Decoding structural complexity in conical carbon nanofibers
Bibliographic record
Abstract
Conical carbon nanofibers (CNFs) exist primarily as graphitic ribbons that fold into a cylindrical structure with the formation of a hollow core. Structural analysis aided by molecular modeling proves useful for obtaining a full picture of how the size of the central channel varies from fiber to fiber. From a geometrical perspective, conical CNFs possibly have cone tips that are nearly closed. On the other hand, their fiber wall thickness can be reduced to a minimum possible value that is determined solely by the apex angle, regardless of the outer diameter. A formula has been developed to express the number of carbon atoms present in conical CNFs in terms of measurable structural parameters. It appears that the energetically preferred fiber wall thickness increases not only with the apex angle, but also with the number of atoms in the constituent graphitic cones. The origin of the empirical observation that conical CNFs with small apex angles tend to have a large hollow core lies in the fact that in graphene sheets that are more highly curved the curvature-induced strain energy rises more rapidly as the fiber wall thickens.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".