Sculpting Superior Subnanometer Catalysts Directly from Inert Gold

Bulk gold, renowned for its catalytic inertness, can be transformed into an exceptionally active catalyst by engineering undercoordinated sites on its surface. However, directly sculpting such active sites from extended terraces remains a fundamental challenge. Here, we demonstrate a programmable strategy to dynamically generate subnanometer Au clusters directly from inert Au(111) surfaces, unlocking superior activity for CO oxidation. By integrating large-scale machine learning molecular dynamics with density functional theory, we decode the atomistic pathway of this restructuring process. Our approach employs thermal-CO pressure cycles to induce the ejection of step-edge atoms, forming mobile Au-CO complexes. Subsequent cooling kinetically traps these complexes into metastable, subnanometer clusters (3-6 atoms). A controlled reduction of CO exposure then precisely exposes the catalytically crucial undercoordinated sites while maintaining cluster stability. Crucially, these sculpted clusters exhibit a CO oxidation activity that far exceeds that of pristine step edges, terraces, or conventional single-atom sites. This work establishes reaction condition engineering as a powerful paradigm for sculpting active catalysts directly from bulk materials by bypassing traditional synthetic routes.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2026-02-05
DOI
https://doi.org/10.1021/jacs.5c22389
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Sculpting Superior Subnanometer Catalysts Directly from Inert Gold

Shoufu Cao, Ce Liu, Yang-Gang Wang, Xiaoqing Lu
Journal of the American Chemical Society
Machine Learning in Materials Science
article

Sculpting Superior Subnanometer Catalysts Directly from Inert Gold

Shoufu Cao, Ce Liu, Yang-Gang Wang, Xiaoqing Lu
article en

Abstract

Bulk gold, renowned for its catalytic inertness, can be transformed into an exceptionally active catalyst by engineering undercoordinated sites on its surface. However, directly sculpting such active sites from extended terraces remains a fundamental challenge. Here, we demonstrate a programmable strategy to dynamically generate subnanometer Au clusters directly from inert Au(111) surfaces, unlocking superior activity for CO oxidation. By integrating large-scale machine learning molecular dynamics with density functional theory, we decode the atomistic pathway of this restructuring process. Our approach employs thermal-CO pressure cycles to induce the ejection of step-edge atoms, forming mobile Au-CO complexes. Subsequent cooling kinetically traps these complexes into metastable, subnanometer clusters (3-6 atoms). A controlled reduction of CO exposure then precisely exposes the catalytically crucial undercoordinated sites while maintaining cluster stability. Crucially, these sculpted clusters exhibit a CO oxidation activity that far exceeds that of pristine step edges, terraces, or conventional single-atom sites. This work establishes reaction condition engineering as a powerful paradigm for sculpting active catalysts directly from bulk materials by bypassing traditional synthetic routes.

Journal of the American Chemical SocietyVol. 148(6)
China University of Petroleum, Beijing (CN), Southern University of Science and Technology (CN), Institute of Molecular Functional Materials (CN)
Sustainable cities and communities
Openalex Percentile: Top 66%
Machine Learning in Materials Science
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Sculpting Superior Subnanometer Catalysts Directly from Inert Gold — Shoufu Cao, Ce Liu, et al. · Journal of the American Chemical Society (2026) | TGRS Research Map | TGRS