Fe-Based Catalysts for Oxygen Reduction in PEMFCs
Bibliographic record
Abstract
Fe-based catalysts for the oxygen reduction reaction (ORR) in polymer electrolyte membrane fuel cells (PEMFCs) have been prepared with commercial and developmental carbon black powders containing initially between 0 and 0.8 atom % of nitrogen. The catalysts were obtained by adsorbing 0.2 wt % Fe from iron acetate on each carbon support, which is then pyrolyzed at 900°C for 1 h in a mixture. Under these conditions, N contents from 0 to 2.3 atom % are measured at the surface of the catalysts and increased N content leads to increased activity for the ORR. The N content correlates with the weight loss of the carbon support due to a reaction with during pyrolysis. It was found that reacts mainly with the disorganized carbon, leaving nitrogen at the surface of the support. The larger the amount of disorganized carbon in the pristine carbon black, the better the activity for ORR of the resulting catalyst. The most active non-noble catalyst was tested in fuel cells, where it was found that its specific activity (in A per of electrode) is still about two orders of magnitude below the target of a non-noble catalyst for automotive applications . However, such catalysts could already compete with Pt in, e.g., methanol fuel cells because they are ORR-selective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".