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Operando Cluster Catalysis via Coupled Surface-Subsurface Dynamics.

Hong-Yue Wang1, Jia-Lan Chen1, Xin-Ze Qi1

  • 1State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, Anhui, China.

Journal of the American Chemical Society
|November 5, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning reveals how catalyst surfaces restructure during reactions, forming active metal clusters. These clusters, particularly Pd10, significantly enhance reaction rates and selectivity in acetylene hydrogenation.

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Area of Science:

  • Surface Science and Catalysis
  • Computational Materials Science
  • Chemical Engineering

Background:

  • Catalyst restructuring under reaction conditions is a known phenomenon.
  • The precise mechanisms by which coupled surface-subsurface dynamics influence active site formation and performance remain unclear.
  • Understanding operando catalyst behavior is crucial for designing efficient catalytic systems.

Purpose of the Study:

  • To develop and apply a machine-learning-accelerated multiscale framework for atomic-scale resolution of operando catalyst restructuring.
  • To elucidate the role of coupled surface-subsurface dynamics in active site emergence and performance.
  • To identify dominant active ensembles and quantify structure-activity relationships.

Main Methods:

  • Integration of grand-canonical Monte Carlo (GCMC) sampling, neural-network molecular dynamics (NNMD), and first-principles microkinetics.
  • Application to Pd-catalyzed acetylene hydrogenation as a model system.
  • Analysis of structure-activity relationships based on cluster height and composition.

Main Results:

  • Operando restructuring leads to the formation of single atoms and clusters (Pd1-Pd10) driven by hydrocarbon adsorption and subsurface carbon.
  • The Pd10 cluster was identified as the dominant active ensemble, yielding a ~36,000-fold rate enhancement and >99% ethylene selectivity.
  • The approach was validated across multiple transition metals (Ag, Cu, Au, Ni, Rh, Pt), showing the necessity of moderate coadsorption and subsurface carbon for cluster formation.

Conclusions:

  • Coupled surface-subsurface dynamics are critical for the emergence and performance of active sites during catalysis.
  • The developed multiscale framework provides a transferable method for studying operando catalyst restructuring across various systems.
  • This work offers fundamental insights into catalyst design for enhanced activity and selectivity in complex reaction environments.