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Progress and Challenges Toward the Rational Design of Oxygen Electrocatalysts Based on a Descriptor Approach.
Advanced Science ( IF 14.3 ) Pub Date : 2019-11-27 , DOI: 10.1002/advs.201901614 Jieyu Liu 1 , Hui Liu 1 , Haijun Chen 1 , Xiwen Du 2 , Bin Zhang 3 , Zhanglian Hong 4 , Shuhui Sun 5 , Weichao Wang 1
Advanced Science ( IF 14.3 ) Pub Date : 2019-11-27 , DOI: 10.1002/advs.201901614 Jieyu Liu 1 , Hui Liu 1 , Haijun Chen 1 , Xiwen Du 2 , Bin Zhang 3 , Zhanglian Hong 4 , Shuhui Sun 5 , Weichao Wang 1
Affiliation
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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.
中文翻译：
基于描述符方法的氧电催化剂合理设计的进展和挑战。
氧氧化还原催化，包括氧还原反应（ORR）和析氧反应（OER），对于确定燃料电池、金属空气电池和电解槽等能量转换和存储装置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，从而促进了研发进程。识别连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，包括基于体积和表面的描述符来探测结构-性能关系。将当前描述符相互关联将促进对基础物理和化学的理解，从而引发针对未来电催化剂设计的更通用描述符的进一步开发。在此，回顾了当前氧电催化的基准活性描述符及其应用。特别注意规避含氧中间体的缩放关系。对于杂化材料，通过考虑界面重构、限域效应、多位点吸附等更多因素，多重描述符将表现出更强的预测能力。因此，机器学习和高通量模拟对于协助发现新的多重描述符和反应机制至关重要。
更新日期：2019-11-28
中文翻译：
基于描述符方法的氧电催化剂合理设计的进展和挑战。
氧氧化还原催化，包括氧还原反应（ORR）和析氧反应（OER），对于确定燃料电池、金属空气电池和电解槽等能量转换和存储装置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，从而促进了研发进程。识别连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，包括基于体积和表面的描述符来探测结构-性能关系。将当前描述符相互关联将促进对基础物理和化学的理解，从而引发针对未来电催化剂设计的更通用描述符的进一步开发。在此，回顾了当前氧电催化的基准活性描述符及其应用。特别注意规避含氧中间体的缩放关系。对于杂化材料，通过考虑界面重构、限域效应、多位点吸附等更多因素，多重描述符将表现出更强的预测能力。因此，机器学习和高通量模拟对于协助发现新的多重描述符和反应机制至关重要。
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