Issue 7, 2024

Elucidating the impact of oxygen functional groups on the catalytic activity of M–N4–C catalysts for the oxygen reduction reaction: a density functional theory and machine learning approach

Abstract

Efforts to enhance the efficiency of electrocatalysts for the oxygen reduction reaction (ORR) in energy conversion and storage devices present formidable challenges. In this endeavor, M–N4–C single-atom catalysts (MN4) have emerged as promising candidates due to their precise atomic structure and adaptable electronic properties. However, MN4 catalysts inherently introduce oxygen functional groups (OGs), intricately influencing the catalytic process and complicating the identification of active sites. This study employs advanced density functional theory (DFT) calculations to investigate the profound influence of OGs on ORR catalysis within MN4 catalysts (referred to as OGs@MN4, where M represents Fe or Co). We established the following activity order for the 2eORR: for OGs@CoN4: OH@CoN4 > CoN4 > CHO@CoN4 > C–O–C@CoN4 > COC@CoN4 > COOH@CoN4 > C[double bond, length as m-dash]O@CoN4; for OGs@FeN4: COC@FeN4 > C[double bond, length as m-dash]O@FeN4 > OH@FeN4 > FeN4 > COOH@FeN4 > CHO@FeN4 > C–O–C@FeN4. Multiple oxygen combinations were constructed and found to be the true origin of MN4 activity (for instance, the overpotential of 2OH@CoN4 as low as 0.07 V). Furthermore, we explored the performance of the OGs@MN4 system through charge and d-band center analysis, revealing the limitations of previous electron-withdrawing/donating strategies. Machine learning analysis, including GBR, GPR, and LINER models, effectively guides the prediction of catalyst performance (with an R2 value of 0.93 for predicting ΔG*OOH_vac in the GBR model). The Eg descriptor was identified as the primary factor characterizing ΔG*OOH_vac (accounting for 62.8%; OGs@CoN4: R2 = 0.9077, OGs@FeN4: R2 = 0.7781). This study unveils the significant impact of OGs on MN4 catalysts and pioneers design and synthesis criteria rooted in Eg. These innovative findings provide valuable insights into understanding the origins of catalytic activity and guiding the design of carbon-based single-atom catalysts, appealing to a broad audience interested in energy conversion technologies and materials science.

Graphical abstract: Elucidating the impact of oxygen functional groups on the catalytic activity of M–N4–C catalysts for the oxygen reduction reaction: a density functional theory and machine learning approach

  • This article is part of the themed collection: #MyFirstMH

Supplementary files

Article information

Article type
Communication
Submitted
10 Dec 2023
Accepted
12 Jan 2024
First published
18 Jan 2024

Mater. Horiz., 2024,11, 1719-1731

Elucidating the impact of oxygen functional groups on the catalytic activity of M–N4–C catalysts for the oxygen reduction reaction: a density functional theory and machine learning approach

L. Xie, W. Zhou, Y. Huang, Z. Qu, L. Li, C. Yang, Y. Ding, J. Li, X. Meng, F. Sun, J. Gao, G. Zhao and Y. Qin, Mater. Horiz., 2024, 11, 1719 DOI: 10.1039/D3MH02115G

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