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用于电催化将CO还原为高价值碳氢化合物的单原子催化剂的设计原理

Design Principles of Single-Atom Catalysts for Electrocatalytic CO Reduction to High-Value Hydrocarbons.

作者信息

Zhao Wenhao, Wang Shifu, Chatir El Mehdi, Li Xuning, Huang Yanqiang

机构信息

State Key Laboratory of Catalysis, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China.

出版信息

Chem Asian J. 2025 Jun 6:e00545. doi: 10.1002/asia.202500545.

Abstract

The CO electrochemical reduction (CORR) is regarded as a promising approach to mitigate carbon emissions while producing valuable chemical feedstocks and fuels. Among the possible products, multi-carbon (C) compounds such as ethylene and ethanol are highly desirable due to their higher energy density and industrial relevance. Recently, single-atom catalysts (SACs) have emerged as a powerful class of electrocatalysts in CORR, offering high atomic efficiency and tunable active sites. However, challenges such as sluggish C─C coupling kinetics, dynamic evolution of the catalytic sites, limited understanding of reaction mechanism, and difficulties at controlling product selectivity hinder their further development for large-scale application. Hence, this review explores the underlying mechanisms for CO to C product conversion, emphasizing catalyst design strategies to enhance C─C coupling efficiency and selectivity. Furthermore, recent advances in in situ characterization techniques that provide atomic-level insights into reaction intermediates and active site evolution are discussed. Finally, the potential of machine learning approaches in accelerating catalysts discovery by optimizing SACs structures, identifying key design parameters, and predicting catalytic performance is highlighted. Overall, this study aims to provide a comprehensive reference for the rational design of SACs for effective and selective CO conversion into C products.

摘要

CO电化学还原(CORR)被视为一种在生产有价值的化学原料和燃料的同时减少碳排放的有前景的方法。在可能的产物中,诸如乙烯和乙醇等多碳(C)化合物因其较高的能量密度和工业相关性而备受青睐。最近,单原子催化剂(SACs)已成为CORR中一类强大的电催化剂,具有高原子效率和可调控的活性位点。然而,诸如缓慢的C─C偶联动力学、催化位点的动态演变、对反应机理的理解有限以及控制产物选择性方面的困难等挑战阻碍了它们在大规模应用中的进一步发展。因此,本综述探讨了CO转化为C产物的潜在机制,重点强调了提高C─C偶联效率和选择性的催化剂设计策略。此外,还讨论了原位表征技术的最新进展,这些技术能提供有关反应中间体和活性位点演变的原子水平见解。最后,强调了机器学习方法通过优化SACs结构、识别关键设计参数和预测催化性能在加速催化剂发现方面所具有的潜力。总体而言,本研究旨在为合理设计用于将CO有效且选择性地转化为C产物的SACs提供全面的参考。

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