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Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H₂ Activation Using Machine Learning-Accelerated First-Principles Simulations.

ABSTRACT: Oxygen vacancies (OVs) play important roles on any oxide catalysts. In this work, using an investigation of the OV effects on ZnO(101̅0) for CO and H₂ activation as an example, we demonstrate, via machine learning potentials (MLPs), genetic algorithm (GA)-based global optimization, and density functional theory (DFT) validations, that the ZnO(101̅0) surface with 0.33 ML OVs is the most likely surface configuration under experimental conditions (673 K and 2.5 MPa syngas (H₂:CO = 1.5)). It is found that a surface reconstruction from the wurtzite structure to a body-centered-tetragonal one would occur in the presence of OVs. We show that the OVs create a Zn₃ cluster site, allowing H₂ homolysis and C-O bond cleavage to occur. Furthermore, the activit

SUBMITTER: Han Y

PROVIDER: S-EPMC10127212 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H₂ Activation Using Machine Learning-Accelerated First-Principles Simulations.

OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H2 Activation Using Machine Learning-Accelerated First-Principles Simulations.

OmicsDI is part of the ELIXIR infrastructure

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Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H₂ Activation Using Machine Learning-Accelerated First-Principles Simulations.