Searching novel complex solid solution electrocatalysts in unconventional element combinations

Krysiak, O.A. and Schumacher, S. and Savan, A. and Schuhmann, W. and Ludwig, A. and Andronescu, C.

Volume: Pages:
DOI: 10.1007/s12274-021-3637-z
Published: 2021

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. [Figure not available: see fulltext.] © 2021, The Author(s).

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