Iron‐ and Nitrogen‐Doped Graphene‐Based Catalysts for Fuel Cell Applications
Bibliographic record
Abstract
Abstract A simple synthesis method was used to prepare an active oxygen reduction reaction (ORR) electrocatalyst based on iron and nitrogen co‐doped graphene for polymer electrolyte fuel cell applications. For the synthesis of the ORR catalysts, two different graphene‐based materials, commercially available graphene (Gra) and graphene oxide (GO), were used as the carbon substrates. The half‐cell experiments conducted by using the rotating disc electrode (RDE) method revealed that Fe−N−Gra showed much higher ORR electrocatalytic activity than Fe−N−GO in alkaline medium. This is attributed to the higher surface area, micro‐/mesoporous nature and larger amount of Fe‐Nx/amine moieties present in Fe−N−Gra compared to Fe−N−GO, as shown by different physicochemical methods. Almost half of the iron was confirmed to be in highly active Fe‐Nx form by 57Fe Mössbauer spectroscopy. Thus, the Fe−N−Gra as ORR catalyst was further selected to apply this for both proton exchange membrane (PEM) and anion exchange membrane (AEM) fuel cell tests.
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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 | 0.000 |
| 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".