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

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^-2, with a discharge capacity of 806.5 mAh g_Zn^-1 at 10 mA cm^-2, 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.