Issue 31, 2021

Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C2N by DFT and machine learning

Abstract

Designing high-performance bifunctional oxygen evolution/reduction reaction (OER/ORR) catalysts is a newly emerging topic and these catalysts have wide applications in metal–air batteries and fuel cells. Herein, we report a group of (27) single-atom catalysts (SACs) supported on the C2N monolayer as promising bifunctional OER/ORR catalysts by theoretical calculations. In particular, Rh@C2N exhibits a lower OER overpotential (0.37 V) than the IrO2(110) benchmark with good ORR activity, while Au and Pd@C2N are superior ORR catalysts (with an overpotential of 0.38 and 0.40 V) to Pt(111) and their OER performance is also outstanding. More importantly, we discover the origin of the bifunctional catalytic activity by density functional theory (DFT) calculations and machine learning (ML). Using DFT, we find a volcano-shaped relationship between the catalytic activity and ΔGO, and finally link them to the normalized Fermi abundance, a parameter based on the electronic structure analysis. We further unravel the origin of element-specific activity by ML modelling based on the random forest algorithm that considers the outer electron number and oxide formation enthalpy as the two most important factors, and our model can give an accurate prediction of ΔGO with much reduced time and cost. This work not only paves the way for understanding the origin of bifunctional OER/ORR activity of SACs, but also benefits the rational design of novel SACs for other catalytic reactions by combining DFT and ML.

Graphical abstract: Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C2N by DFT and machine learning

Supplementary files

Article information

Article type
Paper
Submitted
08 شوال 1442
Accepted
25 ذو القعدة 1442
First published
25 ذو القعدة 1442

J. Mater. Chem. A, 2021,9, 16860-16867

Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C2N by DFT and machine learning

Y. Ying, K. Fan, X. Luo, J. Qiao and H. Huang, J. Mater. Chem. A, 2021, 9, 16860 DOI: 10.1039/D1TA04256D

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements