A General Carboxylate‐Assisted Approach to Boost the ORR Performance of ZIF‐Derived Fe/N/C Catalysts for Proton Exchange Membrane Fuel Cells
Bibliographic record
Abstract
Abstract An Fe/N/C catalyst derived from the pyrolysis of metal–organic frameworks, for example, a zeolitic‐imidazolate‐framework‐8 (ZIF‐8), has been regarded as one of the most promising non‐precious metal catalysts toward oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFCs). However, its ORR mass activity is still much inferior to that of Pt, partly because of the lack of general and efficient synthetic strategies. Herein, a general carboxylate‐assisted strategy that dramatically enhances the ORR mass activity of ZIF‐derived Fe/N/C catalysts is reported. The carboxylate is found to promote the formation of Fe/N/C catalysts with denser accessible active sites and entangled carbon nanotubes, as well as a higher mesoporosity. These structural advantages make the carboxylate‐assisted Fe/N/C catalysts show a 2–10 fold higher ORR mass activity than the common carboxylate‐free one in various cases. When applied in H 2 –O 2 PEMFCs, the active acetate‐assisted Fe/N/C catalyst generates a peak power density of 1.33 W cm −2 , a new record of peak power density for a H 2 –O 2 PEMFC with non‐Pt ORR 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) | 0.001 | 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".