Revision of the oxygen reduction reaction on N-doped graphenes by grand-canonical DFT

文献信息

发布日期 2023-11-23
DOI 10.1039/D3CP04517J
影响因子 3.676
作者

Sergey V. Pavlov, Victoria A. Nikitina, Sergey A. Kislenko


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摘要

Nitrogen-doped graphenes were among the first promising metal-free carbon-based catalysts for the oxygen reduction reaction (ORR). However, data on the most efficient catalytic centers and their catalytic mechanisms are still under debate. In this work, we study the associative mechanism of the ORR in an alkaline medium on graphene containing various types of nitrogen doping. The free energy profile of the reaction is constructed using grand-canonical DFT at a constant electrode potential in combination with an implicit electrolyte model. It is shown that the reaction mechanism differs from the generally accepted one and depends on the surface potential and doping type. In particular, as the potential decreases, coupled electron–proton transfer changes to sequential electron and proton transfer, and the potential at which this occurs depends on the doping type. It has been shown that oxygen chemisorption is the limiting step. The electrocatalytic mechanism of the nitrogen dopants involves reducing the oxygen chemisorption energy. Calculations predict that, at different potentials, different types of nitrogen impurities most effectively catalyze the ORR.

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Front/Back Matter

DOI: 10.1039/C8AN90043D

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来源期刊

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
自引率: 10.3%
年发文量: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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