Toward a Grand-Potential Approach for Multiscale Modeling of Thermocatalysis

This project will improve the understanding of catalytic reactions, which are chemical processes that turn raw materials into useful products like plastics, medicines, and cleaner fuels. These reactions are important because they can help save energy and reduce environmental damage. Catalysts speed up chemical reactions, but their effectiveness depends on factors like their shape and surrounding conditions, making the reactions difficult to predict. The project will develop a new method for studying catalytic reactions by considering all important factors together. Researchers will test this approach using computer simulations and laboratory experiments, with a special focus on how carbon monoxide can be converted into higher value products using copper-based catalysts. The research could lead to better and more efficient catalysts, supporting cleaner and more sustainable technologies. The project will also provide educational opportunities for students preparing for careers in science and engineering. This project will establish a comprehensive grand-potential framework for thermocatalytic reactions, addressing longstanding challenges in accurately and efficiently modeling gas-phase catalytic processes under realistic operating conditions. By integrating first-principles calculations with ab initio molecular dynamics simulations, the project will overcome the limitations of conventional static approaches in capturing the dynamic interplay among catalyst surfaces, chemisorbed intermediates, and gas-phase species across varying thermodynamic environments. This framework will enable a more rigorous description of reaction mechanisms by explicitly accounting for the influence of surface morphology, reaction conditions, and spectator adsorbates on catalytic activity and selectivity. In parallel, microkinetic simulations will be employed to correlate surface phase behavior with reaction kinetics, providing mechanistic insights that guide the rational optimization of catalyst performance. The theoretical predictions will be validated against experimental observations, with particular emphasis on elucidating the mechanisms of carbon monoxide hydrogenation to two-carbon products over copper-based catalysts and the promotional effects of zinc oxide species. Overall, the project will advance the fundamental understanding of industrial thermocatalysis and support the development of more efficient, selective, and sustainable catalytic processes for energy conversion and environmental applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2603102 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Jianzhong Wu | Institution: University of California-Riverside, RIVERSIDE, CA | Award Amount: $456,986 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2603102 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2603102.html