Breaking Atomic Fe–N 4 Symmetry in Aerogel Catalysts by Nitrogen and Chlorine Doping for Enhancing Oxygen Reduction

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Authors: Ying Yu, Tan Li, WU Sheng-qi, Peng-Fei Xie, Yi-Long Chen, Jiyoung Lee, Yubo Xing, Zhiqiang Xiao, Dong Feng, Peng Dong, Yingjie Zhang, Shichao Ding
+2 more Il-Doo Kim, Jin-Cheng Li
Cited by: 5
References: 54

Abstract

The growing demand for metal-air batteries and fuel cells has spurred extensive research into low-cost, highly efficient, noble-metal-free electrocatalysts to overcome the sluggish oxygen reduction reaction (ORR) at the cathode. Herein, we propose a chemical assembly strategy to engineer an asymmetrically structured Fe–N 4 single-atom active site densely embedded within a hierarchical micro-nanoporous aerogel. The asymmetric Fe–N 4 single-atom moiety, modulated by N and Cl codopants, enhances intrinsic ORR activity, while the porous aerogel geometry facilitates rapid electron and mass transport. As a result, the resulting catalyst demonstrates high ORR performance, achieving half-wave potentials of 0.92 V in alkaline media and 0.82 V in acidic media, in stark contrast to conventional Fe catalysts with planar coordination symmetries. When used in the H 2 –O 2 fuel cell, a peak power density of 755 mW cm –2 is achieved. Furthermore, Zn–air batteries utilizing this catalyst deliver high peak power densities of 395 mW cm –2 and 161 mW cm –2 for liquid- and solid-state batteries, respectively, while maintaining excellent stability under repeated cycles and various mechanical deformations. Complementing these experimental results, we introduced an explainable XGBoost machine-learning model to accurately predict battery power density, uncovering critical performance trends driven by voltage, catalyst atomistic architecture, and device configurations. This work not only presents a method for fabricating high-performance single-atom aerogel catalysts but also offers valuable design principles for advancing the commercial viability of electrocatalysis-based energy systems.

Fields of Study & Subjects

Electrocatalysts for Energy Conversion Machine Learning in Materials Science Advanced battery technologies research

Publication details

Journal / source
ACS Nano
Publisher
American Chemical Society (ACS)
Volume
20
Issue
19
Pages
14170–14182
Publication date
7 May 2026
Language
EN