The electrochemical CO reduction (CO ER) to multi-carbon chemical feedstocks over Cu-based catalysts is of considerable attraction but suffers with the ambiguous nature of active sites, which hinder the rational design of catalysts and large-scale industrialization. This paper describes a large-scale simulation to obtain realistic CuZn nanoparticle models and the atom-level structure of active sites for C products on CuZn catalysts in CO ER, combining neural network based global optimization and density functional theory calculations. Upon analyzing over 2000 surface sites through high throughput tests based on NN potential, two kinds of active sites are identified, balanced Cu-Zn sites and Zn-heavy Cu-Zn sites, both facilitating C-C coupling, which are verified by subsequent calculational and experimental investigations. This work provides a paradigm for the design of high-performance Cu-based catalysts and may offer a general strategy to identify accurately the atomic structures of active sites in complex catalytic systems.
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http://dx.doi.org/10.1002/anie.202201913 | DOI Listing |
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