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异质结构电催化剂:从基本微观动力学模型到电子构型与界面反应微环境

Heterostructured Electrocatalysts: from Fundamental Microkinetic Model to Electron Configuration and Interfacial Reactive Microenvironment.

作者信息

Li Yun, Hassan Md Samim, Zhao Xin, Rogach Andrey L

机构信息

Department of Materials Science and Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, 999077, P.R. China.

IT4Innovations, VSB - Technical University of Ostrava, Ostrava-Poruba, 70800, Czech Republic.

出版信息

Adv Mater. 2025 Apr;37(15):e2418146. doi: 10.1002/adma.202418146. Epub 2025 Mar 4.

Abstract

Electrocatalysts can efficiently convert earth-abundant simple molecules into high-value-added products. In this context, heterostructures, which are largely determined by the interface, have emerged as a pivotal architecture for enhancing the activity of electrocatalysts. In this review, the atomistic understanding of heterostructured electrocatalysts is considered, focusing on the reaction kinetic rate and electron configuration, gained from both empirical studies and theoretical models. We start from the fundamentals of the microkinetic model, adsorption energy theory, and electric double layer model. The importance of heterostructures to accelerate electrochemical processes via modulating electron configuration and interfacial reactive microenvironment is highlighted, by considering rectification, space charge region, built-in electric field, synergistic interactions, lattice strain, and geometric effect. We conclude this review by summarizing the challenges and perspectives in the field of heterostructured electrocatalysts, such as the determination of transition state energy, their dynamic evolution, refinement of the theoretical approaches, and the use of machine learning.

摘要

电催化剂能够有效地将地球上储量丰富的简单分子转化为高附加值产品。在此背景下,很大程度上由界面决定的异质结构已成为增强电催化剂活性的关键结构。在这篇综述中,我们考虑了对异质结构电催化剂的原子层面理解,重点关注从实证研究和理论模型中获得的反应动力学速率和电子构型。我们从微观动力学模型、吸附能理论和双电层模型的基本原理开始。通过考虑整流、空间电荷区、内建电场、协同相互作用、晶格应变和几何效应,强调了异质结构通过调节电子构型和界面反应微环境来加速电化学过程的重要性。我们通过总结异质结构电催化剂领域的挑战和前景来结束这篇综述,例如过渡态能量的确定、它们的动态演化、理论方法的完善以及机器学习的应用。

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