期刊
ACS ENERGY LETTERS
卷 3, 期 12, 页码 2983-2988出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsenergylett.8b01933
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资金
- Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub through the Office of Science of the U.S. Department of Energy [DE-SC0004993]
- U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists, Office of Science Graduate Student Research (SCGSR) program
- DOE [DE-SC0014664]
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1512759] Funding Source: National Science Foundation
Recent experiments have shown that CO reduction on oxide derived Cu nanoparticles (NP) are highly selective toward C2+ products. However, understanding of the active sites on such NPs is limited, because the NPs have, similar to 200 000 atoms with more than 10 000 surface sites, far too many for direct quantum mechanical calculations and experimental identifications. We show here how to overcome the computational limitation by combining multiple levels of theoretical computations with machine learning. This approach allows us to map the machine learned CO adsorption energies on the surface of the copper nanoparticle to construct the active site visualization (ASV). Furthermore, we identify the structural criteria for optimizing selective reduction by predicting the reaction energies of the potential determining step, Delta E-OCCOH, for the C2+ product. Based on this structural criterion, we design a new periodic copper structure for CO reduction with a theoretical faradaic efficiency of 97%.
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