chemical complexity of materials used in challenging applications is usually high, as many elements (4-12) are needed to adjust properties to meet frequently contradicting demands. Traditional examples are steels, superalloys, or metallic glasses, while since several years, new types of chemically complex materials are emerging such as high entropy alloys (HEA) or compositionally complex solid solutions (CSS). [1] Whereas HEA can be multi-phase materials, CSS are single-phase materials. CSS were identified recently as a discovery platform for novel electrocatalysts. [2,3] However, the poly-elemental nature of these materials makes the identification of optimal compositions for specific properties a very challenging task. The choice of constituent elements and their relative chemical composition presents an immense search space for finding materials with enhanced properties such as high activity, selectivity, and stability for a given catalytic reaction. CSS-based electrocatalysts were already successfully applied to hydrogen [4--7] and oxygen evolution reactions, [6,[8][9][10] CO, [11,12] CO 2, [11,13] and oxygen reduction reactions, [2,3,10,12,[14][15][16] methanol, [7,17,18] and ethanol oxidation [19] as well as ammonia synthesis [20] and decomposition. [21,22] The special properties of CSS arise from their unique multi-element active High entropy alloys (HEA) comprise a huge search space for new electrocatalysts. Next to element combinations, the optimization of the chemical composition is essential for tuning HEA to specific catalytic processes. Simulations of electrocatalytic activity can guide experimental efforts. Yet, the currently available underlying model assumptions do not necessarily align with experimental evidence. To study deviations of theoretical models and experimental data requires statistically relevant datasets. Here, a combinatorial strategy for acquiring large experimental datasets of multi-dimensional composition spaces is presented. Ru-Rh-Pd-Ir-Pt is studied as an exemplary, highly relevant HEA system. Systematic comparison with computed electrochemical activity enables the study of deviations from theoretical model assumptions for compositionally complex solid solutions in the experiment. The results suggest that the experimentally obtained distribution of surface atoms deviates from the ideal distribution of atoms in the model. Leveraging both advanced simulation and large experimental data enables the estimation of electrocatalytic activity and solid-solution stability trends in the 5D composition space of the HEA system. A perspective on future directions for the development of active and stable HEA catalysts is outlined.

Gas diffusion electrodes (GDE) obtained by sputtering metal films on polytetrafluoroethylene (PTFE) membranes are among the most performant electrodes used to electrochemically reduce CO2. The present work reveals several essential aspects for fabricating performant PTFE‐based gas diffusion electrodes (GDEs) for CO2 electroreduction (CO2R). We show that adding an additive layer (a mixture of carbon and Nafion™ or Nafion™ only) is required for stabilizing the metal catalyst film (Cu), deposited via sputtering on the PTFE membrane, during the CO2R experiments. We found that the PTFE membrane thickness used in the GDE fabrication plays an essential role in electrode performance. The quantification of the products formed during the CO2R conducted in a flow‐cell electrolyzer revealed that on thinner membranes, CO2R is the dominant process while on thicker ones, the H2 formation is promoted. Thus, the PTFE membrane influences the CO2 transport to the catalyst layer and can be used to promote the CO2R while maintaining a minimum H2 production.

Discovery of new electrocatalysts requires the combination of high-throughput synthesis and high-throughput screening techniques to explore the vast compositional range of compositionally complex solid solutions (CCSS). This work explores more...