Ordering-dependent subsurface reconfiguration reshapes the ORR activity ranking of L10 and L12 Pt–Fe nanoparticles
Year: 2026
Type: preprint
Abstract: Nanoparticle catalyst properties are often governed by activation-induced subsurface reconfiguration and ordering-dependent structural responses rather than nominal bulk phases. Static idealized models therefore can miss the kinetic pathways that drive subsurface evolution and active-site redistribution during synthesis and activation. Here, we present an activation-mapped kinetic modeling workflow combining density functional theory (DFT), machine-learning cluster expansion (ML-CE), Metropolis Monte Carlo (MMC), and kinetic Monte Carlo (KMC) to trace processing-driven structural evolution in L1 0 and L1 2 Pt–Fe nanoparticles. We define the activation-derived ensemble as the statistically sampled Pt-skin configurations generated by high-temperature ordering, selective Fe leaching, vacancy-mediated restructuring, and room-temperature activation. This ensemble construction links processing history to subsurface composition, surface morphology, and site-resolved catalytic response. Within the modeled activation sequence, phase-dependent Fe leaching produces an inversion in second-layer Fe enrichment relative to the ideal ordered references and changes the dominant active-site families, making L1 0 -derived nanoparticles terrace-controlled, whereas L1 2 -derived nanoparticles become comparatively edge-controlled. When coupled to steady-state *OH coverage kinetics, these activation-derived ensembles recover the experimental oxygen reduction reaction (ORR) activity ranking and reveal phase-specific size–activity trends missed by ideal models. More broadly, the results show that the activation-derived structures, rather than pristine phases alone, govern catalyst ranking and highlight how ordering-dependent subsurface reconfiguration can reshape static-model predictions.
Source: ChemRxiv
Authors: Jiadong Shen, Qi Xiao, Liang Cao
Institution: Zhejiang University
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223276373