Mechanistic Evaluation of Degradation Pathway Attribution at Coupled Fe-N4/Sn-Nx Oxygen-Reduction Sites

Yvonne Choi; Xinyi Yan

Authors

Yvonne Choi School of Energy and Power Engineering, Shandong University, Jinan, Shandong, China Author
Xinyi Yan School of Energy and Power Engineering, Shandong University, Jinan, Shandong, China Author

Keywords:

Oxygen Reduction Reaction, Single-Atom Catalysts, Degradation Pathways, Coupled Sites

Abstract

The commercial viability of proton exchange membrane fuel cells relies heavily on the development of highly active and durable platinum group metal-free catalysts for the oxygen reduction reaction. Among these, transition metal-nitrogen-carbon materials, particularly those featuring atomically dispersed iron sites coordinated by nitrogen, have emerged as the most promising candidates. However, severe performance degradation under operational conditions remains a critical bottleneck. Recent advancements have introduced heteronuclear dual-atom catalysts, such as coupled Fe-N4 and Sn-Nx sites, which exhibit enhanced intrinsic activity and improved stability due to synergistic electronic interactions. This paper provides a comprehensive investigation into the degradation pathway attribution at these coupled catalytic sites. By decoupling the complex degradation mechanisms, which include metal center demetallation, carbon support oxidation, and micropore flooding, this study delineates the specific protective role of the secondary tin site. The electronic modulation induced by the tin atom alters the d-band center of the iron active site, thereby optimizing the binding energy of oxygen intermediates and concurrently increasing the thermodynamic barrier for iron dissolution. Through a detailed analysis of theoretical models and accelerated stress test methodologies, the complex interplay between structural evolution and electrochemical performance decay is elucidated. The findings offer profound insights into the rational design of durable heteronuclear single-atom catalysts for advanced electrochemical energy conversion systems.

References

1. Song, S., Huang, D., Deng, D., Xiong, H., Tang, Y., Zhao, Y., & Qin, R. (2026). Olbedo: An Albedo and Shading Aerial Dataset for Large-Scale Outdoor Environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6474-6483).

2. Tian, F., Gao, X., Wu, C., Zhang, L., Xia, X., & Wang, X. (2017). Initial energy supplementation in critically ill patients receiving enteral nutrition: a systematic review and meta-analysis of randomized controlled trials. Asia Pacific Journal of Clinical Nutrition, 26(1), 11-19.

3. Yang, Z., Hu, D., Guo, Q., Zuo, L., & Ji, W. (2023). Visual E 2 C: AI-driven visual end-edge-cloud architecture for 6G in low-carbon smart cities. IEEE Wireless Communications, 30(3), 204-210.

4. Lin, Y., Yao, Y., Zhu, J., & He, C. (2025, July). Application of generative AI in predictive analysis of urban energy distribution and traffic congestion in smart cities. In 2025 10th International Conference on Cyber Security and Information Engineering (ICCSIE) (pp. 620-623). IEEE.

5. Peng, Y., Li, H., BouDagher-Fadel, M., Wang, L., Zhang, D., Zheng, T., & Yang, K. (2022). Benthic foraminifera distribution and sedimentary environmental evolution of a carbonate platform: A case study of the Guadalupian (middle Permian) in eastern Sichuan Basin. Marine Micropaleontology, 170, 102079.

6. Peng, Y., & Li, H. (2023). Benthic foraminiferal morphogroups at the end-Guadalupian extinction in eastern Sichuan Basin, China. Palaeogeography, Palaeoclimatology, Palaeoecology, 618, 111522.

7. Li, H., Zheng, T., Peng, Y., & BouDagher-Fadel, M. K. (2023). The fusulinids in a middle-late Permian carbonate platform (from Maokou to Wuchiapingian stage) in eastern Sichuan Basin, China. In Stratigraphy & Timescales (Vol. 8, pp. 413-435). Academic Press.

8. Yin, L., Ghosh, R., Lin, C., Hale, D., Weigl, C., Obarowski, J., ... & Jin, Z. (2023). Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin. Remote Sensing of Environment, 295, 113695.

9. Wang, Y., Lou, M., Luo, Y., Wan, Z., Wang, L., & Tang, Q. (2026). Contact dynamics characteristics of a deepwater drilling riser-drillstring coupled system. Ocean Engineering, 366, 127458.

10. Meng, L., Zhang, B., & Cao, L. (2026). Establishing linear cultural heritage corridors by integrating cultural and ecological values: a case study of the Jinzhong section of the Great Tea Road. Land, 15(2), 293.

11. Liu, Z., Ye, Z., & Zhang, K. (2025). Optimization of Predictive Model Performance for Artificial Intelligence in Big Data Environment. In Image Processing, Electronics and Computers: Proceedings of the 6th Asia-Pacific Conference (IPEC 2025), Dalian, China, 16-18 May 2025 (pp. 1870-1878). 1 Oliver's Yard, 55 City Road, London, EC1Y 1SP: SAGE Publications.

12. Wang, H., Wei, W., Wang, K., Kong, H., Vieira, C. L. Z., Koutrakis, P.,... & Giorio, C. (2025). A global multidimensional analysis of air pollution in land, sea, and air transport cabins reveals substantial health risks. One Earth, 8 (1).

13. Tian, Y. (2026, January). A Data-Driven Energy Efficiency Improvement and Cost Optimisation Model for New Energy Electric Vehicles. In 2026 IEEE International Conference on Power, Electronics and Green Energy (ICPEGE) (pp. 304-308). IEEE.

14. Fan, Y., Hu, C., Wang, Z., Wang, H., Zhang, R., Jiang, D., ... & Koutrakis, P. (2025). Residential VOC from building materials: Exposures, health risks, and ambient hazards.Sustainable Cities and Society,119, 106080.

15. Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., Roy, D. P., & Morisette, J. T. (2002). An overview of MODIS land data processing and product status. Remote Sensing of Environment, 83(1–2), 3–15.

16. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.

17. Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., et al. (2007). The shuttle radar topography mission. Reviews of Geophysics, 45(2), RG2004.

18. Li, Q., Ye, Q., Zhang, N., Zhang, W., & Hu, F. (2025). Digital-twin-enabled industrial IoT: Vision, framework, and future directions. IEEE Wireless Communications, 32(6), 173-181.

19. Pei, Z., Huang, Q., & Wang, S. (2026). When LLMs develop languages: Symbolic communication for efficient multi-agent reasoning. arXiv preprint arXiv:2606.29354.

20. Yang, Z., Ji, W., Guo, Q., & Wang, Z. (2023, October). Javp: Joint-aware video processing with edge-cloud collaboration for dnn inference. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 9152-9160).

21. Prescribed-time tracking control of MIMO nonlinear systems with nonvanishing uncertainties

22. Zou, R., Zhang, Y., Zhao, M., Tan, Z., Zhang, Y., & Cheung, Y. M. (2025). SDENK: Unbiased Subspace Density--Clustering. Neurocomputing, 131225.