Ordering-dependent subsurface reconfiguration reshapes the ORR activity ranking of L10 and L12 Pt–Fe nanoparticles
- 1Department of Chemistry
- 2Zhejiang University
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.
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BibTeX
@misc{shen2026orderingdependent,
title = {{Ordering-dependent subsurface reconfiguration reshapes the ORR activity ranking of L10 and L12 Pt–Fe nanoparticles}},
author = {Jiadong Shen and Qi Xiao and Liang Cao},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15006286/v3},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006286/v3},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Ordering-dependent subsurface reconfiguration reshapes the ORR activity ranking of L10 and L12 Pt–Fe nanoparticles
AU - Jiadong Shen
AU - Qi Xiao
AU - Liang Cao
PY - 2026
DA - 2026/10/23
PB - ChemRxiv
DO - 10.26434/chemrxiv.15006286/v3
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006286/v3
AB - 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.
ER -