Influence of Structural Properties of Pristine Carbon Blacks on Activity of Fe∕N∕C Cathode Catalysts for PEFCs
Bibliographic record
Abstract
Four series of carbon blacks, with various disordered carbon content, have been fabricated at the Sid Richardson Carbon Corporation and were used as supports to obtain heat-treated electrocatalysts for the oxygen reduction reaction (ORR) in polymer electrolyte fuel cells (PEFCs). All catalysts were very active and differed by about one order of magnitude in their electrocatalytic activity. Two pristine carbon blacks were then selected to determine, by Raman spectroscopy and X-ray diffraction, which structural parameters of the pristine carbons are important in electrocatalysis. It was found that (the full width at half-maximum of the D band in the Raman spectrum) is indicative of the disordered phase content in the pristine carbon black. The pristine carbon black having the largest also yielded the best ORR electrocatalytic activity. and are structural parameters representing the lateral and vertical extensions of the graphitic crystallites. They do not change with the catalytic activity, at least not with a change of one order of magnitude of the catalytic site density. Only the distance between the graphene layers in the graphitic crystallites changes with the electrocatalytic activity, but only marginally. This is, however, believed to be an indirect effect related to the gasification of disordered carbon found between the graphitic crystallites in pristine carbon black.
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 | 0.001 |
| 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".