Challenging the Activity-Durability Tradeoff of Fe-N-C Fuel Cell Catalysts via Controlling thermal Activation Atmosphere
Bibliographic record
Abstract
Fe-N-C catalysts, the most promising platinum group metal (PGM)-free oxygen-reduction catalysts, often simultaneously contain pyrrolic N- (S1) and pyridinic N (S2) -coordinated FeN4 sites. These two types of active sites show significantly different intrinsic activity and stability. S1 sites are more active but less stable compared to S2 sites. Designing a Fe-N-C catalyst, which exclusively contains active S1 sites with enhanced intrinsic stability, is highly desirable to break the activity-stability trade-off. Herein, we report a Fe-N-C model catalyst that solely comprises S1 sites prepared by adding H2 in the pyrolysis atmosphere (i.e., 10% H2/Ar). A membrane electrode assembly (MEA) with the Fe-N-C cathode demonstrated compelling activity and generated a current density of 50.8 mA cm−2 at 0.9 ViR-free (H2-O2) and 211 mA cm−2 at 0.8 V (H2-air), which have significantly exceeded the U.S. DOE 2025 targets. The highly active Fe-N-C catalyst also demonstrated improved stability during life tests and accelerated stability tests (ASTs). The knowledge obtained from experimental and theoretical results elucidates that the FeN4 site formation process can be controlled by thermal activation atmospheres, which is essential to breaking activity-stability trade-off and design viable Fe-N-C catalysts with adequate activity and stability for proton exchange membrane fuel cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".