Effect of Different Surface Morphologies and Nitrogen Contents on the Electrochemical Activity of Nitrogen Doped Carbon Nanotubes towards Oxygen Reduction Reaction for Low Temperature Fuel Cells
Bibliographic record
Abstract
Nitrogen doped carbon nanotubes (NCNTs) with different nitrogen content and surface morphology were synthesized by a chemical vapour deposition technique. The surface structure and nitrogen content of the NCNTs was controlled by the choice of growth catalyst and the relative nitrogen-carbon composition in the precursor solution. Based on the structural characterizations, NCNTs synthesized using ferrocene as growth catalyst exhibited large degree of surface defect and showed higher oxygen reduction reaction (ORR) activity compared with NCNTs synthesized using iron (II) phthalocyanine as growth catalyst. From the elemental composition analysis, the overall nitrogen content was also found to be positively correlated to the ORR activity of NCNTs catalysts. This report illustrates the viable methods in improving ORR activity of NCNT 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 | 0.001 |
| 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".