Fuel cells act as highly sustainable energy conversion devices that exhibit tremendous potential for the global transition toward clean energy systems. However, their widespread deployment is currently limited by the sluggish kinetics of the oxygen reduction reaction (ORR) at the cathode. While platinum-based (Pt/C) materials are highly efficient at accelerating the ORR, their high cost and susceptibility to poisoning severely limit large-scale commercial utilization.
To overcome these barriers, a collaborative research team from Fuzhou University , Qingyuan Innovation Laboratory , and the University of Science and Technology of China has published a groundbreaking study in ENGINEERING Energy . The researchers utilized an innovative combination of density functional theory (DFT) and machine learning (ML) to systematically investigate Fe-N-C single-atom catalysts, which serve as earth-abundant, highly promising alternatives to traditional platinum-based catalysts.
Historically, identifying optimal modification strategies to enhance the activity and stability of Fe-N-C catalysts through traditional trial-and-error procedures has been immensely challenging due to the vast combinatorial space of possible heteroatom types and doping sites. By creating 158 modified Fe-N-C catalyst models, the team thoroughly explored a "dual modulation" strategy that incorporates both in-plane heteroatom doping and axial coordination decoration.
This research successfully establishes a unified mechanistic and data-driven framework that will significantly accelerate the design of high-performance electrocatalysts.
Key Research Highlights and Findings:
This synergistic application of computational chemistry and artificial intelligence precisely unravels the complex interactions within dual-modified single-atom catalysts, paving the way for the development of cheaper, more efficient hydrogen fuel cells.
Journal : ENGINEERING Energy
Read the full article for free : https://rdcu.be/frN5f
Cite this article : Yang, Z., Wu, Q., Zhang, H. et al. Dual modulation of Fe–N–C catalysts via axial and in-plane heteroatoms for oxygen reduction: A combined DFT and machine learning study. ENG. Energy 20, 10740 (2026). https://doi.org/10.1007/s11708-026-1074-0
ENGINEERING Energy
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Dual modulation of Fe–N–C catalysts via axial and in-plane heteroatoms for oxygen reduction: A combined DFT and machine learning study
15-Jun-2026