Metal-Precursor Adsorption Effects on Fe-Based Catalysts for Oxygen Reduction in PEM Fuel Cells
Bibliographic record
Abstract
Fe-based electrocatalysts for the reduction of oxygen in polymer electrolyte membrane (PEM) fuel cells have been prepared by adsorbing either or ions on two carbon blacks to determine if their maximum activity is limited by ( i ) the maximum number of micropores available to host catalytic sites in the support or by ( ii ) the maximum number of ions able to be adsorbed on the carbon. The two carbon supports having the same microporous surface area, one etched in air and the other in , were derived from the same carbon black (N234). Air-etched N234 is more acidic in nature as it possesses carboxylic functionalities that can adsorb , while -etched N234 possesses both pyridinic and carboxylic functionalities that can adsorb either or . Catalysts were prepared by heat-treating, in pure , the materials resulting from or adsorption on both etched carbons. It is concluded that, when catalysts are prepared in pure , the catalytic activity is only governed by the number of micropores, having a size between 0.8 and , that are available to host the catalytic sites in the porous volume of the carbon support, because Fe ion uptake by adsorption is never the limiting factor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".