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Liu X, Zhang Y, Wang W, Chen Y, Xiao W, Liu T, Zhong Z, Luo Z, Ding Z, Zhang Z. Transition Metal and N Doping on AlP Monolayers for Bifunctional Oxygen Electrocatalysts: Density Functional Theory Study Assisted by Machine Learning Description. ACS Appl Mater Interfaces 2022; 14:1249-1259. [PMID: 34941239 DOI: 10.1021/acsami.1c22309] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
It is vital to search for highly efficient bifunctional oxygen evolution/reduction reaction (OER/ORR) electrocatalysts for sustainable and renewable clean energy. Herein, we propose a single transition-metal (TM)-based defective AlP system to validate bifunctional oxygen electrocatalysis by using the density functional theory (DFT) method. We found that the catalytic activity is enhanced by substituting two P atoms with two N atoms in the Al vacancy of the TM-anchored AlP monolayer. Specifically, the overpotential of OER(ORR) in Co- and Ni-based defective AlP systems is found to be 0.38 (0.25 V) and 0.23 V (0.39 V), respectively, showing excellent bifunctional catalytic performance. The results are further presented by establishing the volcano plots and contour maps according to the scaling relation of the Gibbs free-energy change of *OH, *O, and *OOH intermediates. The d-band center and the product of the number of d-orbital electrons and electronegativity of the TM atom are the ideal descriptors for this system. To investigate the activity origin of the OER/ORR process, we performed the machine learning (ML) algorithm. The result indicates that the number of TM-d electrons (Ne), the radius of TM atoms (rd), and the charge transfer of TM atoms (Qe) are the three primary descriptors characterizing the adsorption behavior. Our results can provide a theoretical guidance for designing highly efficient bifunctional electrocatalysts and pave a way for the DFT-ML hybrid method in catalysis research.
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Affiliation(s)
- Xuefei Liu
- School of Physical and Electronic Sciences, Guizhou Normal University, Guiyang 550025, China
| | - Yuefei Zhang
- School of Physical and Electronic Sciences, Guizhou Normal University, Guiyang 550025, China
| | - Wentao Wang
- Guizhou Provincial Key Laboratory of Computational Nano-Material Science, Guizhou Education University, Guiyang 550018, China
| | - Yuanzheng Chen
- School of Physical Science and Technology, Key Laboratory of Advanced Technologies of Materials, Ministry of Education of China, Southwest Jiaotong University, Chengdu 610031, China
| | - Wenjun Xiao
- School of Physical and Electronic Sciences, Guizhou Normal University, Guiyang 550025, China
| | - Tianyun Liu
- School of Physical and Electronic Sciences, Guizhou Normal University, Guiyang 550025, China
| | - Zhen Zhong
- School of Physical and Electronic Sciences, Guizhou Normal University, Guiyang 550025, China
| | - Zijiang Luo
- | College of Information, Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Zhao Ding
- College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
| | - Zhaofu Zhang
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
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