Theoretical and Computational Chemistry

Atomic scale mechanism of Pt catalyst restructuring under a pressure of gas

Authors

  • Vaidish Sumaria Department of Chemical & Biomolecular Engineering, University of California Los Angeles, Los Angeles, California 90094, United States ,
  • Luan Nguyen Department of Chemical and Petroleum Engineering University of Kansas, Lawrence, Kansas 66045, United States ,
  • Franklin (Feng) Tao Department of Chemical and Petroleum Engineering University of Kansas, Lawrence, Kansas 66045, United States ,
  • Philippe Sautet Department of Chemical & Biomolecular Engineering, University of California Los Angeles, Los Angeles, California 90094, United States & Department of Chemistry & Biochemistry Engineering, University of California Los Angeles, Los Angeles, California 90094, United States

Abstract

Heterogeneous catalysis is key for chemical transformations. Understanding how catalyst active sites dynamically evolve at the atomic scale under reaction conditions is a prerequisite for accurate determination of catalytic mechanisms and predictably developing catalysts. We combine in-situ observation and Machine Learning accelerated first-principle atomistic simulations to uncover the mechanism of restructuring for Pt catalysts under a pressure of carbon monoxide CO. We show that a high CO coverage at a Pt step edge triggers the formation of atomic protrusions of low-coordination Pt atoms, that then detach from the step edge to create sub-nano-islands on the terraces, where undercoordinated sites are stabilized by the CO adsorbates. These studies open an avenue to achieve an atom-scale understanding of structural dynamics of more complex metal nanoparticles under reaction.

Content

Thumbnail image of Pt_island_CO_Final.pdf

Supplementary material

Thumbnail image of Pt_island_CO_SI_chemrxiv.pdf
Supporting Materials for Atomic scale mechanism of Pt catalyst restructuring under a pressure of gas
This PDF file includes: Methods Figs. S1 to S20 Tables S1 to S7

Supplementary weblinks

Computational Data
All the structural data (trajectory files including the positions, energy, and force information) used for training & validation of HDNNP and the data generated from Basin Hopping simulations have been included. Contains the files to utilize the n2p2-Neural Network Potential. Contrains the NEB trajectories and python script used to generate Fig. 5 and S20.