Machine Learning-Assisted Spray Pyrolysis for the Synthesis of Single-Atom Fe-N-C Porous Hollow Microspheres for Zinc-Air Batteries.

Journal: ACS applied materials & interfaces
Published Date: Jun 10, 2025

Abstract

Non-noble metal single-atom catalysts with high catalytic activity have garnered considerable attention from researchers in recent years. Yet, their synthesis is affected by various factors, making process optimization a challenging and systematic task. In this study, single-atom Fe-N-C porous hollow microspheres were successfully synthesized via ultrasonic spray pyrolysis, with machine learning employed to optimize the fabrication process. Machine learning models, trained on pre-experimental data, identified the key factors influencing material structure and oxygen reduction reaction (ORR) performance. The resulting Fe-N-C (600-900) material demonstrated excellent ORR activity with a half-wave potential of 0.865 V, along with high stability and methanol tolerance. When applied to traditional liquid zinc-air batteries (ZABs), it achieved an open-circuit voltage of 1.56 V and a maximum power density of 313.4 mW cm, with a discharge capacity of 806.5 mAh g at 10 mA cm, outperforming commercial noble metal catalysts. This work offers valuable insights into the application of machine learning for optimizing ORR catalysts and designing high-performance materials for energy conversion devices.

Authors

  • Aojie Li
    Key Laboratory of Hunan Province for Advanced Carbon-based Functional Materials, School of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang 414006, P. R. China.
  • Qiao Jiang
    Key Laboratory of Hunan Province for Advanced Carbon-based Functional Materials, School of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang 414006, P. R. China.
  • Tianyi Suo
    Key Laboratory of Hunan Province for Advanced Carbon-based Functional Materials, School of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang 414006, P. R. China.
  • Liang Chen
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Hong Yin
    Department of Mathematics, School of Information, Renmin University of China, No.59 Zhong Guan Cun Avenue, Hai Dian District, Beijing, 100872, China. yinxiaohong82@hotmail.com.
  • Junlin Huang
    Key Laboratory of Hunan Province for Advanced Carbon-based Functional Materials, School of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang 414006, P. R. China.
  • Wen-Yuan Xu
    Key Laboratory of Hunan Province for Advanced Carbon-based Functional Materials, School of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang 414006, P. R. China.
  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Binhong He
    Key Laboratory of Hunan Province for Advanced Carbon-based Functional Materials, School of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang 414006, P. R. China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.

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