{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry",
    "Zhejiang University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Jiadong Shen",
      "orcid": "0009-0001-6498-6687"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Qi Xiao",
      "orcid": "0009-0003-7699-4937"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Liang Cao",
      "orcid": "0000-0003-4452-022X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c4gyhwga963mr.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15006286/v3",
  "fulltext_url": null,
  "id": "c4gyhwga963mr",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006286/v3",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2105|Electrocatalysts for Energy Conversion"
    ]
  ],
  "openalex": "W7223276373",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15006286/v3",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/c4gyhwga963mr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15006286",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006286/v3"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2105|Electrocatalysts for Energy Conversion",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Ordering-dependent subsurface reconfiguration reshapes the ORR activity ranking of L10 and L12 Pt–Fe nanoparticles",
  "title_text": "Ordering-dependent subsurface reconfiguration reshapes the ORR activity ranking of L10 and L12 Pt–Fe nanoparticles",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/c4gyhwga963mr",
  "venue": "ChemRxiv",
  "versions": []
}