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Progress and Challenges Toward the Rational Design of Oxygen Electrocatalysts Based on a Descriptor Approach

机译:基于描述符方法的氧电催化剂合理设计的进展与挑战

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摘要

Oxygen redox catalysis, including the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), is crucial in determining the electrochemical performance of energy conversion and storage devices such as fuel cells, metal–air batteries,and electrolyzers. The rational design of electrochemical catalysts replaces the traditional trial‐and‐error methods and thus promotes the R&D process. Identifying descriptors that link structure and activity as well as selectivity of catalysts is the key for rational design. In the past few decades, two types of descriptors including bulk‐ and surface‐based have been developed to probe the structure–property relationships. Correlating the current descriptors to one another will promote the understanding of the underlying physics and chemistry, triggering further development of more universal descriptors for the future design of electrocatalysts. Herein, the current benchmark activity descriptors for oxygen electrocatalysis as well as their applications are reviewed. Particular attention is paid to circumventing the scaling relationship of oxygen‐containing intermediates. For hybrid materials, multiple descriptors will show stronger predictive power by considering more factors such as interface reconstruction, confinement effect, multisite adsorption, etc. Machine learning and high‐throughput simulations can thus be crucial in assisting the discovery of new multiple descriptors and reaction mechanisms.
机译:氧气的氧化还原催化,包括氧气还原反应(ORR)和氧气释放反应(OER),对于确定能量转换和存储设备(例如燃料电池,金属空气电池和电解槽)的电化学性能至关重要。合理设计的电化学催化剂替代了传统的反复试验方法,从而促进了研发过程。确定链接结构和活性以及催化剂选择性的描述符是合理设计的关键。在过去的几十年中,已经开发了两种类型的描述符,包括基于体积的和基于表面的描述符,以探讨结构与属性之间的关系。将当前的描述符相互关联将促进对基础物理和化学的理解,从而触发进一步开发用于将来电催化剂设计的通用描述符。在此,回顾了当前用于氧电催化的基准活性描述符及其应用。要特别注意规避含氧中间体的水垢关系。对于混合材料,通过考虑更多因素(例如界面重构,约束效应,多位吸附等),多个描述符将显示出更强的预测能力。因此,机器学习和高通量模拟对于协助发现新的多个描述符和反应机制至关重要。 。

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