催化作用
电催化剂
材料科学
电化学
表征(材料科学)
扫描透射电子显微镜
纳米技术
贵金属
化学工程
化学
透射电子显微镜
电极
物理化学
有机化学
工程类
作者
Olga A. Krysiak,Simon Schumacher,Alan Savan,Wolfgang Schuhmann,Alfred Ludwig,Corina Andronescu
出处
期刊:Nano Research
[Springer Nature]
日期:2021-07-01
卷期号:15 (6): 4780-4784
被引量:26
标识
DOI:10.1007/s12274-021-3637-z
摘要
Abstract Despite outstanding accomplishments in catalyst discovery, finding new, more efficient, environmentally neutral, and noble metal-free catalysts remains challenging and unsolved. Recently, complex solid solutions consisting of at least five different elements and often named as high-entropy alloys have emerged as a new class of electrocatalysts for a variety of reactions. The multicomponent combinations of elements facilitate tuning of active sites and catalytic properties. Predicting optimal catalyst composition remains difficult, making testing of a very high number of them indispensable. We present the high-throughput screening of the electrochemical activity of thin film material libraries prepared by combinatorial co-sputtering of metals which are commonly used in catalysis (Pd, Cu, Ni) combined with metals which are not commonly used in catalysis (Ti, Hf, Zr). Introducing unusual elements in the search space allows discovery of catalytic activity for hitherto unknown compositions. Material libraries with very similar composition spreads can show different activities vs. composition trends for different reactions. In order to address the inherent challenge of the huge combinatorial material space and the inability to predict active electrocatalyst compositions, we developed a high-throughput process based on co-sputtered material libraries, and performed high-throughput characterization using energy dispersive X-ray spectroscopy (EDS), scanning transmission electron microscopy (SEM), X-ray diffraction (XRD) and conductivity measurements followed by electrochemical screening by means of a scanning droplet cell. The results show surprising material compositions with increased activity for the oxygen reduction reaction and the hydrogen evolution reaction. Such data are important input data for future data-driven materials prediction.
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