(Invited) Strategies to Improve the Stability of Fe/N/C Catalysts in PEM Fuel Cells
Bibliographic record
Abstract
Proton-exchange membrane (PEM) fuel cells hold promising applications in transportation and stationary, however, their widespread commercialization is greatly hindered by the high cost. Platinum (Pt) represents one of the largest cost components of a fuel cell, therefore, my research interests have been focusing on strategies that will increase the activity and utilization of platinum group metal (PGM) catalysts, and improve the performance and stability, as well as prolong the lifetime of the PGM-free catalysts. In this talk, I will first briefly present our work on developing low-PGM catalysts (various unique nanostructured Pt nanowires, nanotubes, and single atoms) to significantly increase the activity and stability of the Pt-based catalysts for ORR in fuel cells. Then, I will mainly focus on our work on PGM-free catalysts. Based on the major breakthroughs on Fe/N/C catalyst achieved by the Dodelet team at INRS (with the MEA activity and performance approaching that of Pt catalyst) [1,2], in collaboration with Ballard Power Systems and Toyota, we have made important progress on improving the stability of PGM-free catalyst [3-17]. Specifically in the following aspects: (i) identifying the active sites, (ii) understanding the fuel cell degradation mechanisms experimentally and theoretically, (iii) developing approaches to improving the stability of the Fe/N/C catalyst, such as pore size control, and fluorination, and (iv) catalyst layer and electrode optimization, such as catalyst hydrophobicity adjustment, Fe/N/C and ultra-low loading Pt/C hybrid catalyst. References: M. Lefèvre, E. Proietti, F. Jaouen, J.P. Dodelet, Science, 324, 71 (2009). E. Proietti, F. Jaouen, J.P. Dodelet, et al, Nature Communications, 2, 416 (2011) G. Zhang, S. Sun, J.P. Dodelet, et al, Nano Energy, 29, 111,125 (2016). R. Chenitz, G. Zhang, J.P. Dodelet, et. al, Energy Environmental Science, 11, 365 (2018). G. Zhang, S. Sun, J.P. Dodelet, et. al, Energy Environmental Science, 12, 3015 (2019). J.P. Dodelet, G. Zhang, S. Sun et. al, Energy Environmental Science, 14, 1034 (2021). V.P. Glibin, Cherif M, Vidal F, et al. J. of The Electrochemical Society. 166, F3277 (2019). V.P. Glibin, G. Zhang, J.P. Dodelet. J. of The Electrochemical Society. 168, 094502 (2021). V.P. Glibin, G. Zhang, J.P. Dodelet. SusMat. 2, 731 (2022). X. Yang, G. Zhang, S. Sun, et al, ACS Applied Materials & Interfaces., 12, 13739 (2020). X. Yang, G. Zhang, S. Sun, et al, Applied Catalysis B: Environmental., 264, 118523 (2020). Q. Wei, G. Zhang, S. Sun, et. al, Applied Catalysis B: Environmental., 237, 85 (2018). Q. Wei, G. Zhang, S. Sun, et. al, Nano Energy, 62, 700 (2019). G. Zhang, S. Sun, Nature Sustainability, 2023, accepted. X. Tong, G. Zhang, S. Sun, et al, ACS Applied Materials & Interfaces, 13, 30512 (2021). N. Komba, G. Zhang, S. Sun, et. al, Applied Catalysis B: Environmental. 243, 373 (2019). G. Zhang, S. Sun, et al, Advanced Energy Materials, 10, 2000075 (2020).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".