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用于二氧化碳电还原的铜锌催化剂活性位点的性质

Nature of the Active Sites of Copper Zinc Catalysts for Carbon Dioxide Electroreduction.

作者信息

Zhen Shiyu, Zhang Gong, Cheng Dongfang, Gao Hui, Li Lulu, Lin Xiaoyun, Ding Zheyuan, Zhao Zhi-Jian, Gong Jinlong

机构信息

Key Laboratory for Green Chemical Technology of Ministry of Education, School of Chemical Engineering and Technology, Collaborative Innovation Center of Chemical Science and Engineering, Tianjin University, Weijin Road 92, Tianjin, 300072, China.

Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China.

出版信息

Angew Chem Int Ed Engl. 2022 May 23;61(22):e202201913. doi: 10.1002/anie.202201913. Epub 2022 Mar 30.

Abstract

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.

摘要

在铜基催化剂上通过电化学将一氧化碳还原(CO ER)为多碳化学原料具有相当大的吸引力,但活性位点的性质不明确,这阻碍了催化剂的合理设计和大规模工业化。本文描述了一种大规模模拟,结合基于神经网络的全局优化和密度泛函理论计算,以获得逼真的铜锌纳米颗粒模型以及CO ER中铜锌催化剂上C产物活性位点的原子级结构。通过基于神经网络势的高通量测试分析了2000多个表面位点后,确定了两种活性位点,即平衡的铜锌位点和富锌的铜锌位点,两者都促进C-C偶联,随后的计算和实验研究验证了这一点。这项工作为高性能铜基催化剂的设计提供了一个范例,并可能提供一种通用策略,以准确识别复杂催化系统中活性位点的原子结构。

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