Main Descriptors to correlate structures with performances of electrocatalysts.
Main Descriptors to correlate structures with performances of electrocatalysts.
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DOI：
10.1002/anie.202111026
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发表时间：
2021-09
影响因子：
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通讯作者：
Bin Wang;Fuxiang Zhang
中科院分区：
文献类型：
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作者：
Bin Wang;Fuxiang Zhang
氢/氧氧化还原催化分别涉及析氢反应（HER）和氢氧化反应（HOR）以及氧还原反应（ORR）和析氧反应（OER），其在决定能量转换和存储装置（例如水电解、燃料电池和金属-空气电池）的电化学性能方面至关重要。寻找高活性和稳定性的催化剂的传统试错法通常是繁琐和低效的。从这些方法到催化剂合理设计原则的进展具有重要意义。为了开发合理的催化材料设计策略，迫切需要确定决定催化性能的最重要参数。核心问题是能量标度关系及其衍生物-火山图，这有助于定量了解催化趋势。在过去的几十年中，已经开发了一些描述符来解开结构-性能关系。在这篇综述中，我们总结了电催化反应性描述符，包括吸附能描述符涉及反应中间体，电子描述符代表的d-带中心，结构描述符，和通用描述符，并讨论其优点/局限性。了解现有的电催化性能的趋势和预测有前途的催化材料，使用反应性描述符应该使催化剂的合理构建。采用人工智能和机器学习来发现新的和先进的描述符。最后，我们分析了线性标度关系的性质，并提出了几种策略来规避或绕过已建立的标度关系，以克服对催化性能的约束。
Hydrogen/oxygen redox catalysis, which involves the hydrogen evolution reaction (HER) and hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), respectively, is crucial in determining the electrochemical performance of energy conversion and storage devices, such as water electrolysis, fuel cells and metal-air batteries. Traditional trial and error approaches to search for catalysts with high activity and stability are typically tedious and inefficient. The progress from these approaches to the rational design principle of catalysts is of great significance. To develop reasonable design strategies for catalytic materials, identifying the most important parameters in determining the catalytic performance is urgently required. The central issue is the energy scaling relationships and their derivatives-volcano plot, which facilitates the quantitative understanding of catalytic trends. In the past decades, several descriptors have been developed to unravel the structure-performance relationships. In this review, we summarize reactivity descriptors in electrocatalysis including adsorption energy descriptors involving reaction intermediates, electronic descriptors represented by a d-band center, structural descriptors, and universal descriptors, and discuss their merits/limitations. Understanding of the trends of the existing electrocatalytic performance and prediction of promising catalytic materials using reactivity descriptors should enable the rational construction of catalysts. Artificial intelligence and machine learning are expectedly adopted to discover new and advanced descriptors. Finally, we analyze the nature of linear scaling relationships and propose several strategies to circumvent or bypass the established scaling relationships to overcome the constraints imposed on the catalytic performance.