化学
铜
催化作用
锌
无机化学
电化学
密度泛函理论
Atom(片上系统)
活动站点
纳米颗粒
纳米技术
材料科学
化学工程
计算机科学
计算化学
物理化学
冶金
电极
有机化学
嵌入式系统
工程类
作者
Shiyu Zhen,Gong Zhang,Dongfang Cheng,Hui Gao,Lulu Li,Xiaoyun Lin,Zheyuan Ding,Zhi‐Jian Zhao,Jinlong Gong
标识
DOI:10.1002/anie.202201913
摘要
The electrochemical CO2 reduction (CO2 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 C2+ products on CuZn catalysts in CO2 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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