Literature DB >> 33592080

Towards bridging the structure gap in heterogeneous catalysis: the impact of defects in dissociative chemisorption of methane on Ir surfaces.

Xueyao Zhou1, Yaolong Zhang2, Hua Guo3, Bin Jiang2.   

Abstract

A quantitative understanding of the role played by defect sites in heterogeneous catalysis is of great importance in designing new and more effective catalysts. In this work, we report a detailed dynamic study of a key step in methane steam reforming under experimentally relevant conditions on a new high-dimensional potential energy surface determined from first principles data with the aid of machine learning, with which the interactions of CH4 with both the flat Ir(111) and stepped Ir(332) surfaces are described. In particular, we argue based on our simulations that the experimentally observed "negatively activated" dissociative chemisorption of methane on Ir surfaces could be due to a combined effect of defects and high substrate temperature, which lowers the reaction barrier relative to that on terraces. Furthermore, a model based on dynamic information of trapping and reaction channels is proposed, which allows a quantitative prediction of the initial sticking probability for different defect densities, thus helping to close the so-called structure gap in heterogeneous catalysis.

Entities:  

Year:  2021        PMID: 33592080     DOI: 10.1039/d0cp06535h

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  3 in total

Review 1.  Dynamics of Heterogeneous Catalytic Processes at Operando Conditions.

Authors:  Xiangcheng Shi; Xiaoyun Lin; Ran Luo; Shican Wu; Lulu Li; Zhi-Jian Zhao; Jinlong Gong
Journal:  JACS Au       Date:  2021-11-04

2.  Accurate Simulations of the Reaction of H2 on a Curved Pt Crystal through Machine Learning.

Authors:  Nick Gerrits
Journal:  J Phys Chem Lett       Date:  2021-12-17       Impact factor: 6.475

3.  Construction of Safety Early Warning Model for Construction of Engineering Based on Convolution Neural Network.

Authors:  Changge Zhao
Journal:  Comput Intell Neurosci       Date:  2022-09-16
  3 in total

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