Bridging thermodynamic models and dynamic surface behavior in the design of binary Pd-based alloy catalysts

Binary Pd-based alloy catalysts have attracted significant interest for their tunable electronic and structural properties, offering promising routes toward enhanced catalytic activity, selectivity, and stability. While classical catalyst design strategies have often relied on equilibrium thermodynamic models and bulk phase diagrams, growing evidence from operando studies and in situ characterization reveals that dynamic surface restructuring, segregation phenomena, and metastability play a decisive role under real catalytic conditions. This review bridges the gap between thermodynamic predictions and dynamic surface behavior in Pd-based binary alloys, critically examining how nanoscale composition, facet orientation, and adsorbate-induced effects influence catalytic performance. Emphasis is placed on recent advances in synthesis, characterization, and computational modeling that reveal the limits of static design paradigms. We highlight emerging frameworks – such as machine learning-guided design and multi-descriptor modeling – that embrace kinetic complexity and surface dynamism, offering a roadmap for the rational development of Pd-based alloy catalysts suited to realistic, industrially relevant environments.