Direct conversion of carbon nanofibers and nanotubes into diamond nanofibers and the subsequent growth of large-sized diamonds
Bibliographic record
Abstract
We report a pulsed laser annealing method to convert carbon fibers and nanotubes into diamond fibers at ambient temperature and pressure in air. The conversion of carbon nanofibers and nanotubes into diamond nanofibers involves melting in a super undercooled state using nanosecond laser pulses, and quenching rapidly to convert into phase-pure diamond. The conversion process occurs at ambient temperature and pressure, and can be carried out in air. The structure of diamond fibers has been confirmed by selected-area electron diffraction in transmission electron microscopy, electron-back-scatter-diffraction in high-resolution scanning electron microscopy, all showing characteristic diffraction lines for the diamond structure. The bonding characteristics were determined by Raman spectroscopy with a strong peak near 1332 cm-1, and high-resolution electron-energy-loss spectroscopy in transmission electron microscopy with a characteristic peak at 292 eV for σ* for sp3 bonding and the absence of π* for sp2 bonding. The Raman peak at 1332 cm-1 downshifts to 1321 cm-1 for diamond nanofibers due to the phonon confinement in nanodiamonds. These laser-treated carbon fibers with diamond seeds are used to grow larger diamond crystallites further by using standard hot-filament chemical vapor deposition (HFCVD). We compare these results with those obtained without laser treating the carbon fibers. The details of diamond conversion and HFCVD growth are presented in this paper.
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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) | 0.001 | 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".