Stable dioxin-linked metallophthalocyanine covalent organic frameworks as trifunctional electrocatalysts for overall water splitting and oxygen reduction: A combining density functional theory and machine learning study
Lu, Song Ding, Yi Ying, Jiadi Liu, Tiancun Li, Qing Wu, Yong Zhao, Yafei Yu, Zhixin
Lu, Song
Ding, Yi
Ying, Jiadi
Liu, Tiancun
Li, Qing
Wu, Yong
Zhao, Yafei
Yu, Zhixin
Author
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Finding efficient electrocatalysts used in the hydrogen evolution reaction (HER), oxygen evolution and reduction reactions (OER/ORR) is significant. Herein, density functional theory (DFT) calculations were employed to prove the viability of a series of transition metal (TM) atoms anchored dioxin-linked metallophthalocyanine (Pc-TFPN) for HER, OER and ORR. V and CoPc-TFPN monolayers display moderate binding strength for *H adsorption, with ΔG<inf>*H</inf> of 0.01 and −0.01 eV, respectively. CoPc-TFPN follows Volmer-Heyrovsky mechanism for hydrogen production with energy barrier of 0.40 eV. Co, Rh, and IrPc-TFPN were identified as promising bifunctional OER/ORR catalyst with overpotentials of 0.18/0.36 V, 0.44/0.24 V, and 0.33/0.36 V. Machine learning (ML) study was employed to uncover the underlying correlation between activity and atomic features. Besides the d band center, the number of electrons in the d-orbital plays a crucial role in determining the activity. This work provides guidance for designing single-atom catalysts based on covalent organic frameworks.
Citation
S. Lu et al., “Stable dioxin-linked metallophthalocyanine covalent organic frameworks as trifunctional electrocatalysts for overall water splitting and oxygen reduction: A combining density functional theory and machine learning study,” Int J Hydrogen Energy, vol. 187, p. 152132, Nov. 2025, doi: 10.1016/J.IJHYDENE.2025.152132
Source
International Journal of Hydrogen Energy
Conference
Keywords
DFT calculations, HER/OER/ORR activity, Machine learning, Single-atom catalysts, Trifunctional catalyst
Subjects
Source
Publisher
Elsevier