Modified Carbon Materials for O<sub>2</sub>Reduction Reaction Electrocatalysts in Acid PEM Fuel Cells
Bibliographic record
Abstract
Novel carbon materials such as fullerene, graphenes, and carbon nanotubes of various morphologies have seen immense development in the last few decades due to their widespread potential applications. Doping with heteroatoms such as nitrogen, phosphorus, and sulfur brings about significant changes in the electronic and structural properties, which makes them viable for use in semiconductors, fuel cells, and various other catalytic processes. Here, we present a brief review of the evolution of carbon materials (doped and undoped) and their applications as oxygen reduction reaction (ORR) catalysts for proton exchange membrane (PEM) fuel cells. The effect of transition metal on the catalytic activity of these materials has been reviewed, by comparing literature that report enhancement of catalytic activity owing to contribution of transition metal, with those that attribute activity to heteroatom doping: primarily nitrogen functional groups. This chapter covers the breadth of literature on these doped carbon materials for ORR, directing towards the many questions that remain to be answered.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".