Helical and Dendritic Unzipping of Carbon Nanotubes: A Route to Nitrogen-Doped Graphene Nanoribbons
Bibliographic record
Abstract
Bamboo structured nitrogen-doped multiwalled carbon nanotubes (CN(x)-MWCNTs) have been successfully unzipped by a chemical oxidation route, resulting in nitrogen-doped graphene nanoribbons (CN(x)-GNRs) with a multifaceted microstructure. The oxidation of CN(x)-MWCNTs was carried out using potassium permanganate in the presence of trifluoroacetic acid or phosphoric acid. On the basis of the high resolution transmission electron microscopy studies, the bamboo compartments were unzipped via helical or dendritic mechanisms, which are different from the longitudinal unzipping of open channel MWCNTs. The product graphene oxide nanoribbons were simultaneously reduced and doped with nitrogen by thermal annealing in an ammonia atmosphere. The effects of the annealing temperature, time, and atmosphere on the doping level and types of the nitrogen functional groups have been investigated. X-ray photoelectron spectroscopy results indicate that a wide range of doping levels can be achieved (4-9 at %) simply by changing the annealing conditions. Pyridinic and pyrrolic nitrogen functional groups were the dominant species that were attached to the edges of the CN(x)-GNRs. The GNRs, with a faceted structure and pyridinic and pyrrolic groups on their edges, have abundant nitrogen sites. These active sites could play a vital role in enhancing the electrocatalytic performance of GNRs.
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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".