A non-metal doped VTe<sub>2</sub> monolayer: theoretical insights into the enhanced mechanism for the hydrogen evolution reaction

Two-dimensional transition metal dichalcogenides (TMDCs), such as vanadium ditelluride (VTe<sub>2</sub>), have emerged as promising catalysts for the hydrogen evolution reaction (HER) due to their unique layered structures and remarkable electronic properties. However, the catalytic performance of pristine VTe<sub>2</sub> remains inferior to that of noble metals. In this study, density functional theory (DFT) calculations were employed to systematically investigate the influence of fourteen different non-metal dopants on the HER activity of VTe<sub>2</sub>. Our results disclose that N-VTe<sub>2</sub>, P-VTe<sub>2</sub> and As-VTe<sub>2</sub> possess exceptional catalytic properties for the HER with the Gibbs free energy of hydrogen adsorption (Δ<i>G</i><sub>H*</sub>) values of 0.031, -0.032 and 0.024 eV, respectively. Furthermore, analyses of the geometric and electronic structures reveal that non-metal doping induces localized geometric distortions and charge redistribution, thereby altering the electronic environment of active sites and enhancing catalytic performance. More importantly, a composite descriptor <i>φ</i>, integrating the bond length between doped non-metal atoms and neighboring V atoms (<i>L</i><sub>NM-M</sub>) and the pz band center (<i>ε</i><sub>pz</sub>) of the doped atoms, demonstrates a strong correlation with Δ<i>G</i><sub>H*</sub> and may serve as an effective predictor of HER activity. These findings shed light on non-metal doping as an effective strategy for developing efficient, non-noble metal HER catalysts based on TMDCs.

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Journal
Physical Chemistry Chemical Physics
Published
2025-01-01
DOI
https://doi.org/10.1039/d5cp00670h
Citations
6
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.11
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article

A non-metal doped VTe2 monolayer: theoretical insights into the enhanced mechanism for the hydrogen evolution reaction

Jisong Hu, Ling Fang, Yang Xu, Guang‐Xia Shi et al.
6 citations
Physical Chemistry Chemical Physics
Machine Learning in Materials Science
3.11
article

A non-metal doped VTe2 monolayer: theoretical insights into the enhanced mechanism for the hydrogen evolution reaction

Jisong Hu, Ling Fang, Yang Xu, Guang‐Xia Shi, Guofeng Li, Ying Zhang, Lei Zhang, Yanwei Wang, Ge Gao
article en
6 citations

Abstract

Two-dimensional transition metal dichalcogenides (TMDCs), such as vanadium ditelluride (VTe<sub>2</sub>), have emerged as promising catalysts for the hydrogen evolution reaction (HER) due to their unique layered structures and remarkable electronic properties. However, the catalytic performance of pristine VTe<sub>2</sub> remains inferior to that of noble metals. In this study, density functional theory (DFT) calculations were employed to systematically investigate the influence of fourteen different non-metal dopants on the HER activity of VTe<sub>2</sub>. Our results disclose that N-VTe<sub>2</sub>, P-VTe<sub>2</sub> and As-VTe<sub>2</sub> possess exceptional catalytic properties for the HER with the Gibbs free energy of hydrogen adsorption (Δ<i>G</i><sub>H*</sub>) values of 0.031, -0.032 and 0.024 eV, respectively. Furthermore, analyses of the geometric and electronic structures reveal that non-metal doping induces localized geometric distortions and charge redistribution, thereby altering the electronic environment of active sites and enhancing catalytic performance. More importantly, a composite descriptor <i>φ</i>, integrating the bond length between doped non-metal atoms and neighboring V atoms (<i>L</i><sub>NM-M</sub>) and the pz band center (<i>ε</i><sub>pz</sub>) of the doped atoms, demonstrates a strong correlation with Δ<i>G</i><sub>H*</sub> and may serve as an effective predictor of HER activity. These findings shed light on non-metal doping as an effective strategy for developing efficient, non-noble metal HER catalysts based on TMDCs.

Physical Chemistry Chemical PhysicsVol. 27(19)
Jiangsu Normal University (CN), Xuzhou University of Technology (CN), Xinjiang Industry Technical College (CN), Chongqing Institute of Green and Intelligent Technology (CN), Xuzhou College of Industrial Technology, Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 9%
Machine Learning in Materials Science
3.11
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