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First-Principles Kinetic Monte Carlo Simulations for Single-Cluster Catalysis: Study of CO2 and CH4 Conversion on
Hector Prats1,2, Michail Stamatakis3
1Department of Chemistry, Physical & Theoretical Chemistry Laboratory, University of Oxford, South Parks Road, Oxford OX1 3QZ, U.K.
Single-cluster catalysts (SCCs) featuring platinum clusters on hafnium carbide significantly boost catalytic performance for multiple industrial reactions. Even minimal platinum loading enhances activity, showcasing SCCs
Area of Science:
- Catalysis
- Materials Science
- Chemical Engineering
Background:
- Transition metal (TM) clusters on transition metal carbides (TMCs) form bifunctional catalysts with tunable properties.
- Single-cluster catalysts (SCCs) exhibit strong metal-support interactions, modifying catalytic activity.
- Previous DFT studies identified Pt/HfC as a highly promising TM/TMC catalyst combination.
Purpose of the Study:
- To investigate the catalytic conversion of CO2 and CH4 on Pt/HfC using first-principles Kinetic Monte Carlo (KMC) simulations.
- To analyze the interplay between Pt clusters and the HfC support under various operating conditions and Pt loadings.
- To evaluate the catalytic activity and selectivity for five key industrial processes.
Main Methods:
- First-principles Kinetic Monte Carlo (KMC) simulations.
- Analysis of catalytic activity, selectivity, and adlayer composition.
- Evaluation across a wide range of partial pressures (CO2, CH4), temperature, and Pt loadings.
Main Results:
- Pt clusters on HfC systematically enhance catalytic performance for CO2 and CH4 conversion.
- Significant catalytic benefits are observed even at low Pt loadings (approx. 0.02 ML).
- The study evaluated dry reforming, steam reforming, partial oxidation of methane, and water-gas shift reactions.
Conclusions:
- Single-cluster catalysts (SCCs) offer extensive catalytic advantages.
- Pt/HfC demonstrates enhanced performance across multiple industrial chemical reactions.
- Kinetic modeling must consider diffusion and lateral interactions for accurate predictions.
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