Integration of Facet-Dependent, Adsorbate-Driven Surface Reconstruction into Multiscale Models for the Design of Ni-Based Bimetallic Catalysts for Hydrogen Oxidation

Ni-based bimetallic catalysts (NiM) show promise to replace expensive Pt-based catalysts for hydrogen oxidation reaction (HOR). However, the effect of dopant and reaction conditions on the adsorbate-driven surface reconstruction of NiM nanoparticles remains largely unexplored. Here, we use a multiscale modeling approach – integrating density functional theory, kubic harmonics interpolation, and microkinetic modeling – to investigate the interplay between dopant, reaction conditions, facet, adsorbate type, adsorbate coverage, in situ surface structure, and performance for NiM nanoparticles during HOR. Clear periodic trends appear in dopant effects on key adsorption energies, with dopants showing 7-fold greater effects when located in the surface as compared to subsurface. Multi-faceted nanoparticle models showed a non-uniform correlation between O* coverage and surface reconstruction. HOR performance was facet-dependent, with the highest performance occurring at reactive fronts formed between regions of high O* and OH* coverage. The nanoparticle averaged performance showed promotional effects for nearly all dopants compared to pure Ni, with the best-performing dopants located preferentially in the surface layer (e. g. Au, Pd, Ag). Taken together, this work emphasizes the importance of understanding the interplay between reaction conditions, surface reconstruction, and HOR performance for NiM nanoparticles, enabling researchers to both predict and control the working nanoscale catalyst structure.