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DFT Study on the Hydrogen Evolution Reaction for Different Facets of Co 2 P
Author(s) -
Liang Zhun,
Zhong Xiaoliang,
Li Tianqi,
Chen Ming,
Feng Guang
Publication year - 2019
Publication title -
chemelectrochem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.182
H-Index - 59
ISSN - 2196-0216
DOI - 10.1002/celc.201800601
Subject(s) - facet (psychology) , density functional theory , catalysis , gibbs free energy , hydrogen , linear regression , stability (learning theory) , chemistry , materials science , computational chemistry , thermodynamics , mathematics , computer science , physics , statistics , organic chemistry , psychology , social psychology , biochemistry , personality , machine learning , big five personality traits
Co 2 P is one of the most promising non‐noble metal catalysts for hydrogen evolution reaction (HER) during water splitting, owing to its many advantages, such as earth‐abundance, high activity and good stability. The as‐synthesized Co 2 P is multi‐faceted while its facet‐dependent HER activity is still little known. To explore the facet‐dependent HER activity of Co 2 P, density functional theory (DFT) calculations are conducted for five different facets of Co 2 P: (001), (010), (101), (112) and (113) in this work. By comparing the surface energy and Gibbs free energy of H* (ΔG H* ) of these five facets, Co 2 P (113) is found to possess both good surface stability and high HER activity, which could be the guidance for catalyst design and synthesis. Multiple linear regression has been adopted to quantitatively scrutinize the relationship between H−Co bond length distribution and ΔG H* , and the mean absolute error of the multiple linear regression is as small as 0.0756 eV. This analysis shows that statistical methods are effective to explore the effect of structure on the HER activity, which could be helpful in understanding the underlying HER mechanism.