Tridoped Reduced Graphene Oxide as a Metal‐Free Catalyst for Oxygen Reduction Reaction Demonstrated in Acidic and Alkaline Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
Recently, it has been demonstrated that doping of graphene by elements such as N, S, or F creates active sites for the oxygen reduction reaction (ORR). This results from bond polarization caused by the difference in electronegativity between heteroatom dopants and carbon, and/or the presence of defects within the graphene lattice. In this work, fluorine, nitrogen, and sulfur tridoped reduced graphene oxide (F,N,S‐rGO) is designed to combine these catalytically active sites. F,N,S‐rGO can be inexpensively synthesized by a facile and scalable route involving pyrolysis at 600 °C of sulfur‐doped rGO in the presence of Nafion and dimethyl formamide (DMF). The pyrolysis of Nafion and DMF provides F• and N• radicals which serve as doping agents. Rotating disk electrode investigations reveal the ORR catalytic activities of F,N,S‐rGO in both acidic and alkaline media, which are consistent with the real performances of the respective polymer electrolyte fuel cells (PEFCs). Maximum power densities of 14 and 46 mW cm−2 are obtained for the acidic and alkaline PEFCs, respectively, using F,N,S‐rGO as ORR catalysts. To the best of knowledge, this is the first report on the synthesis of F,N,S tridoped rGO and on its ORR activity in both acidic and alkaline PEFCs.
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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 | 0.000 | 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) | 0.000 | 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".