Transition-Metal and Nitrogen-Doped Carbon Nanotube/Graphene Composites as Cathode Catalysts for Anion-Exchange Membrane Fuel Cells
Bibliographic record
Abstract
Transition-metal and nitrogen-doped graphene-like material and carbon nanotube (M-N-Gra/CNT) composites are prepared, characterized, and used as cathode catalysts in anion-exchange membrane fuel cells (AEMFCs). Melamine as a nitrogen source and cheap iron and cobalt salts as metal precursors are used for doping via high-temperature pyrolysis. The success of doping is proven by several physicochemical analysis methods, and the catalyst materials possess rather similar textural properties. The initial assessment of the oxygen reduction reaction activity using the rotating disk electrode method shows that Fe-N-Gra/CNT, Co-N-Gra/CNT, and CoFe-N-Gra/CNT materials have very similar electrocatalytic performances in alkaline media as well as excellent short-term stability but a different yield of HO 2 – formation. The M-N-Gra/CNT materials as cathode catalysts together with the Aemion+ reinforced anion-exchange membrane exhibit very good AEMFC performance, especially CoFe-N-Gra/CNT, comparable to that of Pt/C, reaching a peak power density of 638 mW cm –2 . Such an excellent fuel cell performance of the M-N-Gra/CNT catalyst materials is attributed to the presence of M–N x sites, carbon-encapsulated transition-metal nanoparticles, and feasible nitrogen-containing moieties.
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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 | 0.001 | 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".