Boron/Nitrogen Co-Doped Helically Unzipped Multiwalled Carbon Nanotubes as Efficient Electrocatalyst for Oxygen Reduction
Bibliographic record
Abstract
Bamboo structured nitrogen doped multiwalled carbon nanotubes have been helically unzipped, and nitrogen doped graphene oxide nanoribbons (CNx-GONRs) with a multifaceted microstructure have been obtained. CNx-GONRs have then been codoped with nitrogen and boron by simultaneous thermal annealing in ammonia and boron oxide atmospheres, respectively. The effects of the codoping time and temperature on the concentration of the dopants and their functional groups have been extensively investigated. X-ray photoelectron spectroscopy results indicate that pyridinic and BC3 are the main nitrogen and boron functional groups, respectively, in the codoped samples. The oxygen reduction reaction (ORR) properties of the samples have been measured in an alkaline electrolyte and compared with the state-of-the-art Pt/C (20%) electrocatalyst. The results show that the nitrogen/boron codoped graphene nanoribbons with helically unzipped structures (CNx/CBx-GNRs) can compete with the Pt/C (20%) electrocatalyst in all of the key ORR properties: onset potential, exchange current density, four electron pathway selectivity, kinetic current density, and stability. The development of such graphene nanoribbon-based electrocatalyst could be a harbinger of precious metal-free carbon-based nanomaterials for ORR applications.
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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".