Highly Active Porous Carbon-Supported Nonprecious Metal−N Electrocatalyst for Oxygen Reduction Reaction in PEM Fuel Cells
Bibliographic record
Abstract
As an alternative for platinum to reduce the cost, nonprecious catalysts for the oxygen reduction reaction (ORR) were synthesized by deposition of an Fe/Co−N x composite onto nanoporous carbon black with ethylenediamine (EDA) as a nitrogen precursor. Two different nanoporous carbon supports, Ketjen Black EC300J (KJ300) and EC600JD (KJ600), were used as catalyst supports for the nonprecious catalysts. Rotating ring disk electrode measurements were carried out to investigate the ORR activity and selectivity of these catalysts. The results obtained from the optimized FeCo/EDA-carbon catalyst, using KJ600 as the support, showed improved onset and half-wave potentials and superior selectivity than that of the KJ300. Similarly, the catalyst showed good performance in the hydrogen−oxygen PEM fuel cell. At a cell voltage of 0.6 V, the fuel cell managed to produce 0.37 A/cm 2 with a maximum power density of 0.44 W/cm 2 . A fuel cell life test at a constant voltage of 0.40 V demonstrated promising stability up to 100 h. The catalysts were characterized by X-ray diffraction, energy-dispersive X-ray spectrometry, and X-ray photoelectron spectroscopy. The characterizations indicated that pyridinic-type nitrogen of the nonprecious metal catalysts is critical for ORR catalytic activity and selectivity. These results suggest that a higher pore volume and surface area of the carbon support could lead to a higher nitrogen content, providing more active sites for ORR, and this type of catalyst has great potential used as a nonprecious PEM fuel cell catalyst.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".