Fe-Based Electrocatalysts for Oxygen Reduction in PEMFCs Using Ballmilled Graphite Powder as a Carbon Support
Bibliographic record
Abstract
Active Fe-based electrocatalysts were prepared using ballmilled graphite powder as a carbon support. The best performing catalysts were achieved by acid-washing, iron-loading, and pyrolyzing the ballmilled graphite powders. Only of ballmilling was required to produce optimal catalytic activity. High-energy ballmilling of pristine graphite powder under nitrogen was shown to reduce crystallite size, increase nitrogen content, increase surface area, increase degree of disorder, and inevitably introduce metallic impurities. Acid-washing treatment of ballmilled graphite powders reduced, but did not completely eliminate, metallic impurities. Iron enrichment and pyrolysis of acid-washed, ballmilled graphite powder was shown to increase catalytic activity, have little effect on crystallite size, increase surface area, and decrease degree of disorder. It was found that catalytic activity increases as crystallite size decreases, degree of disorder and nitrogen content increase, and micropore specific surface area increases. Fuel cell test results have shown that the order of increasing maximum power density follows the order of increasing catalytic activity. Interestingly, the optimal crystallite size parameter and maximum activity for catalysts made with either ballmilled graphite powder or carbon black is almost the same.
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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) | 0.000 | 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".