析氧
纳米技术
表征(材料科学)
催化作用
电化学
电解
生化工程
合理设计
材料科学
化学
氧化物
计算机科学
电化学能量转换
大规模运输
可扩展性
电解水
制氢
克拉克电极
电极
工艺工程
可再生能源
作者
Rabia Khalid,Muhammad Tahir,Muhammad Umar,Pin Fang,Yujing Li
摘要
ABSTRACT Water electrolysis is pivotal for converting renewable energy into clean hydrogen fuel, addressing global energy demand sustainably. However, the development of highly efficient and cost‐effective catalysts for the oxygen evolution reaction (OER) remains a significant challenge, particularly at the industrial scale. This report explores a newly discovered pathway, the oxide path mechanism (OPM) for OER—mechanism involving the oxide formation and evolution during the reaction, emphasizing its potential to overcome existing limitations. OPM enables direct O─O coupling without oxygen vacancies, offering superior stability. We detail both classical and innovative in‐situ characterization techniques that are central to unraveling the OER mechanism. The advanced in‐situ electrochemical techniques, such as inductively coupled plasma mass spectroscopy, X‐ray photoelectron spectroscopy, and Mössbauer spectroscopy, coupled with in‐situ structural analyses, provide crucial insights into the catalyst surface, the electrode‐electrolyte interface and the kinetics of OER. This review provides a systematic analysis integrating classical electrochemical methods with advanced in‐situ/operando techniques, specifically focusing on understanding OPM. While numerous studies have examined individual characterization methods, this study systematically integrates traditional electrochemical approaches with in‐situ and operando techniques, offering critical insights into their complementary roles in elucidating reaction pathways. The integration of these methodologies provides unprecedented understanding of catalyst behavior under operational conditions, guiding the rational design of next‐generation OER catalysts. Furthermore, we discuss essential standardized test toolkits and protocols, such as those for rotating disk electrode and membrane electrode assembly, which are vital for ensuring reproducibility and scalability in OER catalyst research.
科研通智能强力驱动
Strongly Powered by AbleSci AI