High‐Efficiency Oxygen Evolution Reaction: Controllable Reconstruction of Surface Interface

析氧 过电位 催化作用 材料科学 曲面重建 机制(生物学) 表面工程 纳米技术 化学 曲面(拓扑) 物理化学 电化学 物理 几何学 电极 量子力学 生物化学 数学
作者
Lianhui Wu,Zhixi Guan,Daying Guo,Lin Yang,Xi’an Chen,Shun Wang
出处
期刊:Small [Wiley]
卷期号:19 (49): e2304007-e2304007 被引量:83
标识
DOI:10.1002/smll.202304007
摘要

The precatalyst undergoes surface reconstruction during the oxygen evolution reaction (OER) process, and the reconstituted material is the one that really plays a catalytic role. However, the degree of surface reconstruction seriously affects the catalytic performance. For this reason, it is important to establish the link between the degree of reconstruction and catalytic activity based on a deep understanding of the OER mechanism for the rational design of high-performance OER electrocatalysts. Here, the reaction mechanism of OER is briefly introduced, the competition between adsorbate evolution mechanism (AEM) mechanism and lattice oxygen-mediated mechanism (LOM) mechanism is discussed, and several activity descriptors of OER reaction are summarized. The strategies to realize OER controllable surface reconstruction are emphatically introduced, including ion leaching, element doping, regulating catalyst size, heterogeneous structure engineering, and self-reconstruction. A mechanistic perspective is emphasized to understand the relationship between dynamic surface reconstruction and electronic structure. Controlled reconfiguration of OER surface can break the limitation of proportional relationship brought by traditional AEM mechanism, also can realize the switching between AEM mechanism and LOM mechanism, thus realizing ultra-low overpotential. This review will provide some reference for surface controllable reconstruction of OER transition metal-based catalysts and reasonable development of ideal catalytic performance.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
1秒前
追风少年应助欢呼金鱼采纳,获得50
1秒前
斯文败类应助xixi采纳,获得10
1秒前
1秒前
充电宝应助Itazu采纳,获得10
2秒前
马克叔叔发布了新的文献求助10
3秒前
刘轩完成签到 ,获得积分10
3秒前
molihuakai应助YY采纳,获得10
4秒前
顾矜应助复杂平凡采纳,获得10
5秒前
Ava应助xuan采纳,获得10
5秒前
dingdong发布了新的文献求助10
5秒前
贝贝完成签到 ,获得积分0
6秒前
submarines发布了新的文献求助10
6秒前
liubowen发布了新的文献求助20
7秒前
MST完成签到,获得积分10
8秒前
情怀应助Jun采纳,获得10
8秒前
diu关闭了diu文献求助
10秒前
10秒前
白山茶发布了新的文献求助10
10秒前
寞失完成签到,获得积分10
12秒前
12秒前
桐桐应助筱尤采纳,获得30
12秒前
13秒前
美好斓应助种子采纳,获得100
13秒前
13秒前
甜甜若冰发布了新的文献求助10
13秒前
自由问芙关注了科研通微信公众号
13秒前
dph发布了新的文献求助10
16秒前
16秒前
美满又蓝应助元谷雪采纳,获得10
16秒前
梅思寒完成签到 ,获得积分10
16秒前
尊敬莞应助qifunongsuo1213采纳,获得10
16秒前
17秒前
17秒前
SciGPT应助高兴的雅阳采纳,获得10
17秒前
YY发布了新的文献求助10
18秒前
科目三应助Jerome采纳,获得10
19秒前
qing完成签到,获得积分10
19秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7580965
求助须知:如何正确求助?哪些是违规求助? 9160358
关于积分的说明 19598825
捐赠科研通 7163470
什么是DOI,文献DOI怎么找? 3265939
关于科研通互助平台的介绍 2430880
邀请新用户注册赠送积分活动 2257015