Cathode Catalysts Based on Cobalt- and Nitrogen-Doped Nanocarbon Composites for Anion Exchange Membrane Fuel Cells
Bibliographic record
Abstract
Cobalt- and nitrogen-doped carbide-derived carbon/carbon nanotube (CDC/CNT) composites are prepared and used as oxygen reduction reaction (ORR) electrocatalysts for an anion exchange membrane fuel cell (AEMFC) cathode. For the doping, high-temperature pyrolysis is applied using a cobalt salt and a nitrogen precursor (either dicyandiamide, urea, or melamine). During the doping, (i) new mesopores are formed as confirmed by the N 2 physisorption results, (ii) atomically dispersed cobalt is present on the catalysts as detected by scanning transmission electron microscopy, and (iii) N-pyridinic and Co–N 4 are the dominant N-containing species as shown by X-ray photoelectron spectroscopy. This indicates that using the composite of CDC and CNTs as well as the cobalt salt and nitrogen precursor is advantageous for the preparation of electrocatalysts. All three catalyst materials demonstrate similarly good electrocatalytic activity toward O 2 electroreduction in alkaline medium and excellent stability after 10000 repetitive potential cycles. The Co-N-CDC/CNT catalyst as the cathode material together with a hexamethyl- p -terphenyl poly(benzimidazolium) (HMT-PMBI) membrane exhibits excellent AEMFC performance by reaching maximum power density of 577 mW cm –2 .
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".