# Surface Nanostructures of Pt-Compositionally Complex Alloy Single-Crystal Model Catalyst Surfaces for Improved Oxygen Reduction Reaction: Machine-Learning-Assisted Exploration

https://mdr.nims.go.jp/datasets/99e22a27-11f2-44fc-be93-1e29fc38ba78

## File

- [MI_Pt-CCA_re-revised main_final version.docx](https://mdr.nims.go.jp/filesets/240a1cba-04f8-4137-b4a1-78239e857abe/download) ([Detail](https://mdr.nims.go.jp/filesets/240a1cba-04f8-4137-b4a1-78239e857abe.md))

## Id

99e22a27-11f2-44fc-be93-1e29fc38ba78

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-26T02:57:48.448688Z

## Updated at

2026-02-14T13:06:22.865640Z

## Published at

2026-04-05T23:25:49.897016Z

## Doi

https://doi.org/10.48505/nims.6093

## First published url

https://doi.org/10.1021/acsami.4c22052

## Date published

2025-04-16

## Recorded date published

2025-4-16

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: 'Surface Nanostructures of Pt-Compositionally Complex Alloy Single-Crystal
    Model Catalyst Surfaces for Improved Oxygen Reduction Reaction: Machine-Learning-Assisted
    Exploration'
  title_type: original
  lang: en

## Description

- description: 'We investigated oxygen reduction reaction (ORR) properties of Pt-containing
    compositionally complex alloy (Pt-CCA) single-crystal model catalyst surfaces
    to optimize dry-process synthesis conditions, that is, CCA compositions of less-noble
    alloying elements and their synthesis (annealing) temperatures. Using a machine-learning
    approach, we effectively navigated the large space of possible synthesis conditions
    to minimize the experimental workload. The ORR activity and durability of the
    Pt/CCA/Pt(111) model catalyst surfaces (synthesized through vacuum deposition
    on a Pt(111) substrate of nonequiatomic Cr–Mn–Fe–Co–Ni or Mn–Fe–Co–Ni alloy (111)
    lattice stacking layers, followed by a surface Pt(111) layer) depend upon the
    alloy composition and synthesis temperature: the model catalyst surfaces synthesized
    with specific combinations of these two parameters outperformed benchmark surfaces
    such as Pt/equiatomic Cr–Mn–Fe–Co–Ni/Pt(111) in terms of the ORR durability during
    potential-cycle loading. The outstanding ORR properties are attributed to the
    use of machine learning to predict synthesis conditions that are closely linked
    to the atomic-level surface microstructures that favor enhanced ORR properties.
    These microstructures enable the formation of a so-called “pseudo-core-shell-like
    structure”, i.e., surface Pt(111) underlaid with CCA(111) lattice stacking layers
    with atomically distributed active elements (Co and/or Ni) close to the surface
    that are beneficial for ORR property enhancements. This study demonstrates that
    not only the “high-entropy” effect of charged less-noble CCA elements but also
    the precise control of elemental distributions in the near-surface vicinity in
    the pristine state, resulting from optimized CCA compositions and synthesis temperatures,
    are the key factors to improve Pt-CCA catalyst material systems.'
  description_type: abstract
  lang: und

## Creator

- name: Yoshihiro Chida
  role: author
- name: Sae Dieb
  role: author
  orcid: https://orcid.org/0000-0002-8111-2009
  organization: National Institute for Materials Science
- name: Hiraku Masui
  role: author
- name: Arata Umehara
  role: author
- name: Keitaro Sodeyama
  role: author
  orcid: https://orcid.org/0000-0002-9228-0729
  organization: National Institute for Materials Science
- name: Toshimasa Wadayama
  role: author

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: compositionally complex alloy, materials informatics, Catalysts, oxygen
    reduction reaction
  schema: not_defined

## Rights

- description: This document is the Accepted Manuscript version of a Published Work
    that appeared in final form in ACS Applied Materials & Interfaces, copyright ©
    2025 American Chemical Society after peer review and technical editing by the
    publisher. To access the final edited and published work see https://doi.org/10.1021/acsami.4c22052.
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo

start_date: 2025-04-06
end_date: 2026-04-07

## Journal

- title: ACS Applied Materials & Interfaces
  issn: '19448244'
  volume: '17'
  issue: '15'
  start_page: 22557
  end_page: 22567

## Conference



## Related item



## Funding

- identifier: 20001184-0
  funder_name: New Energy and Industrial Technology Development Organization
- identifier: JP21H01645
  funder_name: Japan Society for the Promotion of Science
- identifier: JP23KJ0111
  funder_name: Japan Society for the Promotion of Science

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## Fileset

- id: 240a1cba-04f8-4137-b4a1-78239e857abe
  filename: MI_Pt-CCA_re-revised main_final version.docx
  content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
  size: 4134345
  md5: 88ef2a90d83e4439b9db6399f09c4505

## Thumbnail

fileset_id: 240a1cba-04f8-4137-b4a1-78239e857abe
filename: MI_Pt-CCA_re-revised main_final version.docx