Workflow-driven catalytic modulation from single-atom catalysts to Au–alloy clusters on graphene

Abstract

Gold-based (Au) nanostructures are efficient catalysts for CO oxidation, hydrogen evolution (HER), and oxygen evolution (OER) reactions, but stabilizing them on graphene (Gr) is challenging due to weak affinity from delocalized carbon orbitals. This study investigates forming metal alloys to enhance stability and catalytic performance of Au-based nanocatalysts. Using ab initio density functional theory, we characterize sub-nanoclusters (M = Ni, Pd, Pt, Cu, and Ag) with atomicities, both in gas-phase and supported on Gr. We find that M atoms act as “anchors,” enhancing binding to Gr and modulating catalytic efficiency. Notably, /Gr shows improved stability, with segregation tendencies mitigated upon adsorption on Gr. The d-band center () model indicates catalytic potential, correlating an optimal range of eV for HER and OER catalysts. Incorporating Au into adjusts closer to the Fermi level, especially for Group-10 alloys, offering designs with improved stability and efficiency comparable to pure Au nanocatalysts. Our methodology leveraged SimStack, a workflow framework enabling modeling and analysis, enhancing reproducibility, and accelerating discovery. This work demonstrates SimStack’s pivotal role in advancing the understanding of composition-dependent stability and catalytic properties of Au-alloy clusters, providing a systematic approach to optimize metal-support interactions in catalytic applications.