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将结构与电催化剂性能相关联的主要描述符。

Main Descriptors To Correlate Structures with the Performances of Electrocatalysts.

作者信息

Wang Bin, Zhang Fuxiang

机构信息

State Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, The Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457# Zhongshan Road, Dalian 116023, Liaoning, China.

Center for Advanced Materials Research, School of Materials and Chemical Engineering, Zhongyuan University of Technology, 41# Zhongyuan Road, Zhengzhou, 450007, Henan, China.

出版信息

Angew Chem Int Ed Engl. 2022 Jan 21;61(4):e202111026. doi: 10.1002/anie.202111026. Epub 2021 Oct 28.

Abstract

Traditional trial and error approaches to search for hydrogen/oxygen redox catalysts with high activity and stability are typically tedious and inefficient. There is an urgent need to identify the most important parameters that determine the catalytic performance and so enable the development of design strategies for catalysts. In the past decades, several descriptors have been developed to unravel structure-performance relationships. This Minireview summarizes reactivity descriptors in electrocatalysis including adsorption energy descriptors involving reaction intermediates, electronic descriptors represented by a d-band center, structural descriptors, and universal descriptors, and discusses their merits/limitations. Understanding the trends in electrocatalytic performance and predicting promising catalytic materials using reactivity descriptors should enable the rational construction of catalysts. Artificial intelligence and machine learning have also been adopted to discover new and advanced descriptors. Finally, linear scaling relationships are analyzed and several strategies proposed to circumvent the established scaling relationships and overcome the constraints imposed on the catalytic performance.

摘要

传统的通过反复试验来寻找具有高活性和稳定性的氢/氧氧化还原催化剂的方法通常既繁琐又低效。迫切需要确定决定催化性能的最重要参数,从而能够开发催化剂的设计策略。在过去几十年中,已经开发了几个描述符来揭示结构-性能关系。本综述总结了电催化中的反应性描述符,包括涉及反应中间体的吸附能描述符、以d带中心表示的电子描述符、结构描述符和通用描述符,并讨论了它们的优缺点。利用反应性描述符理解电催化性能趋势并预测有前景的催化材料,应该能够合理构建催化剂。人工智能和机器学习也已被用于发现新的和先进的描述符。最后,分析了线性标度关系,并提出了几种策略来规避已建立的标度关系并克服对催化性能的限制。

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