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通过将关键中间体映射到活性位点描述符来推进电催化反应。

Advancing electrocatalytic reactions through mapping key intermediates to active sites descriptors.

作者信息

Sun Xiaowen, Araujo Rafael B, Dos Santos Egon Campos, Sang Yuanhua, Liu Hong, Yu Xiaowen

机构信息

State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China.

Department of Materials Science and Engineering, The Ångstrom Laboratory, Uppsala University, SE-751 03 Uppsala, Sweden.

出版信息

Chem Soc Rev. 2024 Jul 15;53(14):7392-7425. doi: 10.1039/d3cs01130e.

Abstract

Descriptors play a crucial role in electrocatalysis as they can provide valuable insights into the electrochemical performance of energy conversion and storage processes. They allow for the understanding of different catalytic activities and enable the prediction of better catalysts without relying on the time-consuming trial-and-error approaches. Hence, this comprehensive review focuses on highlighting the significant advancements in commonly used descriptors for critical electrocatalytic reactions. First, the fundamental reaction processes and key intermediates involved in several electrocatalytic reactions are summarized. Subsequently, three types of descriptors are classified and introduced based on different reactions and catalysts. These include d-band center descriptors, readily accessible intrinsic property descriptors, and spin-related descriptors, all of which contribute to a profound understanding of catalytic behavior. Furthermore, multi-type descriptors that collectively determine the catalytic performance are also summarized. Finally, we discuss the future of descriptors, envisioning their potential to integrate multiple factors, broaden application scopes, and synergize with artificial intelligence for more efficient catalyst design and discovery.

摘要

描述符在电催化中起着至关重要的作用,因为它们可以为能量转换和存储过程的电化学性能提供有价值的见解。它们有助于理解不同的催化活性,并能够在不依赖耗时的试错方法的情况下预测更好的催化剂。因此,本综述着重强调关键电催化反应常用描述符的重大进展。首先,总结了几种电催化反应所涉及的基本反应过程和关键中间体。随后,根据不同的反应和催化剂对三种类型的描述符进行了分类和介绍。这些包括d带中心描述符、易于获得的本征性质描述符和自旋相关描述符,所有这些都有助于深入理解催化行为。此外,还总结了共同决定催化性能的多类型描述符。最后,我们讨论了描述符的未来,设想它们整合多种因素、拓宽应用范围以及与人工智能协同以实现更高效催化剂设计和发现的潜力。

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