This document outlines research into catalyzing the hydrogenation of CO2 using metal oxides. It discusses the background and challenges, investigating the reaction mechanism and factors that influence catalyst performance. The research aims to understand the process at a microscopic level to optimize catalyst design without trial and error. Methods explored include comparing catalysts in a database, mapping electron density changes during reaction, and applying machine learning to understand structure-activity relationships. Future work involves further computational modeling and analysis to refine rate constants and transition state theory.

1. Computational and literature
investigation to understand the
mechanism behind the
catalyzed hydrogenation of CO2
- Darrell Nelson

2. Outline
I. Background
II. Attacking the Problem
III. Metal Oxides
IV. Mechanism
V. Additional Factors
VI. Future Work

3. Background
 Converting CO2 into useful fuels (i.e. methanol, methane)
 (MTO) Methanol to Olefins (i.e. ethylene, propylene)
 Create CO to be used as syngas
 1$27 billion annual market for ethylene glycol
1. © 2013 Liquid Light Corporation. All rights reserved. | www.llchemical.com

4. 2

5. 35,000 Mt produced per year
27,000 MtCO2
3

6. Energy Crisis
 Over 7 billion people in the world
 Depleting energy sources rapidly
 Economies of both China and India are growing
 Creating scarcity of natural resources

7. 2 problems 1 solution
 Hydrogenation of CO2 instead of sequestration
 Stops emissions and provides very cheap fuel can be used again and again

8. Attacking the problem
 CO2 is a very stable and oxidized form of C
 Linear molecule
 No strain
 Add a high energy electron makes it unstable

9. Catalyst
lifetime
4

10. How to unravel this complex system?
𝐴 + 𝐵 𝐴𝐵
CAT

11. How to make the best catalyst?
 What is the composition/structure of the catalyst
 Bulk structure (lattice)
 Surface composition
 Subsurface composition
 “Active Sites”

12. How to make the best catalyst?
 What is the reaction mechanism?
Surface chemistry is dynamic
Active Sites are changing
5

13. How to make the best catalyst?
4

14. 4

15. Metal Oxides (Chosen catalyst)
 e.g. (Al2O3, SiO2)
 High surface area
 Very stable under usual chemical reaction conditions
 Reduced metal oxides (i.e. CuO, Ce2O) that can change their oxidation states
and have vacancies in their structure upon release/storage of oxygen
 Diffusion from the bulk

16. 4

17. Optimization Problem
Keeping the reactivity high involves not changing the catalyst. But, reactivity
means that the catalyst is unstable and willing to change.
Using an industrial high-throughput approach to find the maximum between
lifetime and reactivity

19. What is the chemistry?
 As you can see from previous slides Surface properties of the catalyst are the most important
(e.g. solid acid/base)
 CO2 is a Lewis acid
 Look at materials that are willing to donate electrons
 Low valent ion oxides are preferred

21. Correlation
0
10
20
30
40
50
60
70
80
0 0.5 1 1.5 2 2.5 3 3.5
%CO2conversion
Activation Energy
Activation vs Conversion

22. Statistical Analysis
 Highest conversion came from Ni and Fe
 46 out of 287 Ni, 34 out of 287 Fe

23. Mechanism (microscopic)
 Understanding mechanism sheds light on the “why and how”
 Create a high performance catalyst based off of its properties and not through
trial and error
 Adsorption creates carbonate group on M.O. (not MgO)
 Why not MgO? What’s special/different?
 Makes sense to use compounds that have coordinated oxygens because of their high
electronegativity
 The electronegativity of oxygen activates the binding sites

24. 5

25. Note on transition states
3H2 + CO2 CH3OH + H2O
 6Some elements help facilitate other parts of the reaction (Pt – good at
disassocitating H2 , Zn – good at binding, Cu – catalyzes transition states)
6. Ref #2 of J. Graciani, K. Mudiyanselage, F. Xu, a. E. Baber, J. Evans, S. D. Senanayake, D. J. Stacchiola, P. Liu,
J. Hrbek, J. F. Sanz, and J. a. Rodriguez, “Highly active copper-ceria and copper-ceria-titania catalysts for methanol
from CO2,” Science (80-. )., vol. 345, no. 6196, pp. 546–550, 2014.

26. Analyzing hydrogenation of CO2 on ceria

29. % use ode45 first and then compare to ode15s, this is a 'stiff' problem
% (concentrations are changing at different time scales) so I want to
% measure the accuracy between the two
x0=0; xf=40; %start at time zero and go for 40 seconds
%assume 1:1 molar ratio of CO2 and H2 with the number active sites being 7
y0=[5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 5]; %last point is active sites
options = odeset('RelTol',1e-3,'NonNegative',ones(25,1));
[x,y] = ode15s(@f,[x0 xf],y0,options);
%plot methanol as a function of time
figure
plot(x,y(:,19))
xlabel('time (s)')
ylabel('[CH_3OH]')
%plot adsorbed hrdroxyl
figure
plot(x,y(:,20))
xlabel('time (s)')
ylabel('[OH*]')
%adsorbed water
figure
plot(x,y(:,21))
xlabel('time (s)')

30. function dC = f(~,y)
% all rate constants are in INVERSE SECONDS
global ratesk
load myparam
% ratesk = [1.95e4 3.23e1
% 2.67e4 5.19e4
% 6.31e3 3.13e5
% 3.11e1 1.78e2
% 2.17e3 4.21e3
% 1.02e1 3.57e3
% 4.24e6 3.82e6
% 3.82e-3 1.31e-2
% 7.33e-4 5.41e-5
% 6.55e-2 8.21e-4
% 3.91e4 3.55e2
% 5.11e3 8.26e5
% 7.63e3 5.19e2
% 4.55e-4 1.98e-2
% 1.02e1 2.82e3
% 3.76e1 5.43e-1
% 9.21 2.13e-1
% 8.33e1 4.08e2
% 5.91e-3 3.41e-5
% 7.32e2 2.45e2
% 5.89e1 2.87e1];

31. Additional Factors
 Treatments (a priori)
 Incipient wetness method, reverse microemulsion (RM), coprecipitation and
impregnation
 Dopants and deposits
 Varying temperature and pressure a priori and/or in situ
 Thermodynamic and kinetic effects

32. Future Work
 Comparing catalysts from database
 Mapping and understanding the change in electron density as CO2 is adsorbed onto surface and
converted
 Plot activity, activation energy and adsorption energy vs d-band center
 Machine Learning
 Understand Transition State Theory and recalculate k’s

33. Future Work

35. Machine Learning
 Supervised Learning
 Using gradient descent to maximize
specified characteristics (θ0, θ1 are independent variables)

36. Machine Learning
 Classification
𝑦 = {0,1}
Where 0 is negative and 1 is positive
Decision Boundary

37. Machine Learning

39. Questions?

40. Works Cited
1. © 2013 Liquid Light Corporation. All rights reserved. | www.llchemical.com
2. UNEP, Introduction to Climate Change
http://www.grida.no/climate/vital/05.htm
3. Global greenhouse gas emissions (2012), International Energy Agency.
4. Dr. John T. Gleaves – received on December 2, 2015
5. Lo, C.; Cheng, Zhuo. EECE Washington University
6. Ref #2 of J. Graciani, K. Mudiyanselage, F. Xu, a. E. Baber, J. Evans, S. D. Senanayake, D. J. Stacchiola, P. Liu,
J. Hrbek, J. F. Sanz, and J. a. Rodriguez, “Highly active copper-ceria and copper-ceria-titania catalysts for
methanol synthesis from CO2,” Science (80-. )., vol. 345, no. 6196, pp. 546–550, 2014.