Recent years have witnessed significant development of electrocatalysis for clean energy and related potential technologies. The precise identification toward active sites of catalysts and the monitoring of product information are highly desirable to understand how the materials catalyze a specific electrocatalytic reaction. For a long period, the identification of active sites and the cognition of corresponding catalytic mechanisms are generally based on various ex situ characterization methods which actually could not capture dynamic structure and intermediate information during electrocatalytic processes. With recent developments of in situ and operando characterization techniques, it has been extensively observed that most of the catalysts would undergo structural self-reconstruction as a result of electro-derived oxidation or reduction process of the catalysts at a given potential, often accompanied by the increase or decrease of catalytic activity as well as the change of catalytic selectivity. In fact, such structural self-change in the catalytic process does make it difficult to identify the true catalytically active sites efficiently, thus hindering the understanding of the real catalytic mechanism. Therefore, we believe that understanding the self-reconstruction by the combination of reliable characterization techniques and theoretical calculations holds the key to rational design of advanced catalysts.

中文翻译：

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电催化中催化剂的结构自重

近年来，清洁能源和相关潜在技术的电催化技术取得了长足发展。对于了解催化剂如何催化特定的电催化反应，非常需要对催化剂的活性位点进行精确识别并监控产品信息。长期以来，活性位点的识别和相应催化机制的识别通常基于各种异位表征方法，这些方法实际上无法捕获电催化过程中的动态结构和中间信息。随着原位和操作表征技术的最新发展，广泛观察到，大多数催化剂在给定电势下由于电衍生的催化剂的电衍生氧化或还原过程而发生结构自重构，通常伴随着催化活性的增加或降低以及变化。催化选择性。实际上，催化过程中的这种结构自变的确使得难以有效地识别真实的催化活性位点，从而阻碍了对真实催化机理的理解。因此，我们认为，通过可靠的表征技术和理论计算相结合来理解自我重建是合理设计高级催化剂的关键。通常伴随着催化活性的增加或减少以及催化选择性的变化。实际上，催化过程中的这种结构自变的确使得难以有效地识别真实的催化活性位点，从而阻碍了对真实催化机理的理解。因此，我们认为，通过可靠的表征技术和理论计算相结合来理解自我重建是合理设计高级催化剂的关键。通常伴随着催化活性的增加或减少以及催化选择性的变化。实际上，催化过程中的这种结构自变的确使得难以有效地识别真实的催化活性位点，从而阻碍了对真实催化机理的理解。因此，我们认为，通过可靠的表征技术和理论计算相结合来理解自我重建是合理设计高级催化剂的关键。