖) calculations are calculations that use parameters to model electronic interactions and molecular properties without using experimentally derived simplifications34. The Hartree-Fock method was created in 1927 one year after the discovery of the Schrödinger equation and is still a popular starting point for many ab initio methods35. Hartree-Fock is an iterative method to solve many electron systems (since as discussed earlier there are no exact solutions to many electron systems) that allows each electron to ignore electron-electron interactions by having the electron ―see‖ a field of electronic charge35,36. The iterations are then repeated over and over for each electron so that the electron-field interactions tend toward a lower energy35,36. One of the biggest advantages of Hartree-Fock is that the energy derived from Hartree-Fock is always an upper bound to the actual energy of the system meaning that the calculated energy of a Hartree-Fock system is always greater than or equal to the ―real‖ energy of the system35,36. Empirical and semi-empirical calculations as the name suggests use experimental results to speed up the time a calculation takes to perform37-41. For example in a large organic molecule (i.e. proteins) instead of doing an ab initio calculation which may take a long time, the functional groups of the molecule can be identified and known data for the behavior of other molecules 18 with this functional group in similar circumstances can be used to simplify the calculation41. One of the simplifications to calculating organic molecules is called the Hückel method which was created in 1937 and allows π orbital interactions in conjugated hydrocarbons to be calculated by using molecular orbitals36-41. One of the potential issues with semi-empirical and empirical calculations is that the molecule that is being simulated must be similar to the known molecules in the database because the unknown molecule is being calculated by putting together known parts of other known molecules36. 19 2 Literature Review 2.1 Global Warming and the Demand for New Fuels Global warming was first described in 1827 by Joseph Fourier (the discoverer of the Fourier Transform) because of the atmosphere’s relative transparency to solar radiation (high energy, small wavelength) and opaqueness to thermal radiation (low energy, large wavelength)42,43. In 1861 John Tyndal discovered that water vapour is the gas most responsible for absorbing thermal infrared radiation and creating a greenhouse effect44,45. The temperature increase that is predicted by modern modelling varies from 1.5K to 4.5 K for a doubling of the concentration of CO2 in the atmosphere42. Such a shift in temperature would affect ecosystems negatively46,47 and cause glaciers to melt raising sea levels by 0.18-0.59m in the 21st century48. The source of the 1.5K to 4.5K discrepancy is in different assumptions made by models about cloud treatment and continuum absorption by water vapour42. The concern caused by CO2 emissions is that marginal increases in temperature will be caused by CO2 emissions that cause more absorption and thus keep more thermal energy in the atmosphere42. Heating the atmosphere then causes a higher level of water vapour in the air, further compounding the problem causing a 1.5K to 4.5K increase42. There have been many ways suggested to combat this issue including selective taxation49, alternative fuels50,51 and if global efforts to slow greenhouse gas emissions fail, seeding clouds52. In the United States of America the Environmental Protection Agency (EPA) has stated that transportation is the second largest cause of CO2 emissions behind electricity generation, at 33% and 42% respectively48,53,54. Despite increases in fuel economy from 1990 to 2010 there was still 20 a 19% increase in CO2 emission levels primarily from the transportation sector48,53,54. The primary method of containing CO2 emission levels is fuel efficiency standards, however the carbon balance of the fuel used is also important48,53,54. For example the carbon source in biofuels is CO2 already in the air that is converted into biomass which then becomes a fuel and is burnt giving off CO2. This biofuel is said to be carbon neutral since the CO2 was taken from the atmosphere and returned to the atmosphere. This is in contrast to gasoline which is refined from a carbon source that otherwise would have stayed in the ground and thus adds CO2 to the atmosphere that otherwise wouldn’t have been there48,53,54. Examples of common biofuels are alcohol fuels and mixed alcohol fuels. As the name implies alcohol fuels are pure (or almost pure) alcohol fuels and mixed alcohol fuels are traditional gasoline mixed alcohol48,53,54. In 2004 99% of all biofuel in the United States was ethanol and 12.9 billion liters of ethanol were blended into gasoline, accounting for 2% of the volume and 1.3% of the energy content48,53,54. In 2010 the United States Environmental Protection agency announced an increase in alcohol content to 10% by volume for future fuels, 136.3 billion liters, to try to obtain greater energy independence48,53,54. 21 2.2 Alcohol Catalysts Alcohol synthesis from syn gas has been investigated as a means to supplement traditional production methods of alcohols55,56. This section will discuss some of the popular transition metal based catalysts for alcohol production in addition to investigating other means for producing alcohols with a focus on commercial applications. Two broad studies have been commissioned by the U. S. Department of Energy into Rh catalysts with several transition and alkali metal promoters55. It was found that Mn and Fe as a promoter on Rh was found to be optimal for C2+ oxygenates (primarily C2 to C5 alcohols, acetic acid, acetaldehyde and ethyl acetate)1. The tests were performed for temperatures ranging from 528 to 618K and at 8106kPa55,56. A follow up study was performed (using the same conditions) using Mn as a promoter on a Rh catalyst and then adding 22 different transition and alkali metals as promoters56. They found that the highest selectivity towards C2+ oxygenates occurred at low temperatures and favour lower carbon C2+ oxygenates at higher temperatures, their reactions were carried out at 8.106MPa and temperatures varying from 528K to 675K56. It was found that Li had the highest selectivity to C2+ oxygenates at 47%56. Ir and Pt followed closely by Au had at their highest space time yield of C2+ oxygenates (810L/Lcat/hr, 660L/Lcat/hr and 500 L/Lcat/hr, respectively) a selectivity to C2+ oxygenates of 39%, 39% and 37% respectively56. In and Ga had the highest C2+ alcohol to C2+ oxygenate ratios at 0.71 and 0.67 respectively56. When Co/Cr catalysts promoted with K were tested they were found to produce exclusively hydrocarbons57,58. However when Cu is impregnated into the catalyst in a 0.6:1:1 ratio of Cu to Co to Cr the selectivity towards alcohols rises to 58%, selectivities are summarized in Table 22 2.157,58. C2+ oxygenates were reported to be much less common than C1 but the specific ratio was not reported57,58. By using different calcination temperatures for the copper cobalt chromium catalysts promoted by K it was observed that as the calcination temperature is increased the amount of oxygenates decreases57. Furthermore when comparing the cobalt chromium to the copper cobalt chromium catalysts promoted by K it was noticed that the hydrocarbon production was unchanged57. It was suggested from these findings that there are separate oxygenate creating sites and hydrocarbon creating sites57. Table 2.1 Copper cobalt chromium catalysts promoted with potassium57,58 Catalyst Selectivity to Hydrocarbons Selectivity to Alcohols Selectivity to Aldehydes wt% wt% wt% Cu0.6CoCrK0.05 40 58 2 CuCoCr0.8K0.09 33 58 9 CoCrK0.064 100 0 0 (H2:CO:Ar feeds of 58:28:14, with 5MPa and 548K with conversions <10%) Tests on copper cobalt nanoparticles using syn gas (H2:CO=2:1) have shown carbon based selectivies as high as 11.4% to ethanol and 42.5% to C2+ oxygenates (in separate runs)59. Selectivity towards ethanol was found to be highest at 270oC and C2+ at 300oC both performed at a pressure of 2MPa59. It is worth noting that the maximum methanol selectivity coincided with the maximum ethanol selectivity suggesting that their formations are not separate phenomena59. Dow Chemical and Ecalene HAS processes use a patented MoS2 catalyst, Dow Chemical’s 23 process is only used for bench scale applications however Ecalene HAS has announced plans to run a pilot plant for this process producing 690,000L/year of C2+ alcohols1. The Ecalene HAS process operates at 473K-573K and 3.4MPa-20.7MPa, it uses nanosized MoS2 and requires that a small amount of sulfur is present in the syngas feed1,59. The mechanism of C2+ oxygenate formation from syn gas has been investigated on CuZnO surfaces by using 13C rich methanol and ethanol combined with 13C NMR60,61. The mechanism proposed for C2+ oxygenate formation in the CuZnO system is the formation of an adsorbed COH with CO addition causing carbon growth either in a linear chain or branching (which was found to favor 2-methyl-1-propanol against what the flory distribution would predict for a growing polymer type system)60,61. The chain is then hydrogenated and desorbed in the termination step of the mechanism60,61. How syn gas is adsorbed on a catalyst surface and how the CO is dissociated for chain growth is of considerable interest62,63. The mechanism which explains CuZnO production of C2+ oxygenates is in contrast to what was found for corrugated Ru surfaces62,63. On corrugated Ru surfaces it was found that kinetically the barrier was significantly lower to change from a H+CO adsorbed onto the surface to a H+C+O adsorbed onto the surface rather than a HC+O or C+OH62. It is important to understand different mechanisms for CO dissociation on different surfaces because different mechanisms may produce different products. Through the knowledge of the mechanism a specific surface uses and what products it creates correlations could potentially be drawn which would help in the production of catalysts in the future. It has been suggested that one of the reasons that Rh based catalysts produce C2+ oxygenates efficiently is because of Rh’s electronic properties derived from its location on the periodic 24 Table64. Rh is located between transition metals such as Fe, Co which readily dissociate CO quickly to become hydrocarbons and metals such as Pd, Pt and Ir which adsorb CO more strongly to form methanol64. The mechanism was also found to be strongly related to dispersion as a 0.5% Rh loading on a low dispersion support (such as SiO2) tended to adsorb CO non-dissociatively however using the same loading on Al2O3 or TiO2 tended to adsorb CO dissociatively64. MoS2 catalysts have been studied primarily for HDS and HDN65however its efficacy and mechanism for catalyzing the formation of C2+ oxygenates from syn gas has also been investigated66. Contrary to the carbon growth then hydrogenation mechanism found for CuZnO CO addition and growth only occurs after hydrogenation66. For example CO deposited on MoS2 catalyst surface is hydrogenated and terminated in formic acid or methanol or is hydrogenated to CH3 losing water and then CO is added to form CH3CO and is similarly terminated after acetic acid or ethanol is formed (and continues this trend for propanoic acid/propanol, butanoic acid/butanol etc)66. 25 2.3 Metal Phosphide Catalysts Metal phosphide catalysts have a myriad of applications67-72 however we will try to focus our discussion on a few related topics. There are many metal phosphide catalysts such as early PtP catalysts used to yield phosphoric acid from phosphate and water73. However this section will focus on Mo, Ni and Fe phosphide catalysts. MoP catalysts show selectivity to oxygenated products significantly different to the selectivity obtained for Mo catalysts or MoC catalysts74. At 548K and 8.27MPa with 50/50 syngas the selectivity of K promoted MoP towards C2+ oxygenates was found to follow a volcano plot like trend when correlated to the K:Mo ratio74. The optimum value of K:Mo was found to be 3.25 at which point the selectivity on a CO2 free basis was found to be 77%74. Molybdenum phosphide catalysts have also been used in studies examining HDS75 and HDN76 reactions. MoP has also been shown to be a proficient C2+ oxygenate catalyst (maximum selectivity of 72.5 C%) and when promoted by K a proficient ethanol catalyst (maximum selectivity of 15.3 C%)3,6,7,77. Nickel has eight different phosphides78 and covers a broad array of catalysts79. It has been used in biological reactions such as the use of Ni2P to catalyze the transformation of cellulose to sorbitol at 498K and 6MPa of H2 giving a yield of 48.4% in a batch reactor after 90 minutes79. Additionally Ni12P5 nanospheres with ultraviolet (UV) light activation (254nm wavelength) can be used to catalytically degrade organic dyes at room temperature in aqueous solutions which could be useful in cleaning waste water80. Nickel phosphide catalysts also show a lot of promise as HDS catalysts when compared to commercial Co-Mo-S catalysts81. At 643K and 3.1MPa a sulfur reduction from 440 to 66ppm 26 was observed corresponding to a 85% conversion. For comparison, a commercial Co-Mo-S catalyst was observed to reduce the sulfur content from 440 to 86ppm corresponding to an 80% conversion81. It was also observed that the nickel phosphide compared favorably with the commercial Co-Mo-S catalyst in two other ways. The nickel phosphide had a lower hydrodearomatization (HDA) (0.7% for the nickel phosphide and 1.1% for the Co-Mo-S) which is desirable to lessen hydrogen consumption during processing and maintain the hydrocarbon character of the feed81. The nickel phosphide was also noted to reach steady state quicker than the commercial catalyst and did not display deactivation81. Nickel-calcium phosphate/hydroxyapatite catalysts were found to be active and selective to partial oxidation of methane to form syn gas82. A conversion of 25% for methane was found at a pressure of 1 atm (0.16atm CH4 a0.08atm O2 and 0.76atm Ar) at 923K for the first 30 minutes82. However it is very interesting to note that during the first 30 minutes no CO was formed but at the 1 hour measurement the conversion rose to 69% and had a CO selectivity of 75% and a H2 selectivity of 62% yielding a H2 to CO ratio of 2.882. At 1073K the conversion rose to 93% and gave CO and H2 selectivities of 96% and 90%82. Metal phosphide syntheses undergo several common steps including mixing metal and phosphide precursors in water3,7,18,23,67. If the metal phosphide is supported the mixture is then deposited on the desired support, if the metal phosphide is unsupported the aqueous mixture is then heated to drive off the water3,7,18,23,67. The metal phosphides generally undergo a drying and calcining step where high temperatures are used, sometimes with a controlled inert environment, to drive off any remaining water3,7,18,23,67. The metal phosphides are then either reduced in a heated hydrogen atmosphere in-situ for a characterization or activity measurement or are 27 reduced but then passivated with a low flow of oxygen at room temperature so that the metal phosphides can be handled in the atmosphere and used in other characterization methods3,7,18,23,67. The effects of passivation and how the data about a passivated catalyst relates to the unpassivated catalyst used in activity measurements has not been well studied but is assumed to only form a thin protective oxide layer that stops oxygen in the atmosphere from penetrating and oxidizing the bulk of the metal phosphide similar to how aluminum oxide protects the bulk aluminum. 28 2.4 Modelling of Catalysts Heterogeneous catalysis can particularly benefit from the use of molecular modeling because knowing the energy costs of different reactions on different surfaces allows investigators to tailor surfaces to favour desired reactions. Heterogeneous catalysis is traditionally a largely experimental field that is increasingly adapting to make use of computer modeling.83. Insight into pathways, predicting properties and thermodynamic data about catalysts, substrates and products are some of the benefits that modeling can provide to heterogeneous catalysis83. One example of contributions made to heterogeneous catalysis is in the field of zeolites where steric effects promote some reactions over others. This is achieved because desired reactants/products fit into the structure that the zeolite provides conversely undesired reactants/products do not84-87. Using a previously completed X-ray diffraction study to analyze the structure of the zeolite84, the zeolite framework’s interactions can be simulated for various reactions85-87. Knowing how different reactions are affected by different framework interactions a catalyst can be fine-tuned for desired reactions while discouraging undesired reactions85-87. Another application of computer modeling is in studying diffusion rates of substances allowing a greater understanding of mass transfer rates in a heterogeneous mixture. This was performed for decanes and n-methylnonanes of varying values of n88. This allowed catalytically relevant temperatures to be studied and the mass transfer phenomenon to be better understood88. It was discovered with modeling that the branch point of the methyl group affected the mass transfer rate of the substance and helped understand the catalytic behavior of the zeolite of interest88. Using molecular modeling information about surface-carbon monoxide interactions can also be 29 an enlightening exercise when studying catalysts21. With information about how different surfaces of different catalysts interact with CO, the reasons why one catalyst may catalyze certain reactions when other catalysts don't, can be better understood21. Modeling in this way could be used to assess the viability of a number of catalysts more quickly and cost efficiently than traditional experimental methods of probing a catalyst of interest21. The adsorption of CO on molybdenum metal, carbide, nitride and phosphide as well as the structural parameters of the surfaces for their (001) surface has been examined84. The goal of the study was to understand what would make a better HDN84, HDS87,88, ammonia synthesis88 or Fischer-Tropsch catalyst89. This was accomplished by trying to identify the relationship between absorption energy and catalytic activity, using DFT calculations the study found that the (001) surface of MoP was optimal based on its ―high stability and a reasonable chemical activity‖84. The adsorption energy for CO on all surfaces is reported in Table 2.284. It is important to notice that the study only examined the 001 surfaces and did not investigate any other surfaces84, giving an incomplete picture of what value CO adsorption may have on a real surface. DFT studies of MoP have been conducted using Mo6P3 clusters to simulate a metal rich (100) face of MoP3,77. The cluster was assumed to simulate the (100) surface of MoP because of the cluster’s similar adsorption energies to the (100) surface of MoP. The study found that the formation of CH3OH was less likely than CH4 on the cluster3,77. These species compete for formation from a CH2OH intermediate that, with the addition of a H and loss of H2O, can form CH4 or gain a single hydrogen to form CH3OH. The CH3OH energy barrier was found to be much higher than the formation of CH2 and H2O which will lead to methane3,77. 30 Table 2.2 Energy of adsorption for CO2 Surface Energy of Adsorption of CO kJ/mol MoC(001) 169 MoN(001) 168 MoP(001) 191 Mo(001) 222 Theoretical studies specifically about CO adsorption and dissociation on transition metal surfaces have been performed89. CO is found to prefer a perpendicular ―standing‖ configuration (with carbon being the closer of the two atoms to the metal surface) to a parallel ―laying down‖ position, whereas the opposite is true for diatomic molecules of the same element (i.e. O2, N2)89. The rational suggested is that carbon is less electronegative than oxygen89. The CO is stabilized by back donative interactions using the CO’s 2π* orbitals and the metals d atomic orbitals, this splits the normally anti bonding 2π*89. This split is wider for a higher coordination number of metal atoms to CO and a higher split means a stronger stabilizing effect89. Through mechanistic studies it has also been determined that CO dissociation reaches a maximum rate below maximum CO coverage indicating that CO dissociation requires an empty surface site beside the dissociating CO89. On a simulated surface of Rh metal, a dissociating CO favored hollow sites for both the carbon and oxygen atoms over the carbon and oxygen coordinating to a single Rh each (similar to the previous 2π* obtaining the best interaction with higher coordination number)89. Information about CO adsorption and dissociation rates, for example a stronger adsorbed and higher amount of CO bonded on the surface could give more 31 time to form longer carbon chains, conversely a weak adsorption and sparse CO deposition could make carbon-carbon bond formation less likely and favour methane and methanol. Through the use of DFT, the mechanism of paramagnetic (terpyridine)NiMe (shown in figure 2.1) to catalyze alkyl-alkyl cross coupling reactions was investigated90. Alkyl-alkyl cross coupling is a method for catalyzing carbon-carbon bond formation using either nickel or palladium91. DFT was used to obtain the electronic structure of paramagnetic (terpyridine)NiMe both by itself and of different conformational isomers to better understand its electronic properties and from this better understand the mechanism for the alkyl-alkyl cross coupling reactions of interest and a new mechanism being proposed to replace an older mechanism90,91. Figure 2.1 Paramagnetic (terpyridine)NiMe90 DFT was used to examine the active sites and structure of molybdenum disulfide for the purposes of HDS92. Through DFT analysis and EXAFS (extended X-ray absorption fine structure) different promoters such as cobalt, nickel and iron were examined in different amounts92. It was found that while cobalt and nickel increase the activity to very similar degrees iron has a negligible effect4. This is useful for creating better promoted molybdenum disulfide catalysts for HDS in the future and generally provides an example of the sort of parameters that can be investigated92. 32 2.5 Conclusion With climate change as a motivating force to discover new fuels alcohol based fuels are a particularly promising source as a fuel additive. Alcohols when created from biomass or other carbon neutral sources puts no new CO2 into the atmosphere, thus lowering the impact of transportation on the environment. However one of the challenges posed is the problem of alcohol fuel additive synthesis. There are important opportunities for research in different catalysts which can catalyze the formation of alcohols. Particularly metal phosphides which in the case of MoP have been shown to create several C2+oxygenates and have an increased selectivity towards ethanol when promoted with K3,7,77. With many possible metal phosphides there are many opportunities for theoretical as well as experimental studies of metal phosphides. Using both theoretical and experimental analysis may illuminate correlations between different variables that are only examinable by either theoretical or experimental methods (i.e. the structure of adsorbed species on a surface or bond strengths between adsorbed species and the surface are more easily examined by molecular modeling, conversely activity measurements and chemisorption measurements are more easily examined by experimental methods). 33 3 Experimental 3.1 Phosphide Catalyst Synthesis Wetness impregnation is done by dissolving the metal phosphide precursors in a solvent (for this study 15.6mL of water in all cases) and adding the solution to the support dropwise. In this study 10g of silica gel was used as the support in all cases. A sonicator was used to ensure proper mixing of the aforementioned metal phosphide precursors in 15.6mL of water (for 10 minutes typically) and the solution is taken up in small portions by a pipette and applied equally over the silica support by hand until the solution is consumed. The catalyst preparation in this thesis is summarized in Table 3.1 for metal phosphides of 15wt% using 10g of silica and 15.6mL of water. Information about the reagents is given in Table 3.2. 34 Table 3.1 Metal and phosphide precursor required for the synthesis of 15wt% metal phosphide catalysts Catalyst Being Prepared (Metal Precursor) Amount of Metal Precursor g Amount of Diammonium Phosphate ((NH4)2HPO4) g Fe2P (Iron (III) Nitrate nonahydrate (Fe(NO3)3 9H2O)) 8.515 1.388 RuP (Ruthenim (III) chloride trihydrate (RuCl3(H2O)3)) 1.980 1.010 Co2P (Cobalt (II) nitrate hexahydrate (Co(NO3)2 6H2O)) 7.219 1.638 CoP(Cobalt (II) nitrate hexahydrate (Co(NO3)2 6H2O)) 3.609 1.638 MoP (Ammonium molybdate tetrahydrate ((NH4)6Mo7O24 4H2O)) 2.086 1.561 (It is important to note that the ratio for the precursors in the synthesis of Ru:P is 1:1.1 to produce RuP23) 35 Table 3.2 Reagent and support information Chemical Manufacturer Purity Diammonium phosphate ((NH4)2HPO4) Sigma-Aldrich Reagent Grade 99% Silica gel, grade 62, 60-200 mesh, pore size 150Å, BET=330m2/g Sigma-Aldrich N/A Cobalt (II) nitrate hexahydrate (Co(NO3)2 6H2O) Sigma-Aldrich A.C.S. reagent, 98+% Ammonium molybdate tetrahydrate ((NH4)6Mo7O24 4H2O) Sigma 81.0-83.0% Iron (III) nitrate nonahydrate (Fe(NO3)3 9H2O) Sigma-Aldrich A.C.S. reagent, 98+% In the syntheses of cobalt phosphides the precursor contains two compounds which when mixed, form an insoluble cobalt phosphate precursor. This problem is resolved by depositing only one of the compounds which forms the precursor (the metal containing reagent was added first, i.e. for Fe2P iron (III) nitrate nonahydrate), drying and calcining the support then depositing the other compound (the phosphide containing reagent, in all cases diammonium phosphate) needed and repeating the drying and calcining steps. The solvent’s volume should equal the support’s pore volume for both depositions (15.6mL for 10g of silica gel). For all preparations, the impregnated support was dried at room temperature in air for 12h and then placed in an oven at 383K for 12h. The catalyst precursors were then calcined for 5h at 36 773K in air before being sealed in a u-tube for reduction. Temperature programmed reduction was done by heating the sample from room temperature to 923K at a rate of 1K/min and holding at 923K for 2h under a flow of 60cm3(STP)/min of H2 and 20cm3(STP)/min of Ar at atmospheric pressure. Next, in the relevant studies, passivation was performed at ambient temperature and pressure for 2h under a flow of 20cm3(STP)/min of Ar and 2cm3(STP)/min of O2. 37 3.2 Catalyst Activity Analysis Catalyst activities were measured in a laboratory fixed-bed reactor (o.d.=9.53mm and i.d.=6.35mm, copper lined stainless steel tube) with a thermocouple placed inside to measure the temperature in the middle of the catalyst bed. All experiments used a 50% CO 50% H2 mixture of syn gas with the reactor operated at 523K, 548K, 573K and 598K and a pressure of 7.58MPa, with a GHSV=3494h-1. A high temperature back pressure regulator was used to control the reactor pressure. In all measurements 0.5g of supported catalyst was loaded and held in place with quartz wool. All catalysts were reduced in-situ identically, following the procedure already described (1K/min from room temperature to 923K hold for 2h in 60cm3(STP)/min of H2 and 20cm3(STP)/min of Argon). The reactor effluent was analyzed with an on-line GC (Perkin Elmer Clarus 500 Gas Chromatograph) and MS (Perkin Elmer Clarus 560 S Mass Spectrometer) set up. The GC column temperature changed from 323K to 373K at a rate of 10K/min then from 373K to 463K at a rate of 15K/min and finishing by heating from 463K to 473K at a rate of 10K/min and holding at 473K for 5 minutes for the analysis of all runs. After the removal of a component from the GC set up, the first 15wt% CoP/SiO2 run discovered that the CO and CH4 peaks were eluting close to each other so for subsequent 15wt% CoP/SiO2 and 15wt% Fe2P/SiO2 runs a second method was used. The second method ramped from 323K to 353K at a rate of 5K/min then ramped from 353K to 573K at a rate of 15K/min and held at 573K for 3minutes for a total run time equal to the previous method of 17minutes. In both cases a condenser is used upstream of the GC-MS to separate some of the components also for both cases a split ratio of 25 to 1 was used for allowing gas to be transferred from the GC to the MS. The column used was a Perkin Elmer Elite-Plot Q with a length of 30m an i.d. of 0.32mm and a film thickness of 1.4 μm. 38 GC-MS peaks were integrated and used to quantify products by comparison to a gas of known composition. Three injections of only calibration gas of a known mix were used to calculate the components in three injections that were made by passing the same known calibration gas through an ethanol bubbler (H2 and N2 were calculated by assuming their concentrations dropped by the same ratio as the other components) and then by difference calculate the mol% for etha-nol. With the peak areas and mol%s known a ratio is created of mol% to peak area so that raw peak areas from activity measurements can be multiplied by the ratio to obtain a mol%. For spe-cies that aren’t present in the calibration gas the closest analogue is used, for example butane’s calibration value is used as a substitute for pentane or hexane. Conversions were determined by using the measured out flows of the various products then cor-relating this to the known inflow of CO from a known feed gas with a corrected flow rate for wa-ter lost. Selectivities were determined by the percent of the product in the total out flow which was calculated using the known standard divided by the total of the percents for all the products. 39 3.3 Characterization Temperature programmed reduction and chemisorption analysis was performed in a Micromeritics Autochem II Chemisorption Analyzer. TPRs are performed by a drying step heating to 393K for 45 minutes with a flow of Ar then cooling to 328K followed by the TPR heating from 328K to 923K at 2K/min and holding for 1h. After this the sample is cooled to 328K and is exposed to a flow of Ar and pulses of 85% Ar and 15% CO. The effluent is analyzed by a thermal conductivity detector to ascertain the CO uptake. A AgO standard is used to calibrate the H2 uptake and a Pt-Al standard is used to calibrate the CO uptake observed. BET analysis was performed on a Micromeritics ASAP 2020 Surface Area and Porosity Analyzer. Samples of approximately 0.1g were degassed at 623K for 4 hours and analyzed in N2 at -196oC. A t-plot was then used to obtain micropore area from the adsorption desorption curves that were observed from the N2 adsorption data. X-ray diffraction was performed on samples using a Bruker D8 Focus theta-2theta diffractometer with a cobalt X-ray tube and LynxEye detector. Samples were scanned from 10-80o 2θ with a rate of 0.04o/second for a total run time of 31 minutes. The X-ray photoelectron spectroscopy (XPS) was performed using a Leybold MAX200 XPS using the aluminum Kα excitation. The survey scan was performed at 192eV and the narrow scan was performed at 48eV. 40 3.4 Calculations The software package used in molecular modelling was Materials Studio (version 4.0) from Accelrys Inc. using a module called DMol393. Electronic wave functions are expanded using numerical atomic basis sets defined on an atomic centered spherical polar mesh. Double numerical plus d-function (DND) all electron basis set as well as the Becke exchange94 plus Perdew-Wang approximation95 non-local functional (GGA-PW91) were used in all calculations. All basis sets used a 4.0Å cutoff to simplify calculations. Kohn-Sham equations96 were used in the self-consistent field (SCF) procedure. Direct inversion in an iterative subspace (DIIS)97 with a size value of 6 and a thermal smearing of 0.009 Ha were used to simplify calculations98. Optimization convergence thresholds for energy change, maximum force and maximum displacement were set at 0.00005Ha, 0.002Ha/Å and 0.005Ha respectively. The k-point set used was 3x3x1 was used in all calculations. When making cutting crystal structures to make the various surfaces studied in this thesis the cuts were aimed at higher metal content surfaces. The surfaces were 1x1 on the top of the surface and were 6 layers deep, the bottom four layers were restricted in their movement during optimization to allow optimization to succeed more often and quickly. Although both bridge and standing CO configurations were tested on all surfaces all the metal phosphides modeled in this thesis were more stable in a standing configuration. During optimization CO was placed on metal sites, phosphide sites and hollow sites however the CO optimized to single metal sites in all cases with the carbon pointing toward the metal site and the oxygen pointed away. 41 4 Catalyst Preparation and the Effect of Passivation Passivation is the process of applying a controlled oxidation to a reduced catalyst. For example after reduction the catalyst would be cooled and exposed to a small amount of oxygen diluted with an inert gas for several hours. The intention of this processes is to create a thinner and less destructive oxide layer than if it were exposed directly to the atmosphere as well as creating a protective oxide layer that will prevent further oxidation similar to aluminum oxide protecting bulk aluminum. This process is used in the investigation of many metal phosphide catalysts and their characterization3,14,20,55,56 and is used in the majority of the characterization of metal phosphides investigated in this thesis. However the process of passivation has not been well investigated. Yet it is important to understand passivation as many of the characterization techniques are performed on passivated metal phosphides ex-situ. 42 4.1 Results and Discussion In an effort to better understand the process of passivation, TPR and CO uptake experiments were performed on Ni2P and MoP both with 15wt% metal phosphide and supported on silica, following three different passivation circumstances. All samples were reduced first and then exposed or not exposed to air, in one of the three ways described below, and then H2 uptake was monitored during a TPR (a second reduction or a re-reduction to measure oxidation that occurred after the first reduction) before doing CO uptake measurements. The three passivation circumstances were (i) reducing the phosphide and transferring the phosphide under a nitrogen atmosphere by using a gas bag set up to perform sample preparation with minimal exposure to air (airless), (ii) reducing the phosphide then cooling the sample to room temperature and exposing it to air (unpassivated) and (iii) reducing the phosphide then passivating the phosphide in a flow of 20cm3(STP)/min of Ar and 2cm3(STP)/min of O2 for 2 hours at ambient temperature and pressure (passivated). Each analysis was repeated 4 times and for the passivated and unpassivated samples the weight of the sample in all cases was 0.1g, whereas for the airless samples, due to experimental constraints in keeping the sample free of air, 0.1g was aimed for but the true weights were ascertained after the measurement. The measured TPR and CO chemisorption uptake data are summarized in Table 4.1. It is important to understand the link between the reduction that is being observed in Table 4.1 and the oxidation that occurred after the first reduction of the relevant sample. In all cases (airless, passivated and unpassivated) the sample underwent reduction and then was exposed to air in one of the three ways (airless samples are only exposed during a short period when they are being prepared for analysis). The hydrogen uptake that is recorded in Table 4.1 is a second 43 reduction and therefore is a measurement of the oxygen uptake that is occurring during the previously explained exposures to oxygen after the first reduction. Table 4.1 TPR and CO uptake data for 15wt% Ni2P/SiO2 and 15wt% MoP/SiO2 TPR Chemisorption Sample H2 Uptake Degree of Reduction CO Uptake Dispersion µmol/g Mol% µmol/g Mol% Ni2P - Airless 517 ±35 7.2 ±0.5 2 ±4 0.2 ±0.3 Unpassivated 1426 ±291 19.7 ±4 27 ±16 2.1 ±1.3 Passivated 1410 ±421 19.5 ±5.8 4 ±5 0.3 ±0.4 MoP - Airless 1422 ±86 12.7 ±0.8 108 ±35 9.2 ±3.0 Unpassivated 2941 ±454 26.2 ±4.1 76 ±41 6.4 ±3.5 Passivated 3478 ±677 31.0 ±6.2 67 ±27 5.7 ±2.3 (All uncertainties are ±95% confidence values, dispersion is defined as CO molecules divided by metal phosphide molecules. Samples were isolated from air, exposed to air without passivation and exposed to air after a passivation for 2h at room temperature in 20cm3(STP)/min of Ar and 20cm3(STP)/min of O2. Uncertainty was calculated for 95% confidence and all samples were run 4 times each. These results are summarized from Table B.1 and Table B.2 in appendix B.) The degree of reduction was calculated by assuming a phosphate is formed during the calcination and the H2 consumption reflects the reduction of the phosphate to the phosphide according to Equation 4.1 for MoP and Equation 4.2 for Ni2P. The actual amount of hydrogen per gram of 44 MoP or Ni2P taken up is then divided by the ratio of hydrogen to MoP or Ni2P, respectively, to obtain the degree of reduction. The dispersion was calculated by taking the real CO uptake of MoP or Ni2P in the sample being analyzed and dividing it by the maximum amount of CO adsorbed onto the surface of MoP or Ni2P if one CO molecule was adsorbed for every molecule of Ni2P or MoP. 2MoO3P2O5+19H2-->2MoP+16H2O+2PH3 Ratio H2/MoP=19/2=9.5 (Equation 4.1, Assumed MoP phosphate reduction) 4Ni3(PO4)2+35H2 → 6Ni2P+32H2O+2PH3 Ratio H2/Ni2P=35/6=5.8 (Equation 4.2, Assumed Ni2P phosphate reduction and ratio of Ni2P to H2 consumed) An example of a TPR of both Ni2P and MoP in airless, passivated and unpassivated are summarized in Figures 4.1 and 4.2. The TPR procedure started at 323K and increased in temperature at a rate of 2K/min, until reaching 923K. The final temperature of 923K was then held for 1h. From the data obtained in Table 4.1 several correlations can be drawn. These conclusions will be summarized here and explained in the following paragraphs. The first is that oxidation appears to occur more readily on MoP than on Ni2P (as indicated by higher degree of reduction for airless MoP than for airless Ni2P). The easily oxidized portion of Ni2P (what is oxidized even when only exposed to air for a short period of time in the airless runs) has one very characteristic H2 uptake peak (occurring at 55min and 450K) that is visible in airless, unpassivated and passivated samples. Furthermore the easily oxidized portion doesn’t vary as much as unpassivated or passivated H2 uptake does. The effects of passivation on CO uptake are also very different, in Ni2P passivating the catalyst appears to preserve the low CO uptake 45 character of the airless Ni2P whereas passivated or unpassivated MoP display similar CO uptakes. 46 Figure 4.1 TPR of Ni2P0 50 100 150 200 250 300 350 TCD Signal (a. u.) Time (min)(TPR of airless, unpassivated and passivated for Ni2P. From top to bottom, airless Ni2P, unpassivated Ni2P and passivated Ni2P.) 47 Figure 4.2 TPR of MoP0 50 100 150 200 250 300 350 TCD Signal (a. u.) Time (min)(TPR of airless, unpassivated and passivated for MoP. From top to bottom, airless MoP, unpassivated MoP and passivated MoP.) 48 Comparing MoP H2 uptake to Ni2P H2 uptake it is visible in all six cases that MoP is oxidized much more strongly after its initial reduction than Ni2P. To further illustrate this, Ni2P has reduction percents of 7.2%, 19.7% and 19.5% vs. MoP’s 21.9% and 45.3% and 53.5% for airless, unpassivated and passivated, respectively. In all cases the reduction percent of MoP is more than double Ni2P’s reduction rate. This demonstrates that MoP is more susceptible to oxidation after its initial reduction than Ni2P. Higher H2 uptake could be indicative of a high oxide diffusibility for MoP than the oxide of Ni2P, allowing oxygen in the ambient air to diffuse further into the MoP than into the Ni2P. It is also clear that the temperature at which H2 uptake occurs for Ni2P and MoP is very significant and different. In both cases of airless Ni2P and MoP there is a single principle peak, however the Ni2P airless peak occurs at 55min and 450K and the MoP airless peak occurs at 225min and 775K. The MoP peak occurs at a higher temperature indicating that the portion that is oxidized quickly is more difficult to reduce than the portion that oxidizes quickly in the Ni2P. The Pilling-Bedworth ratio99 for predicting if an oxide is protective or not can be applied to Ni, Mo and their respective oxides. The Pilling-Bedworth ratio, shown in Equation 4.3, computes the ratio of the molar volume of the oxide to the molar volume of the metal. Ratios less than 1 are described as too thin or porous and don’t protect the bulk metal ( i.e. Mg), ratios greater than 2 are described to be too thick and chip off to form pits (i.e. Fe), ratios between 1 to 2 form protective oxides (i.e. Al). Ni forms a protective oxide and has a Pilling-Bedworth ratio of 1.65, Mo forms MoO3 which has a Pilling-Bedworth ratio of 3.29 and so does not form a protective oxide99. 49 𝑅𝑃𝐵 =𝑉𝑜𝑥𝑖𝑑𝑒𝑉𝑚𝑒𝑡𝑎𝑙=𝑀𝑜𝑥𝑖𝑑𝑒 𝜌𝑚𝑒𝑡𝑎𝑙𝑛 𝑀𝑚𝑒𝑡𝑎𝑙 𝜌𝑜𝑥𝑖𝑑𝑒 (Equation 4.3, Pilling-Bedworth ratio99 V is volume, M is the molar mass, ρ is the density and n is the number of metal atoms per molecules of oxide) It is informative to know that Mo does not form a protective oxide while Ni does, because similar effects are also noticed for the associated metal phosphides. As stated previously, the H2 uptake and degree of reduction of the second reduction (that is measuring and reducing the metal phosphides oxide after the first reduction) is much smaller in passivated Ni2P than passivated MoP (1410µmol/g and 7.2% for Ni2P and 3478µmol/g and 31.0% for MoP). This is what might be expected if the metal phosphides behave similarly to the pure metals, because Ni forms a protective oxide, protecting the bulk Ni, whereas Mo does not form a protective oxide. It is very interesting that for Ni2P, the process of reduction, passivation, exposure to air and then a second reduction (and without the passivation) seems to severely reduce the CO uptake observed (the passivated and airless cases). However the unpassivated sample displays a significant amount of CO uptake 26.5µmol/g, however this is still lower than the CO uptake found by Li et al.100 of 67µmol/g for a fresh sample that under went a slightly different TPR (723K for 1h) only once (versus twice in this thesis). The key difference between the unpassivated and passivated/airless TPRs for Ni2P is the appearance of the third and last peak which appears at high temperature and may correspond to the subsequent low CO uptake. The extended exposure to air followed by a second TPR is the differentiating factor between the unpassivated Ni2P sample and the passivated or airless Ni2P samples. It was expected that after TPR of an airless Ni2P sample, the CO chemisorption would be the 50 same as if the chemisorption was measured without a second TPR. To test this disagreement between intuition and results a TPR and CO chemisorption were performed on a calcined sample of Ni2P showing H2 and CO uptake. Another TPR and CO chemisorption was then performed on the same sample (without removal from the unit or exposure to air) and very low H2 uptake and no CO uptake was observed. These results are summarized in Table B.3 in appendix B. This seems to confirm that the second TPR is somehow destructive of the CO uptake capacity of the Ni2P surface and denies the intuition that was posited originally. BET characterization was also performed to give an indication of the surface structure of the catalysts being examined and are summarized in Table 4.2. Surface areas range from 15wt% MoP/SiO2 on the low end at 164m2/g to 15wt% CoP and 15wt%Co2P which had surface areas of 282 and 294m2/g respectively both around what the silica support itself was found to be at 286m2/g. 51 Table 4.2 BET surface analysis of metal phosphides supported on silica Wt% Sample BET Surface Area Micropore (t-plot) Area External Surface Area m2/g m2/g m2/g 15% MoP/SiO2 164 62 102 18% CoP/SiO2 282 11 277 18% Co2P/SiO2 294 31 212 15% Fe2P/SiO2 216 33 191 15% Ni2P Unpassivated/SiO2 257 19 237 15% Ni2P /SiO2 186 60 126 Silica Support 286 42 244 BET measurements of the surface areas of the metal phosphides show that CoP and Co2P display a low reduction in surface area compared to the silica support (282m2/g, 294m2/g and 286m2/g respectively). MoP and Ni2P show a lower surface area (164m2/g and 186m2/g respectively), it is interesting to note that the unpassivated Ni2P that was examined by BET showed a higher surface area than the passivated Ni2P. Data obtained by XPS was used to ascertain the Mo content versus Mo6+ (MoO3 presumably) for airless, passivated and unpassivated samples. This data is summarized in Table 4.3. The samples were prepared and synthesized then analyzed by a third party. The airless sample was contained in an airless environment until it was given to be analyzed. The airless sample experienced be-tween 20 to 30 minutes of exposure to air because the facilities used to analyze the samples did not have any capability to protect the samples from air during preparation. 52 Table 4.3 XPS analysis of 15wt% MoP supported on silica Sample Mo Peak Area 228.3eV (a.u.) Mo6+ Peak Area 232.5eV (a.u.) Mo Peak Area to Mo6+ Peak Area Ratio 15wt% MoP Airless 4752 48558 0.097 15wt% MoP Passivated 0 44039 0 15wt% MoP Unpassivated 0 48503 0 Table 4.3 shows that the airless procedure developed for this study is valid and is useful in pre-serving some metal character from oxidation. However as previously mentioned the XPS facili-ties used lacked an airless sample preparation so although the air exposure was minimized be-forehand the sample was exposed to air before the measurement, if the sample was prepared in an air free environment it is expected that the Mo/Mo6+ ratio would be higher. 53 4.2 Conclusions The passivation study of Ni2P and MoP has shown that the effects of passivation are not the same for these two catalysts. A passivated catalyst may have different properties than the reduced in situ catalysts that are trying to be simulated when characterizing a passivated catalyst. Reduction, exposure to air and re-reduction can be damaging to the catalyst’s original properties, for example the catalysts ability to adsorb CO effectively. Study of the passivation process for more catalysts could be instructive of what passivation means for those catalysts and may prove necessary to validate any characterization of a passivated catalyst. Furthermore in general it may be elucidative, where possible, to test the properties of an airless sample of a catalyst instead of a passivated one despite the increased experimental complexity. 54 5 Synthesis Gas Conversion Over Metal Phosphide Catalysts Past research in the synthesis gas conversion of MoP has shown how a strong oxygenate catalyst can be tuned by K promotion into a strong ethanol catalyst3,6,7,77. This has motivated an interest in other metal phosphide catalysts in an effort to find better ethanol catalysts which can create biofuels from syn gas efficiently. In an effort to further understand and tune the catalysts to produce oxygenates and ultimately ethanol the CO adsorptions are also examined through molecular modelling. The reason that CO adsorptions are examined is the assumption that the strength of the CO-surface adsorption is a determining step in what the catalyst creates from syn gas. For example if CO adsorption is too weak the CO may not stay on the surface for long and it may tend to favour smaller, low carbon products like CH4 or CH3OH conversely a CO adsorption that is too strong may favour long chain hydrocarbons or alcohols creating waxy products or not reacting at all because the CO is bound too strongly to the surface. The goal is to find an ideal CO adsorption energy by correlating CO adsorption energy to selectivity towards desired products. 55 5.1 Results and Discussion Adsorption energies were obtained by calculation of the total energy of the chosen surface, CO, and CO on the chosen surface by taking the difference between them, shown in Equation 5.1. These were obtained by using the parameters summarized in section 3.4. The results of the ad-sorption energy calculations are shown in Table 5.1 and summarized in Table 5.2. Comparison with literature values are shown in Table 5.3. ECO-Surface=ECO on Surface-ECO-ESurface (Equation 5.1, CO-Surface bond strength equation) 56 Table 5.1 Metal phosphide CO adsorption energy Compound (Miller Indicies) Adsorption Energy of CO on Compound kJ/mol CoP (100) -227 CoP (010) -135 CoP (001) -169 CoP (111) -103 Co2P (100) -105 Co2P (010) -105 Co2P (001) -112 Co2P (111) -139 Fe2P (100) -125 Fe2P (010) -158 Fe2P (001) -123 Fe2P (111) -160 RuP (100) -195 RuP (010) -158 RuP (001) -186 RuP (111) -203 Ru2P (100) -219 Ru2P (010) -188 Ru2P (001) -164 Ru2P (111) -211 57 (Calculated by taking the difference between CO adsorped on the surface and CO and the surface separately. In all cases the CO is bonded in a standing configuration with the carbon being closest to the surface.) Table 5.2 Summary of metal phosphide CO adsorption energies Compound Range of Adsorption Energies Average of Adsorption Energies kJ/mol kJ/mol CoP -103  -227 -159 Co2P -105  -139 -115 Fe2P -123  -160 -142 RuP -158  -203 -186 Ru2P -164  -219 -196 Table 5.3 CO adsorption of metal surfaces in literature Metal Surface Calculated CO Adsorption Energy Experimental CO Adsorption Energy kJ/mol kJ/mol Cobalt -1121 -1161 Ruthenium -1561, -1552 -1441, -1752 Iron -1351 Nickel -1231, -1422 1241, -1532 When comparing metal phosphides to their related metals the average adsorption energy of CO on metal phosphides is higher than the adsorption energy of CO on their associated metals. It is important to note that dimetal phosphides seem to be much closer to the pure metals than 1 to 1 58 metal phosphides. For example the difference between the average adsorption energy of CO on Co2P is only 3kJ/mol higher than the experimental CO adsorption energy for pure Co (a difference smaller than the difference between experimental and theoretical CO adsorption strength, -112kJ/mol and -116kJ/mol respectively). Similarly the average adsorption energy of CO on Fe2P is 7kJ/mol higher than the experimental value for adsorption energy of CO on Fe, however the average adsorption energy of CO on RuP and Ru2P are both much higher (30kJ/mol and 40kJ/mol respectively) than pure Ru, possibly indicating less metallic character in these systems. The calculated values for different surfaces were found to have large ranges in some systems, for example CoP was shown to have a range of -103kJ/mol, the lowest calculated value, to -226kJ/mol, the highest calculated value, a range of 123kJ/mol between the (111) and (100) surfaces respectively. This has very important implications to the goal of this thesis, if one phosphide sample can have surfaces which span a range that encompasses the weakest to the strongest CO bond then it is difficult to correlate activity to a single or narrow range of CO bond strength values. However it does allow a more complex correlation to be drawn between activity with a range of possible CO adsorption energies on a set of surfaces of a metal phosphide. The goal of which is to create correlations between observed activity and certain ranges of CO adsorption values. Computing the CO adsorption energies for various metal phosphide surfaces is a very important first step to accurately modelling the CO adsorption energies for metal phosphide surfaces and correlating this information to real trends. With this information further research can narrow down which surfaces are the most common on metal phosphide surfaces and which surfaces are 59 the most active. Additionally metal phosphide activity data involving CO can be partially understood by comparing it to the computed CO adsorption energy. Activity data were obtained using GC-MS to analyze the effluent from a reactor with a set up described in Chapter 3.2. The catalyst was then reduced as detailed in Chapter 3.1 in situ and activity measurements were started without exposure to air. The catalyst was exposed to 80cm3(STP)/min of 50:50 CO:H2 syn gas and allowed to stabilize for 7 hours before measurements were obtained. The GC-MS was then used to analyze the effluent gas feed obtaining peak sizes that were then translated into mole percents by dividing by predetermined response factors. These response factors were obtained by injecting a known gas mixture with a known distribution of gases, the peak sizes for each gas were then divided by the known mol percent of the gas. The activity data sets are summarized in Tables 5.4, 5.5 and 5.6. XRDs of the substances are shown in Appendix C in Figures C.13 to C.17 and their crystal sizes are summarized in Table 5.7. 60 Table 5.4 Results of synthesis gas conversion on Co2P Time – Temp XCO C atom selectivity (h) – (K) Carbon % Carbon % CO2 CH4 C2H6 C3H8 CH3OH 7 – 598 3 8 88 2 0 3 31 – 573 18 90 10 0 0 0 36 – 548 13 100 0 0 0 0 47 – 523 9 100 0 0 0 0 55 - 598 25 73 26 1 1 0 Summarized information from Table A.1 in appendix A, no C2+ oxygenates were observed 61 Table 5.5 Results of synthesis gas conversion on CoP Time – Temp XCO C atom selectivity (h) – (K) Carbon % Carbon % CO2 CH4 C2H6 C3H8 CH3OH 7 – 598 18 74 20 1 1 3 13 – 573 13 89 8 0 0 2 26 – 548 5 98 0 0 0 2 31 – 523 6 99 0 0 0 1 37 - 598 9 65 24 2 1 5 Summarized information from Table A.2 in appendix A, no C2+ oxygenates were observed Table 5.6 Results of synthesis gas conversion on Fe2P Time – Temp XCO C atom selectivity (h) – (K) Carbon % Carbon % HC CH4 C2H6 CH3OH C2+oxy 7 - 548 41 78 53 9 5 8 24 - 573 67 76 58 7 2 3 29 – 598 ~100 68 44 10 3 4 47 - 523 16 82 56 8 8 13 Summarized information from Table A.3 in appendix A 62 Table 5.7 Surface area and crystal size summary Metal Phosphide BET Surface Area (m2/g) XRD Cystal Size (nm) 18wt% Co2P/SiO2 282 29.9 18wt% CoP/SiO2 294 31.1 15wt% Fe2P/SiO2 216 29.8 15wt% Ni2P/SiO2 186 22.4 Conversion and all selectivities are calculated using C atom %, XCO is the conversion with respect to CO, C2+oxy is the selectivity towards C2+ oxygenates. Selectivity percents are taken from an average of several injections to a GC-MS set up that uses the area of each peak and the area of pre-injected calibration data. It is important to note that the 29h injection had a conversion that was over 100% and so was reported as ~100%. The reason for this erroneous measurement could be due to catalyst stability issues The results in Table 5.4 show an activation over time. The first data point at 598K is out of place with the trend that the next 3 data points show. This prompted the taking of a 5th data point to review the 598K measurement which gave a result that is more inline with the 2nd, 3rd and 4th data points. This indicates that the initial 598K data point was while the catalyst had not yet reached its full activity and the trend of temperature vs. activity holds when the 5th, 2nd, 3rd and 4th points are compared. A possible explanation is that the environment created by the syn gas (CO and H2) is a stronger reducing environment than the original reduction (H2 and He) meaning the first data point might indicate a catalyst that has not been fully reduced at that time. 63 When comparing Table 5.4 to Table 5.5 similar products which can be expected since the CoP and Co2P syntheses are differentiated based on their stoichiometry meaning there is some CoP in the Co2P sample and vice versa. It is however important to note that unlike Co2P which had a certain ramp up time which made the first data point lower CoP’s first data point fits in the context of the other datapoints. Furthermore CoP also shows some deactivation over the 37 hour run where Co2P did not. CoP also showed a much stronger affinity for methanol production and appears to be a better candidate for further experiments utilizing K promotion to make C2+ oxygenates than Co2P. Furthermore CoP and Co2P have very different CO adsorption energies, where CoP has a broader range and higher average, -103kJ/mol to -227kJ/mol and an average of -159kJ/mol, Co2P has a narrower range and a lower average, -105kJ/mol to -139kJ/mol and an average of -115kJ/mol. This makes sense of their similar products (CO2, hydrocarbons and methanol) but dissimilar selectivities (CoP is more selective towards methanol and hydrocarbons where Co2P is more selective towards CO2, see Tables 5.4 and 5.5 for details). Fe2P’s results shown in Table 5.6 show the most promising results for K promotion studies in the future because of its methanol and C2+ oxygenate yield. Unlike CoP and Co2P the methanol (and C2+ oxygenates) have an inverse correlation to temperature. At low temperatures the methanol and C2+ oxygenates (at 523K they are 8% and 13% respectively) are higher than at higher temperatures (at 598K they are 3% and 4% respectively) indicating less of a kinetic influence (more of a thermodynamic influence) on the distribution of products or less dehydration of oxygenates after formation. When comparing Table 5.2 to Tables 5.4, 5.5 and 5.6 several trends can be elucidated. It is important to note that a 15wt% RuP supported on silica was also tested under the same 64 conditions as the catalysts above and showed no activity. The significance of the lack of activity for RuP could indicate that its high CO adsorption energy reflects CO being too strongly adsorbed to allow significant reaction (-158kJ/mol to -203kJ/mol). When comparing Fe2P to CoP/Co2P, Fe2P at all temperatures is the most active catalyst and produces a significant amount of C2+ oxygenates whereas CoP and Co2P do not. Furthermore it is the only catalyst (in this study) that produces more methanol at lower temperatures than at higher temperatures. It would make sense that for the range of CO adsorption energies, Fe2P is the closest to an ―ideal‖ adsorption energy for the catalysts examined (CO adsorption energies for Fe2P range between -123kJ/mol and -160kJ/mol). Comparing Co2P and CoP it is clear that at all temperatures CoP has a higher methanol selectivity (reaching 5% selectivity at its last 598K data point). This could be used to indicate that the smaller and lower CO adsorption energy range of Co2P (-105kJ/mol to -139kJ/mol) is farther from an ideal CO adsorption energy than Fe2P, however the broad CO adsorption energy of CoP (-103kJ/mol to -227kJ/mol) means that one of its surfaces, likely the CoP (001) and/or CoP (010) surfaces (-169kJ/mol and -135kJ/mol) are the active surfaces as the other surfaces have CO adsorption energies that are either too strong (CoP (100) at -227kJ/mol) or too weak (CoP (111) at -103kJ/mol). When comparing different metal phosphides it is worth looking at Table 5.7 and previously shown Table 4.2 to examine the surface properties of the metal phosphides. The values reported in both tables show that Fe2P, CoP and Co2P have similar surface areas of 216m2/g, 282m2/g and 294m2/g respectively, obtained by BET and metal phosphide particle sizes of 29.8nm, 31.1nm and 29.9nm respectively, obtained using the Scherrer equation and the XRD data shown in 65 Appendix B Figures 1-17. When repeats of Fe2P and CoP were being performed (shown in Table A.4 and A.5 in Appendix A) a significant deactivation was noted from the original Fe2P and CoP experiments. These experiments were performed with catalysts that were not freshly synthesized and were 3 and 6 days old for Fe2P and CoP respectively. To further understand this phenomenon CO chemisorption tests were performed on fresh and 3 day old CoP shown in Table B.3 in appendix B. They showed similar TPR peaks however the fresh CoP displayed CO uptake whereas the 3 day old CoP did not. This indicates a tendency of these metal phosphides to lose a large part or all of their CO adsorption ability when their calcined form is left in air for an extended period of time similar to the results shown for MoP in Chapter 4.1. The effect of calcined metal phosphides losing activity and CO adsorption ability when left in ambient conditions may be caused by moisture in the air. Phosphides like P2O5 are extremely hygroscopic, the phosphide precursor could be taking up water from air which then becomes mobile enough to separate from the associated metal precursor preventing good contact between the metal and phosphide precursors. 10wt% MoP/SiO2 that has been tested in the past and shows a selectivity of 0.5% towards methanol and 15.1% towards C2+ oxygenates under conditions similar to those in this study (548K, 8300kPa, 1:1 CO:H2), and has an ethanol selectivity of 4.4%3. When comparing this to Fe2P which produces more methanol (8% at 523K) and less C2+ oxygenates (13% at 523K) but produces no ethanol, indicates that Fe2P is more selective towards low C alcohols which may be an advantage if it can be tuned slightly to allow for slightly more C-C formation. 66 Tests on Fe-Co metal catalysts have shown production of light olefins and hydrocarbons but with no oxygenates or alcohols101. 4.2wt% Co/SiO2 has shown selectivity of 5.1% towards methanol and 15.8% towards C2+ oxygenates (493K, 2100kPa, 3:6:1 CO:H2:Ar)102. When comparing Co metal to Co2P or CoP, the phosphides show similar selectivity towards methanol (3% for Co2P and 5% for CoP at 598K) however the phosphides do not show C2+ oxygenate production. This would indicate that further refinement of the Co2P and CoP catalysts may focus on retaining more metallic properties. Table 5.8 Summary of Computed CO Adsorption Energies, Activity and Selectivity to CH3OH and C2+ oxygenates Compound Range of CO Adsorption Energies kJ/mol Highest Activity (Temperature) Carbon % (K) Highest Selectivity to CH3OH (Temperature) Carbon % (K) Highest Selectivity to C2+ Oxygenates (Temperature) Carbon % (K) CoP -103  -227 25 (598) 3 (598) 0 Co2P -105  -139 18 (598) 5 (598) 0 Fe2P -123  -160 ~100 (598) 8 (523) 13(523) RuP -158  -203 Ru2P -164  -219 An activity study was performed on RuP however it showed no reaction 67 5.2 Conclusions Several catalysts were studied in this section by molecular modelling and activity studies. The highest selectivies towards methanol for each catalyst were 3% for Co2P at 598K, 5% for CoP at 598K and 8% for Fe2P at 523K. Only Fe2P created significant amounts of C2+ oxygenates with a selectivity of 13% towards C2+ oxygenates at 523K. Molecular modelling results show that the CO adsorption calculated is stronger than CO adsorption measured for the associated metals (exceptions to this are CoP (111), Co2P (100), Co2P (010) and Co2P (001) which had CO adsorption energies of -103kJ/mol, -105kJ/mol, -105kJ/mol and -112kJ/mol). The most promising metal phosphide for future study is Fe2P because of its catalysis of significant amounts of C2+ oxygenates. The range of CO adsorption values were found to be between -123 and -160kJ/mol for Fe2P giving a strong first step to narrowing down a single or range of ideal CO adsorption energies for syn gas to alcohol synthesis catalysts. 68 6 Conclusion 6.1 Conclusions Over the course of this thesis several different goals have been accomplished in furthering the knowledge of molecular modelling, activity analysis and passivation of several metal phosphide catalysts. This knowledge was accumulated with the goal of helping to produce better ethanol catalysts to better satisfy existing and future demand for ethanol in a variety of applications. Molecular modelling performed on a wide array of catalysts helped to elucidate the CO adsorption strength on the catalysts of interest. From the data collected the best oxygenate producing catalyst was Fe2P which had a CO adsorption range of -123kJ/mol to -160kJ/mol giving a good range to examine for future work. With this knowledge a theoretical frame work can be established of what the environment, when talking about CO, is like on different metal phosphides and different surfaces on a single metal phosphide. This information is valuable in trying to understand not only reaction mechanisms but identifying which surfaces may be more active and why. Activity analysis performed on Co2P, CoP, RuP and Fe2P has helped to understand several catalysts for which activities under these conditions with syn gas were not previously examined. The importance of these results helps to narrow down potential options for future study and refinement. This is particularly true for Fe2P which is the strongest oxygenate producing catalyst that was examined and would be a prime candidate for K promotion to examine its potential. Passivation was analyzed to begin a longer journey of trying to understand what passivation does 69 to catalysts in general. It may be expected when planning to perform or performing a passivation on a catalyst that passivation simply provides a thin protective and unobtrusive oxide layer. However results presented in this thesis display that this is demonstrably not the case and passivation is a much more complex process that may need to be examined and tuned for every catalyst. This thesis has answered several important questions on molecular modelling, activity and passivation of metal phosphides however it has also prompted areas of future inquiry. The potential for metal phosphides passivation and activity to be further examined and refined is important. The process by which metal phosphides are refined has also been examined through molecular modelling which can help to point future research towards a new metric for predicting which catalyst is more efficient for a predetermined purpose and most importantly why it is more effective. 70 6.2 Future Work The suggested future work on this project is both in Fe2P investigation and passivation studies. The study of Fe2P is more specific in possible future research however passivation studies are something that could possibly benefit all studies of species that are passivated. Out of the possible candidates for future research from the metal phosphides that have been tested, Fe2P is the most promising because of its high oxygenate selectivity during syn gas conversion. There are several things that can be tuned to try to make the best possible ethanol catalyst. The first that should be performed is a K promotion study following the same guidelines as the MoP K promotion study that partially motivated this research3. If this proves successful in catalyzing the formation of ethanol, optimization could also be performed on the support pore size and catalyst particle size to try to optimize the catalyst. This thesis has shown that passivation can cause vastly different results in catalysts that are otherwise similar (Ni2P and MoP are both metal phosphides). 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(K) Time (hr) Mole Percent Total HC (C atom %) CO CH4 CO2 C2H6 H2O C3H6 C3H8 CH3OH 325 3 36.22 4.66 9.11 0.00 25.92 0.00 0.00 0.00 4.66 325 3.5 38.74 2.83 8.44 0.00 14.38 0.00 0.00 0.00 2.83 325 5.75 37.70 2.17 8.91 0.13 8.22 0.04 0.02 0.38 2.59 325 6.75 38.54 2.12 8.14 0.14 7.49 0.04 0.03 0.31 2.62 325 7 40.24 1.85 6.30 0.13 7.07 0.03 0.03 0.25 2.30 325 7.5 41.20 1.48 6.56 0.11 6.75 0.03 0.02 0.20 1.84 325 7.75 41.57 1.73 5.82 0.10 5.68 0.03 0.02 0.23 2.08 300 12.5 43.13 0.50 5.97 0.00 3.84 0.02 0.01 0.15 0.57 300 12.75 44.22 0.59 4.97 0.00 3.09 0.01 0.00 0.13 0.63 300 13 42.84 0.45 6.30 0.00 2.74 0.01 0.01 0.11 0.52 275 25.5 48.37 0.00 1.58 0.01 0.55 0.00 0.00 0.02 0.03 275 26 47.26 0.00 2.67 0.01 1.05 0.00 0.00 0.05 0.01 275 26.25 47.39 0.00 2.53 0.01 0.78 0.00 0.00 0.06 0.02 275 26.75 46.98 0.00 2.95 0.01 0.73 0.00 0.00 0.05 0.02 250 31 46.30 0.00 3.66 0.01 0.87 0.00 0.00 0.03 0.01 250 31.25 47.51 0.00 2.46 0.01 0.53 0.00 0.00 0.02 0.01 250 31.75 47.32 0.00 2.63 0.00 0.78 0.00 0.00 0.04 0.01 325 36.5 44.52 1.70 2.94 0.10 4.72 0.04 0.02 0.19 2.07 325 37 46.12 0.81 2.24 0.08 4.17 0.03 0.01 0.21 1.09 325 37.5 45.84 0.54 2.66 0.08 3.70 0.03 0.01 0.21 0.81 Total HC (C atom %) = 1 x CH4+2 x C2H6+3 x (C3H6 +C3H8) 85 Table A.2 Mole percent over time for 15wt% fresh Co2P/SiO2 catalyst Reaction conditions: Pressure – 7.58 MPa, H2:CO – 1.1 and GHSV - 3494h-1, Catalyst wt. 0.5g Temp. (K) Time (hr) Mole Percent Total HC (C atom %) CO CH4 CO2 C2H6 C3H8 CH3OH 325 7 48.84 0.99 0.06 0.03 0.00 0.05 1.06 325 7.26 48.75 1.03 0.10 0.04 0.00 0.04 1.11 325 7.49 48.79 1.09 0.12 0.00 0.00 0.00 1.09 300 30.53 40.22 0.95 8.83 0.00 0.00 0.00 0.95 300 31.49 40.92 0.94 8.14 0.00 0.00 0.00 0.94 300 32.1 41.25 0.95 7.80 0.00 0.00 0.00 0.95 275 36.24 43.46 0.00 6.54 0.00 0.00 0.00 0.00 275 37.08 43.13 0.00 6.87 0.00 0.00 0.00 0.00 250 46.47 45.32 0.00 4.68 0.00 0.00 0.00 0.00 325 54.51 36.75 3.40 9.28 0.09 0.12 0.03 3.94 325 55.16 38.26 2.98 8.46 0.14 0.00 0.03 3.25 Total HC (C atom %) = 1 x CH4+2 x C2H6+3 x (C3H6 +C3H8) 86 Table A.3 Mole percent over time for 15wt% fresh Fe2P/SiO2 catalyst Reaction conditions: Pressure – 7.58 MPa, H2:CO – 1.1 and GHSV - 3494h-1, Catalyst wt. 0.5g Temp. (K) Time (hr) Mole Percent Total HC (C atom %) CO CH4 CO2 C2H4 C2H6 C3H6 C3H8 CH3OH 275 7.00 45.29 1.94 0.62 0.06 0.34 0.24 0.13 0.26 3.82 275 7.28 43.50 2.56 0.80 0.07 0.45 0.32 0.16 0.26 5.44 275 8.17 38.30 3.84 1.12 0.13 0.61 0.44 0.22 0.30 9.20 275 9.06 39.08 3.42 0.97 0.12 0.53 0.39 0.20 0.27 8.18 275 9.46 40.29 3.51 1.09 0.12 0.60 0.44 0.21 0.21 8.01 300 24.21 14.65 15.92 5.38 0.20 1.89 0.69 0.62 0.57 27.16 300 24.48 36.48 13.52 0.00 0.00 0.00 0.00 0.00 0.00 13.52 300 25.00 18.90 14.19 4.95 0.18 1.72 0.63 0.57 0.48 23.77 300 25.25 19.99 11.75 4.42 1.47 1.43 0.55 0.50 0.43 23.42 325 29.39 -15.87 27.14 11.54 0.40 3.75 1.32 1.29 1.41 48.64 325 30.03 16.71 2.43 10.41 0.37 3.56 1.27 1.25 0.46 21.22 325 30.23 -3.62 21.84 9.40 0.34 3.17 1.13 1.12 0.75 40.47 325 30.47 -1.28 19.79 9.24 0.32 2.87 1.04 1.05 0.94 37.49 250 47.45 42.26 2.01 0.34 0.12 0.28 0.23 0.10 0.45 4.81 250 48.13 45.97 1.71 0.30 0.09 0.24 0.21 0.10 0.43 3.30 250 48.33 47.42 1.67 0.30 0.09 0.22 0.00 0.00 0.00 2.29 250 49.00 43.70 1.73 0.31 0.10 0.23 0.22 0.22 0.27 4.43 Total HC (C atom %) = 1 x CH4+2 x (C2H4+C2H6)+3 x (C3H6 +C3H8)+4 x (2-Butene+Butane+ 2-Methyl Butane)+6 x HexaneTable 87 A.3 CONTINUED Mole percent over time for fresh 15wt% Fe2P/SiO2 catalyst Reaction conditions: Pressure – 7.58 MPa, H2:CO – 1.1 and GHSV - 3494h-1, Catalyst wt. 0.5g Temp. (K) Time (hr) Mole Percent Total Oxy (C atom %) 2-Butene Butane 1-Butanol 3-Methyl 1-Butanol 2-Methyl Butane 1-Pentanol Hexane 275 7.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.26 275 7.28 0.10 0.00 0.12 0.00 0.00 0.00 0.00 0.73 275 8.17 0.14 0.13 0.18 0.07 0.11 0.04 0.06 1.47 275 9.06 0.11 0.13 0.10 0.08 0.10 0.05 0.06 1.26 275 9.46 0.14 0.13 0.28 0.00 0.00 0.00 0.00 1.34 300 24.21 0.19 0.28 0.00 0.08 0.16 0.11 0.10 1.44 300 24.48 0.00 0.00 0.24 0.00 0.00 0.00 0.00 0.94 300 25.00 0.15 0.26 0.22 0.08 0.14 0.09 0.00 2.11 300 25.25 0.14 0.22 0.62 0.06 0.12 0.09 0.12 3.59 325 29.39 0.32 0.53 0.30 0.12 0.28 0.18 0.13 4.00 325 30.03 0.32 0.52 0.38 0.00 0.00 0.00 0.00 1.97 325 30.23 0.29 0.46 0.49 0.13 0.27 0.12 0.13 3.80 325 30.47 0.29 0.44 0.18 0.11 0.27 0.17 0.18 2.93 250 47.45 0.09 0.06 0.00 0.11 0.10 0.16 0.00 1.67 250 48.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.43 250 48.33 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.42 250 49.00 0.07 0.06 0.11 0.05 0.05 0.00 0.98 Total Oxy (C atom %) = 1 x CH3OH+4 x (1-Butanol+3-Methyl 1-Butanol)+5 x 1-Pentanol 88 Table A.4 Mole percent over time for 3 day old 15wt% Fe2P/SiO2 catalyst Reaction conditions: Pressure – 7.58 MPa, H2:CO – 1.1 and GHSV - 3494h-1, Catalyst wt. 0.5g Temp. (K) Time (hr) Mole Percent Total HC (C atom %) CO CH4 CO2 C2H4 C2H6 C3H6 C3H8 CH3OH 325 20 47.81 0.37 0.24 0.01 0.04 0.08 0.03 0.06 1.62 325 20.25 48.16 0.25 0.16 0.01 0.03 0.06 0.02 0.05 1.37 325 20.75 48.33 0.23 0.15 0.01 0.03 0.05 0.01 0.05 1.22 300 43.5 48.84 0.07 0.10 0.01 0.02 0.03 0.01 0.02 0.47 300 44 49.58 0.03 0.06 0.00 0.01 0.02 0.01 0.02 0.28 300 44.25 49.61 0.04 0.06 0.01 0.01 0.02 0.01 0.02 0.24 275 49.25 49.65 0.00 0.05 0.00 0.00 0.01 0.01 0.02 0.20 275 49.75 49.80 0.00 0.03 0.00 0.00 0.00 0.00 0.02 0.11 275 50 49.78 0.00 0.03 0.01 0.00 0.01 0.00 0.01 0.09 250 53.5 49.87 0.00 0.03 0.00 0.00 0.00 0.00 0.02 0.07 250 54 49.93 0.00 0.03 0.00 0.00 0.00 0.00 0.01 0.02 250 54.25 49.86 0.00 0.02 0.00 0.00 0.00 0.00 0.01 0.08 325 67.75 48.62 0.34 0.13 0.01 0.04 0.03 0.01 0.04 1.04 325 68 49.09 0.26 0.10 0.01 0.03 0.03 0.01 0.03 0.65 325 68.58 48.86 0.23 0.08 0.01 0.02 0.02 0.01 0.03 0.88 Total HC (C atom %) = 1 x CH4+2 x (C2H4+C2H6)+3 x (C3H6 +C3H8)+4 x (2-Butene+Butane+ 2-Methyl Butane)+6 x Hexane 89 Table A.4 CONTINUED Mole percent over time for 3 day old 15wt% Fe2P/SiO2 catalyst Reaction conditions: Pressure – 7.58 MPa, H2:CO – 1.1 and GHSV - 3494h-1, Catalyst wt. 0.5g Temp. (K) Time (hr) Mole Percent Total Oxy (C atom %) 2-Butene Butane 1-Butanol 3-Methyl 1-Butanol 2-Methyl Butane 1-Pentanol Hexane 325 20 0.03 0.05 0.02 0.02 0.03 0.02 0.04 0.29 325 20.25 0.04 0.04 0.02 0.01 0.03 0.02 0.03 0.28 325 20.75 0.04 0.05 0.02 0.01 0.03 0.02 0.02 0.25 300 43.5 0.00 0.02 0.00 0.00 0.04 0.11 0.00 0.55 300 44 0.00 0.01 0.00 0.00 0.02 0.01 0.00 0.07 300 44.25 0.00 0.01 0.00 0.00 0.02 0.01 0.00 0.07 275 49.25 0.01 0.00 0.00 0.00 0.02 0.01 0.00 0.08 275 49.75 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.03 275 50 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.08 250 53.5 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.02 250 54 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.01 250 54.25 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.01 325 67.75 0.02 0.02 0.01 0.01 0.02 0.01 0.02 0.17 325 68 0.00 0.02 0.01 0.00 0.01 0.01 0.00 0.11 325 68.58 0.02 0.02 0.01 0.00 0.03 0.01 0.03 0.15 Total Oxy (C atom %) = 1 x CH3OH+4 x (1-Butanol+3-Methyl 1-Butanol)+5 x 1-Pentanol 90 Table A.5 Mole percent over time for 6 day old 15wt% CoP/SiO2 catalyst Reaction conditions: Pressure – 7.58 MPa, H2:CO – 1.1 and GHSV - 3494h-1, Catalyst wt. 0.5g Temp. (K) Time (hr) Mole Percent Total HC (C atom %) CO CH4 CO2 C2H6 H2O C3H8 CH3OH 325 7 49.82 0.05 0.06 0.01 0.22 0.00 0.03 0.07 325 7.5 49.86 0.04 0.05 0.00 0.21 0.00 0.02 0.06 325 7.75 49.86 0.03 0.04 0.01 0.22 0.00 0.02 0.05 300 13.25 49.81 0.04 0.06 0.01 0.41 0.00 0.05 0.06 300 13.5 49.95 0.01 0.02 0.00 0.11 0.00 0.01 0.02 300 14 49.94 0.00 0.02 0.00 0.16 0.00 0.01 0.01 275 32.5 49.95 0.00 0.02 0.00 0.14 0.00 0.01 0.01 275 33 49.97 0.00 0.01 0.00 0.08 0.00 0.01 0.01 275 33.25 49.98 0.00 0.01 0.00 0.07 0.00 0.00 0.01 250 37 49.97 0.00 0.01 0.00 0.04 0.00 0.01 0.01 250 37.25 49.97 0.00 0.01 0.00 0.05 0.00 0.01 0.01 250 37.75 49.97 0.00 0.01 0.00 0.05 0.00 0.01 0.01 325 52.25 49.84 0.04 0.05 0.01 0.24 0.00 0.03 0.07 325 52.75 49.87 0.03 0.04 0.01 0.25 0.01 0.02 0.06 325 54.25 49.90 0.02 0.03 0.01 0.21 0.00 0.02 0.05 Total HC (C atom %) = 1 x CH4+2 x C2H6+3 x (C3H6 +C3H8) 91 Table A.6 Mole percent over time for 12 hour old 15wt% CoP/SiO2 catalyst Reaction conditions: Pressure – 7.58 MPa, H2:CO – 1.1 and GHSV - 3494h-1, Catalyst wt. 0.5g Temp. (K) Time (hr) Mole Percent Total HC (C atom %) CO CH4 CO2 C2H6 H2O C3H8 CH3OH 325 11.5 46.25 0.88 1.13 0.78 4.73 0.05 0.04 2.58 325 11.75 48.14 0.89 0.44 0.14 1.14 0.05 0.10 1.31 325 12 48.46 0.86 0.35 0.08 0.60 0.03 0.09 1.10 300 29 49.25 0.40 0.19 0.04 0.28 0.01 0.05 0.50 300 29.5 49.60 0.18 0.10 0.03 0.24 0.01 0.06 0.25 300 29.75 49.64 0.15 0.10 0.03 0.29 0.01 0.04 0.22 275 33 49.76 0.09 0.09 0.01 0.20 0.00 0.03 0.12 275 33.25 49.87 0.04 0.04 0.01 0.16 0.00 0.02 0.06 275 33.5 49.87 0.04 0.04 0.01 0.09 0.00 0.02 0.07 250 49.5 49.92 0.01 0.04 0.01 0.13 0.00 0.01 0.02 250 49.75 49.96 0.00 0.02 0.00 0.11 0.00 0.01 0.01 250 50 49.96 0.01 0.02 0.00 0.17 0.00 0.01 0.01 325 53.5 49.50 0.21 0.14 0.04 0.17 0.01 0.02 0.33 325 53.75 49.62 0.14 0.10 0.04 0.07 0.01 0.03 0.25 325 54 49.66 0.12 0.08 0.03 0.08 0.01 0.03 0.22 Total HC (C atom %) = 1 x CH4+2 x C2H6+3 x (C3H6 +C3H8) 92 Table A.7 Calibration relevant to fresh 15wt% Co2P/SiO2 and fresh 15wt% Fe2P/SiO2 Three injections of calibration gas without an ethanol bubbler were analyzed and then the calibration gas was sent through a single ethanol bubbler which is not assumed to saturate the gas flow Component (mol% in calibration gas) CO (25mol%) CH4 (10mol%) CO2 (8mol%) C2H4 (2mol%) C2H6 (8mol%) C3H8 (6mol%) Isobutane (2mol%) Butane (4mol%) C2H5OH (0mol%) Peak Size (a.u.) 126218024 111829104 144719408 109896944 276935104 633794496 374842272 596265344 0 127409232 69075280 76769776 55946744 215173488 352665152 205856016 372618560 0 118711360 66116824 72179608 49742988 200233920 326827232 196355680 346877152 0 101561336 56784860 97954224 44022808 181769136 300023872 155821808 271394048 114719392 94697544 59538172 73164880 43303764 178764528 275861632 154534992 280048096 114180672 80635264 61313484 82279248 43764612 181910160 271191680 109926504 170000464 100557304 Mol%/average peak area 3.21E-7 6.92E-7 1.28E-7 1.47E-7 1.02E-7 5.24E-8 7.72E-9 3.46E-8 7.88E-8 Calibration gas also contained 25mol% hydrogen and 10mol% nitrogen which did not show up to GC-MS analysis. The first three injections of only calibration gas of known mix were used to calculate all the components in the last three injections (H2 and N2 were calculated by assuming their concentra-tions dropped by the same ratio as the other components) of calibration gas/ethanol and then by difference calculate the mol% for ethanol. With the peak areas and mol%s known a ratio is creat-ed of mol% to peak area so that raw peak areas from activity measurements can be multiplied by the ratio to obtain a mol%. For species that aren’t present in the calibration gas the closest ana- 93 logue is used, for example butane’s calibration value is used as a substitute for pentane or hex-ane. Table A.8 Calibration relevant to 15wt% fresh CoP/SiO2 catalyst, 15wt% 3 day old Fe2P/SiO2 catalyst and 15wt% 6 day old CoP/SiO2 catalyst, six injections of calibration gas Component (mol% in calibration gas) CO (25mol%) CH4 (10mol%) CO2 (8mol%) C2H4 (2mol%) C2H6 (8mol%) C3H8 (6mol%) Isobutane (2mol%) Butane (4mol%) Peak Size (a.u.) 189928864 55121768 55572468 17393822 113929936 109780376 34587272 74850912 148499216 67004240 64721044 16347393 141922288 188198848 56051732 133029312 121637464 62337932 62127360 14571437 134485824 169122400 55548880 135130704 109023088 61754400 62992036 13547137 136192704 178335456 56434768 139361056 103000840 56182608 57368592 11707803 126441712 162038464 51715608 125397416 117883216 71657840 74504848 18370298 166715536 183517696 59584152 142027216 Mol%/average peak area 1.58E-7 1.34E-7 1.06E-7 1.09E-7 4.88E-8 3.03E-8 6.37E-8 1.33E-8 Calibration gas also contained 25mol% hydrogen and 10mol% nitrogen which did not show up to GC-MS analysis. 94 Table A.9 Calibration relevant to 15wt% 12hour old CoP/SiO2 catalyst Three injections of calibration gas Component (mol% in calibration gas) CO (25mol%) CH4 (10mol%) CO2 (8mol%) C2H4 (2mol%) C2H6 (8mol%) C3H8 (6mol%) Isobutane (2mol%) Butane (4mol%) Peak Size (a.u.) 131326848 53415524 61002536 17640398 106911888 111257360 40028624 94377864 112766056 58419432 61829676 17831344 128545936 170558000 69076848 155446592 91807672 48335608 61430320 14254542 111070240 133143568 56813696 138132688 Mol%/average peak area 2.23E-7 1.87E-7 1.30E-7 1.21E-7 6.93E-8 4.34E-8 7.23E-8 1.55E-8 Calibration gas also contained 25mol% hydrogen and 10mol% nitrogen which did not show up to GC-MS analysis. 95 Appendix A Reactor Sample Calculations To better elucidate how my results were obtained I will show how a methane peak area was transformed into a mol%, to a selectivity and how the overall conversion was calculated from the mol%. The example will use the injection performed at time 7h in the fresh 15wt% CoP/SiO2 run. Taking the raw peak area of 13866982 I multiplied it by the mol% to peak area ratio from the calibration run which is 1.34E-7 for methane. 13866982x1.34E-7=1.85 So we have the mol% of 1.85% To get the selectivity we then divide this mol% by the total of the mol%s of all the carbon spe-cies produced in this injection (including methane but not including water) which is 8.44% 1.85/8.44=17.55 So we have the selectivity of 17.55% To find the conversion we take the sum total of the carbon containing species where each species is multiplied by how much carbon it contains. In the case of CoP at 7h the math is shown below. 1 x (CH4+CH3OH+CO2)+2 x C2H6+3 x (C3H6 +C3H8)+4 x (unidentified peak assumed C4) 1 x (1.85+0.25+6.30)+2 x 0.13+3 x (0.03 +0.03)+4 x 0.23=9.76 96 We know this carbon had to come from the only carbon source CO in the feed, we know the in flow percent of CO is 50%. 50-9.76=40.24 We know the out flow of CO is 40.24 mol% Next we can get the conversion by taking 1 minus the outflow of CO divided by the inflow of CO Conversion=1-(outflow/inflow) In this case it can be simplified to Conversion=(inflow - outflow)/inflow Conversion=(0.5- outflow)/ 0.5 Conversion=2x(0.5- outflow) Using the CO outflow previously calculated Conversion=2x(0.5- 0.4024)=0.1953 So the conversion for this specific run is 19.53% 97 Appendix B: TPR and CO Chemisorption Data Table B.1 15% Ni2P/SiO2 TPR and CO Chemisorption Data Sample # Sample CO Chemisorption (µmol/g) TPR Uptake (µmol/g) 2 Passivated 15% Ni2P/SiO2 11.74 1116.90 3 Passivated 15% Ni2P/SiO2 0 1534.10 5 Passivated 15% Ni2P/SiO2 0 1018.62 7 Passivated 15% Ni2P/SiO2 3.72 1963.32 1 Airless 15% Ni2P/SiO2 0 1126.95 2 Airless 15% Ni2P/SiO2 0 410.86 3 Airless 15% Ni2P/SiO2 0 773.54 4 Airless 15% Ni2P/SiO2 9.05 1019.59 1 Unpassivated 15% Ni2P/SiO2 43.31 1093.64 2 Unpassivated 15% Ni2P/SiO2 5.49 1745.80 3 Unpassivated 15% Ni2P/SiO2 32.05 1270.15 4 Unpassivated 15% Ni2P/SiO2 25.16 1595.81 98 Table B.2 15% MoP/SiO2 TPR and CO Chemisorption Data Sample # Sample CO Chemisorption (µmol/g) TPR Uptake (µmol/g) 1 Passivated 15% MoP/SiO2 100.71 3122.39 2 Passivated 15% MoP/SiO2 33.87 2997.25 3 Passivated 15% MoP/SiO2 70.14 3295.63 4 Passivated 15% MoP/SiO2 63.31 4498.10 1 Airless 15% MoP/SiO2 69.71 1368.55 2 Airless 15% MoP/SiO2 98.48 1341.91 3 Airless 15% MoP/SiO2 155.79 1538.60 4 Airless 15% MoP/SiO2 109.15 1439.53 1 Unpassivated 15% MoP/SiO2 90.06 2492.88 2 Unpassivated 15% MoP/SiO2 129.35 2677.16 3 Unpassivated 15% MoP/SiO2 36.76 3052.47 8 Unpassivated 15% MoP/SiO2 48.19 3542.30 99 Table B.3 Calcined Metal Phosphide TPR and CO Chemisorption Data Sample CO Chemisorption (µmol/g) TPR Uptake (µmol/g) 15% Ni2P/SiO2 Calcined run 1 of 2 fresh (synthesized the day of use) 8.17 2197 15% Ni2P/SiO2 Calcined run 2 of 2 fresh (synthesized the day of use) 0.59 518 15% CoP/SiO2 Calcined fresh (synthesized the day of use) 6.62 1363 15% CoP/SiO2 Calcined older (3 days after synthesis) 0.79 904 The Ni2P run was performed with freshly calcined Ni2P that immediately had a TPR/CO chemisorption performed on it, without removal from the airless environment a second TPR/CO chemisorption was performed. 100 Figure B.1 TPR of airless Ni2P0 50 100 150 200 250 300 3504.6504.6554.6604.6654.6704.6754.6804.6854.690Time (min)TCD Signal (a. u.)400500600700800900T (K) 101 Figure B.2 TPR of unpassivated Ni2P0 50 100 150 200 250 300 3504.6604.6654.6704.6754.6804.685Time (min)TCD Signal (a. u.)400500600700800900T (K) 102 Figure B.3 TPR of passivated Ni2P0 50 100 150 200 250 300 3504.6604.6654.6704.6754.6804.685Time (min)TCD Signal (a. u.)400500600700800900T (K) 103 Figure B.4 TPR of airless MoP0 50 100 150 200 250 300 3504.6504.6554.6604.6654.6704.6754.6804.685Time (min)TCD Signal (a. u.)400500600700800900T (K) 104 Figure B.5 TPR of unpassivated MoP0 50 100 150 200 250 300 3504.624.634.644.654.664.674.684.69Time (min)TCD Signal (a. u.)400500600700800900T (K) 105 Figure B.6 TPR of passivated MoP0 50 100 150 200 250 300 3504.564.584.604.624.644.664.684.70Time (min)TCD Signal (a. u.)400500600700800900T (K) 106 Appendix C: XPS and XRD Data Figure C.1 XPS of airless 15wt% MoP/SiO2 C content 294 292 290 288 286 284 282 280 278 27612000140001600018000200002200024000Intensity (a.u.)B.E. (eV) (C 1s peak at 285eV) 107 Figure C.2 XPS of airless 15wt% MoP/SiO2 Mo content 245 240 235 230 225 22010000120001400016000180002000022000Intensity (a.u.)B.E. (eV) (Mo 3d at 234eV) 108 Figure C.3 XPS of airless 15wt% MoP/SiO2 P content 140 138 136 134 132 130 128 126 124 122750080008500900095001000010500Intensity (a.u.)B.E. (eV)(P 2p at 134eV) 109 Figure C.4 XPS of airless 15wt% MoP/SiO2 Si content 110 108 106 104 102 100 98 96 940500010000150002000025000300003500040000Intensity (a.u.)B.E. (eV)(Si 2p 103eV) 110 Figure C.5 XPS of passivated 15wt% MoP/SiO2 C content 294 292 290 288 286 284 282 280 278 276120001400016000180002000022000Intensity (a.u.)B.E. (eV)(C 1s 285eV) 111 Figure C.6 XPS of passivated 15wt% MoP/SiO2 Mo content 245 240 235 230 225 220100001500020000Intensity (a.u.)B.E. (eV)(Mo 3d 234eV) 112 Figure C.7 XPS of passivated 15wt% MoP/SiO2 P content 140 138 136 134 132 130 128 126 124 122750080008500900095001000010500Intensity (a.u.)B.E. (eV) (P 2p 134eV) 113 Figure C.8 XPS of passivated 15wt% MoP/SiO2 Si content 112 110 108 106 104 102 100 98 960500010000150002000025000300003500040000Intensity (a.u.)B.E. (eV)(Si 2p 103eV) 114 Figure C.9 XPS of unpassivated 15wt% MoP/SiO2 C content 294 292 290 288 286 284 282 280 278 27612000140001600018000200002200024000Intensity (a.u.)B.E. (eV)(C 1s 285eV) 115 Figure C.10 XPS of unpassivated 15wt% MoP/SiO2 Mo content 245 240 235 230 225 220100001500020000Intensity (a.u.)B.E. (eV)(Mo 3d 234eV) 116 Figure C.11 XPS of unpassivated 15wt% MoP/SiO2 P content 140 138 136 134 132 130 128 126 124 122750080008500900095001000010500Intensity (a.u.)B.E. (eV)(P 2p 134eV) 117 Figure C.12 XPS of unpassivated 15wt% MoP/SiO2 Si content 112 110 108 106 104 102 100 98 960500010000150002000025000300003500040000Intensity (a.u.)B.E. (eV)(Si 2p 103eV) 118 XRD Spectra Explanation XRD spectra of MoP are very difficult to obtain at low loadings due to high dispersion as shown in the thesis of Dr. Sharif Zaman Appendix III (Zaman F. Sharif, An Investigation of MoP Cata-lysts for Alcohol Synthesis, University of British Columbia, 2010). Furthermore the first very broad peak in all spectra is an amorphous peak created by the silica support. Figure C.13 XRD of 15wt% MoP/SiO2 0 10 20 30 40 50 60 70 80 9040060080010001200140016001800200022002400Intensity (a.u.)2Theta (degrees)(PDF 00-027-0771, 32.5o-20, 37.5o-60, 50.5o-100, 67.8o-25, 68.4o-9, 77.1o-9, 79.8o-10) 119 Figure C.14 XRD of 15wt% CoP/SiO2 (PDF 00-029-0497, peak-intensity, 27.5o-7, 36.8o-68, 37.3o-20, 41.2o-18, 42.4o-41, 42.9o-20, 45.5o-2, 52.9o-7, 54.2o-51, 56.5o-100, 56.9o-46, 61.5o-19, 66.1o-26, 66.5o-4, 67.0o-38, 73.1o-3, 76.0o-1, 78.4o-3) [X indicates the CoP peak at 36.8o, and O indicates the principle peak of the Co2P spectrum at 47.4o] Co2P and CoP are expected to not be entirely separate because the syntheses are differentiated based only on their stoichiometry. 120 Figure C.15 XRD of 15wt% Fe2P/SiO2 0 10 20 30 40 50 60 70 80 90-2000200400600800100012001400Intensity (a.u.)2Theta (degrees)(PDF 00-027-1171, 20.3o-17, 29.9o-51, 35.5o-100, 36.4o-57, 41.2o-57, 47.1o-11, 51.8o-11, 55.5o-11, 62.3o-11, 63.7o-11, 64.4o-6, 75.1o-6, 78.8o-6) 121 Figure C.16 XRD of 15wt% Co2P/SiO2 (PDF 00-006-0595, 36.6o-2, 38.2o-2, 47.4o-50, 48.8o-3, 50.4o-40, 51.4o-15, 56.8o-15, 58.2o-3, 58.8o-15, 60.6o-3, 61.2o-20, 61.8o-2, 65.7o-2, 66.0o-15, 67.3o-1, 68.2o-1, 69.8o-2, 73.7o-1, 76.9o-1) [X indicates the CoP peak at 36.8o, and O indicates the principle peak of the Co2P spectrum at 47.4o] Co2P and CoP are expected to not be entirely separate because the syntheses are differentiated based only on their stoichiometry. 122 Figure C.17 XRD of 15wt% Ni2P/SiO2 0 10 20 30 40 50 60 70 80 9005001000150020002500Intensity (a.u.)2Theta (degrees)(PDF 00-003-0953, peak-intensity, 47.7o-10, 52.2o-8, 55.5o-8, 63.9o-7, 64.7o-4, 78.7o-4) 123 Table C.1 XRD crystal sizes of metal phosphides calculated from the Scherrer equation Metal Phosphide 2θ (deg) θ (deg) θ (rad) cosθ peak width 2θ (deg) peak width θ (deg) peak width θ (rad) Crystal width L (nm) Co2P 47.75 23.88 0.42 0.91 0.75 0.38 0.01 29.89 CoP 56.75 28.38 0.50 0.88 0.75 0.38 0.01 31.07 Fe2P 47.13 23.56 0.41 0.92 0.75 0.38 0.01 29.82 Ni2P 47.50 23.75 0.41 0.92 1.00 0.50 0.01 22.40 L=Kλ/(βcosθ) Where k=1 and λ=1.78897x10-10m for this X-ray diffraction analysis, β=peak width at half maximum in radians and θ=peak location in radians 124 Appendix D: Reactor Work The reactor is prepared by inserting quartz wool and packing it approximately 10cm above the bottom of the reactor. The catalyst particles are then placed on top of this and another layer of quartz wool is placed. A high temperature thermocouple is then placed in the middle of the cata-lyst bed. The outlet is attached to a temperature controller thus the temperature controller will maintain the reactor bed at the desired value (<±1oC). At the reactor outlet a 0.5µm mesh is placed to prevent any catalyst particles from blocking tubing downstream of the catalyst bed. The catalyst bed is heated from room temperature to 650oC at 1oC/min and kept at 650oC for 2 hours in a flow of 60cm3(STP)/min of H2 and 20cm3(STP)/min of Ar at atmospheric pressure. The temperature then falls to the desired reaction temperature (325oC). A back pressure regulator is downstream of the filter previously mentioned and kept in a box heated to 110oC. The back pressure regulator is slowly tightened until it reaches the desired reac-tion pressure (7.58 MPa). The outlet of the BPR is divided into two streams by a three way valve. One going to a vent to a bubbler in a fume hood. The other to a GC-MS set up for further analysis. These lines to the GC-MS are heated to 200oC to prevent condensation. To stop the reactor the heating switches on the GC-MS line, the temperature controller linked to the thermocouple in the reactor bed and the heated box containing the back pressure regulator are switched off. The effluent flow is then redirected to the vent line, the incoming gas is switched off and the pressure is slowly decreased to atmospheric temperature. 125 The reactor contains two tubes soldered together. A 3/8inch outer diameter stainless steel tube on the outside and a ¼ inch outer diameter copper tube on the inside. The copper tube is saudered inside the stainless steel tube and pressurized to fit the stainless steel tube tightly. The length of the stainless steel and copper tubes is 49cm. The copper lining is used to suppress the formation of hydrocarbons from syngas by reacting with the stainless steel wall. Relevant diagrams of the reactor (note glass beads were not used in this thesis) and the calcula-tion of heat and mass transfer for this system are shown in the thesis of Dr. Sharif Zaman Appen-dix III (Zaman F. Sharif, An Investigation of MoP Catalysts for Alcohol Synthesis, University of British Columbia, 2010). 126 Appendix E: Repeatability analysis of the reactor set up Due to the air sensitivity of the metal phosphide catalysts a 0.5wt% Cs 40wt% Cu-MgO catalyst previously prepared by Shahin Goodarznia was used at 573K and 8966kPa with a flow of H2:CO 1:1 syn gas at a rate of 80 cm3(STP)/min. The experiment was repeated three times and found to be an uncertainty of 7% Table E.1 First run of 0.5wt% Cs 40wt% Cu-MgO Run number Time from start (h) Selectivity (C atom%) CH4 CO2 CH3OH 2 4.25 22.19% 73.76% 74.40% 3 4.5 18.85% 72.66% 77.78% 4 4.75 14.43% 69.63% 83.13% Table E.2 Second run of 0.5wt% Cs 40wt% Cu-MgO Run number Time from start (h) Selectivity (C atom%) CH4 CO2 CH3OH 1 3.25 18.78% 71.81% 76.66% 2 3.75 20.85% 73.59% 74.36% 4 4.25 14.73% 68.04% 80.68% 5 4.5 13.70% 67.41% 82.49% 6 4.75 13.22% 66.49% 82.87% 127 Table E.3 Third run of 0.5wt% Cs 40wt% Cu-MgO Run number Time from start (h) Selectivity (C atom%) CH4 CO2 CH3OH 1 3 16.09% 67.64% 83.91% 2 3.25 14.91% 63.91% 85.09% 3 3.5 14.03% 62.24% 85.97% 4 3.75 15.24% 64.54% 84.76% 5 4 15.07% 64.36% 84.93% 6 4.25 12.51% 59.29% 87.49% Table E.4 Average Selectivities of CH4 CO2 and CH3OH between all runs Average Selectivity (C atom%) ±95% confidence interval CH4 CO2 CH3OH 16.91% 68.89% 83.09% Example calculation for methane First the mol% is obtained from a peak with area of 229634 and a calibration constant of 1.87x10-7 was used 229634x1.87x10-7=0.04mol% Next this mol% is then divided by the sum of the mol%s of all of the other products which is 0.54mol% 0.04mol%/0.54mol%=22.98% 128 The average is then taken of all the selectivities of methane in run 1, this is found to be 19.09%. In run 2 the average is 17.00% and in run 3 the average is 14.64%. Next the average of the averages is found to be 16.91%, we also calculate the standard deviation for this set. 𝑡 = √( − )2 + ( − )2 + ( − )2 𝑡 = 2 The 95% confidence interval is then calculated 𝑜𝑛 𝑖 𝑛 𝑖𝑛𝑡 𝑟 = 2√ 𝑜𝑛 𝑖 𝑛 𝑖𝑛𝑡 𝑟 = 2 The average of the three runs is then divided by this confidence interval to find the % uncertainty Uncertainty%=0.0206/0.1691 Uncertainty%=12.17% CO2 uncertainty(6.26%) and methanol uncertainty(2.48%) are also monitored so the average of these three is taken to yield the uncertainty of 6.97% or 7%.
Thesis/Dissertation
eng
Chemical and Biological Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivs 2.5 Canada
Graduate
Alcohol synthesis using metal phosphides
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