Han, Y.; Xu, H.; Li, Q.; Du, A.; Yan, X. DFT-Assisted Low-Dimensional Carbon-based Electrocatalysts Design and Mechanism Study: A Review. Preprints2022, 2022110444. https://doi.org/10.20944/preprints202211.0444.v1
APA Style
Han, Y., Xu, H., Li, Q., Du, A., & Yan, X. (2022). DFT-Assisted Low-Dimensional Carbon-based Electrocatalysts Design and Mechanism Study: A Review. Preprints. https://doi.org/10.20944/preprints202211.0444.v1
Chicago/Turabian Style
Han, Y., Aijun Du and Xuecheng Yan. 2022 "DFT-Assisted Low-Dimensional Carbon-based Electrocatalysts Design and Mechanism Study: A Review" Preprints. https://doi.org/10.20944/preprints202211.0444.v1
Abstract
Low-dimensional carbon-based (LDC) materials have attracted extensive research attentions in electrocatalysis because of their unique advantages such as structural diversity, low cost, and chemical tolerance. They have been widely used in a broad range of electrochemical reactions to relief environmental pollution and energy crisis. Typical examples include hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR). Traditional “trial and error” strategies seriously slowed down the rational design of electrocatalysts for these important applications. Recent studies show that the combination of density functional theory (DFT) calculations and experimental research is capable of accurately predicting the structures of electrocatalysts, thus could reveal the catalytic mechanisms. Herein, current well-recognized collaboration methods of theory and practice are reviewed. The history of modern DFT, commonly used calculation methods, and basic functionals are briefly summarized. Special attention is paid to descriptors that are widely accepted as a bridge links the structure and activity, and the breakthroughs for high-volume accurate prediction of electrocatalysts. Importantly, correlating multiple descriptors are used to systematically describe the complicated interfacial electrocatalytic processes of LDC catalysts. In addition, machine learning and high-throughput simulations are crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. This review will guide the further development of LDC electrocatalysts for extended applications from the aspect of DFT computations.
Keywords
Density functional theory; Descriptor; Carbon-based materials; Electrocatalysis; Low dimension
Subject
Chemistry and Materials Science, Electrochemistry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
