Oxygen Reduction on Graphene–Carbon Nanotube Composites Doped Sequentially with Nitrogen and Sulfur
Bibliographic record
Abstract
The development of unique, reliable, and scalable synthesis strategies for producing heteroatom-doped nanostructured carbon materials with improved activity toward the electrochemical oxygen reduction reaction (ORR) occurring in metal–air batteries and fuel cells presents an intriguing technological challenge in the field of catalysis. Herein, we prepare unique graphene–carbon nanotube composites (GC) doped sequentially with both nitrogen and sulfur (GC-NLS) and subject them to extensive physicochemical characterization and electrochemical evaluation toward the ORR in an alkaline electrolyte. GC-NLS provides ORR onset potential increases of 50 and 70 mV in comparison to those of dual-doped individual graphene and carbon nanotubes, respectively. This highlights the significant synergistic effects that arise because of the nanocomposite arrangement, consisting of highly graphitized carbon nanotubes assembled on the surface of graphene sheets. The addition of sulfur as a co-dopant is also highly beneficial, providing an 80 mV increase in the ORR onset potential in comparison to that of GC nanocomposites doped with only nitrogen. Excellent electrochemical stability of GC-NLS is also observed through 5000 electrode potential cycles, indicating the promising potential of this new class of dual-doped GC nanocomposites as ORR catalysts.
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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".