Tailoring FeN<sub>4</sub> Sites with Edge Enrichment for Boosted Oxygen Reduction Performance in Proton Exchange Membrane Fuel Cell
Bibliographic record
Abstract
Abstract Transition metal atoms with corresponding nitrogen coordination are widely proposed as catalytic centers for the oxygen reduction reaction (ORR) in metal–nitrogen–carbon (M–N–C) catalysts. Here, an effective strategy that can tailor Fe–N–C catalysts to simultaneously enrich the number of active sites while boosting their intrinsic activity and utilization is reported. This is achieved by edge engineering of FeN 4 sites via a simple ammonium chloride salt‐assisted approach, where a high fraction of FeN 4 sites are preferentially generated and hosted in a graphene‐like porous scaffold. Theoretical calculations reveal that the FeN 4 moieties with adjacent pore defects are likely to be more active than the nondefective configuration. Coupled with the facilitated accessibility of active sites, this prepared catalyst, when applied in a practical H 2 –air proton exchange membrane fuel cell, delivers a remarkable peak power density of 0.43 W cm −2 , ranking it as one of the most active M–N–C catalysts reported to date. This work provides a new avenue for boosting ORR activity by edge manipulation of FeN 4 sites.
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".