Building Atomic Scale and Dense Fe─N<sub>4</sub> Edge Sites of Highly Efficient Fe─N─C Oxygen Reduction Catalysts Using a Sacrificial Bimetallic Pyrolysis Strategy
Bibliographic record
Abstract
Abstract Replacing high‐cost and scarce platinum (Pt) with transition metal and nitrogen co‐doped carbon (M/N/C, M = Fe, Co, Mn, and so on) catalysts for the oxygen reduction reaction (ORR) in proton exchange membrane fuel cells has largely been impeded by the unsatisfactory ORR activity of M/N/C due to the low site utilization and inferior intrinsic activity of the M─N4 active center. Here, these limits are overcome by using a sacrificial bimetallic pyrolysis strategy to synthesize Fe─N─C catalyst by implanting the Cd ions in the backbone of ZIF‐8, leading to exposure of inaccessible FeN4 edge sites (that is, increasing active site density (SD)) and high fast mass transport at the catalyst layer of cathode. As a result, the final obtained Fe(Cd)─N─C catalyst has an active site density of 33.01 µmol g−1 (with 33.01% site utilization) over 5.8 times higher than that of Fe─N─C catalyst. Specially, the optimal catalyst delivers a high ORR performance with a half‐wave potential of 0.837 (vs RHE) in a 0.1 m HClO4 electrolyte, which surpasses most of Fe‐based catalysts.
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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 | 0.000 | 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".