Liu Jieyu, Liu Hui, Chen Haijun, Du Xiwen, Zhang Bin, Hong Zhanglian, Sun Shuhui, Wang Weichao
Department of Electronics National Institute for Advanced Materials Renewable Energy Conversion and Storage Center Tianjin Key Laboratory of Photo-Electronic Thin Film Device and Technology Nankai University Tianjin 300071 China.
Institute of New Energy Materials School of Materials Science and Engineering Tianjin University Tianjin 300350 China.
Adv Sci (Weinh). 2019 Nov 27;7(1):1901614. doi: 10.1002/advs.201901614. eCollection 2020 Jan.
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.
氧还原催化,包括氧还原反应(ORR)和析氧反应(OER),对于确定能量转换和存储设备(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。电化学催化剂的合理设计取代了传统的试错方法,从而推动了研发进程。识别连接催化剂结构、活性以及选择性的描述符是合理设计的关键。在过去几十年中,已开发出两类描述符,包括基于体相和基于表面的描述符,以探究结构-性能关系。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,从而推动未来电催化剂设计中更通用描述符的进一步发展。在此,对当前用于氧电催化的基准活性描述符及其应用进行了综述。特别关注规避含氧化合物中间体的标度关系。对于混合材料,通过考虑更多因素(如界面重构、限域效应、多位点吸附等),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理可能至关重要。