JOURNAL ARTICLE

First-principles study of CO2 hydrogenation on Cd-doped ZrO2: Insights into the heterolytic dissociation of H2.

  • Published In: Journal of Chemical Physics, 2023, v. 159, n. 21. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zeng, Yabing; Yu, Jie; Li, Yi; Zhang, Yongfan; Lin, Wei 3 of 3

Abstract

This article focuses on the theoretical investigation of cadmium (Cd)-doped zirconia (ZrO₂) catalysts for the selective hydrogenation of carbon dioxide (CO₂) to methanol using density functional theory (DFT) calculations. The study reveals that Cd doping significantly facilitates the formation of oxygen vacancies on the ZrO₂ surface, which enhances CO₂ adsorption and activation by creating unsaturated oxygen sites (Omob). Hydrogen (H₂) adsorption is more challenging and primarily occurs via heterolytic dissociation forming compact ion pairs such as [H_Cd–H_O]*, which lower energy barriers for subsequent hydrogenation steps. The reaction proceeds predominantly through the formate (HCOO*) pathway, with key intermediates undergoing sequential hydrogenation and isomerization steps leading to methanol formation, while competing pathways producing CO are kinetically and thermodynamically less favorable. The presence of oxygen vacancies and Cd doping modulates the electronic structure and active sites, improving catalytic activity and selectivity toward methanol, with microkinetic modeling supporting high turnover frequencies under relevant reaction conditions.

Additional Information

  • Source:Journal of Chemical Physics. 2023/12, Vol. 159, Issue 21, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2023
  • ISSN:0021-9606
  • DOI:10.1063/5.0177849
  • Accession Number:174100450
  • Copyright Statement:Copyright of Journal of Chemical Physics is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.