Phonon localization in single wall carbon nanotube: Combined effect of 13C isotope and vacancies
Bibliographic record
Abstract
The combined effect of 13C isotope doping and vacancies on the phonon properties of a single-wall carbon nanotube is theoretically investigated using the forced oscillation method. The phonon density of states (PDOS) is calculated for all (0%–100%) 13C isotope contents and wide (0%–30%) vacancy concentrations. We found a redshift of the Raman active E2g peak in the PDOS with increasing isotope contents, while the disappearance of the E2g peak and the appearance of a new sharp peak in the low-energy region with increasing combined defects. Both 13C isotope and combined defects cause the localization of the high-energy optical phonons. We calculated the typical mode patterns for the in-plane longitudinal optical phonon to visualize the localization phenomena elaborately at the presence of isotope and vacancies. The calculated average localization length shows an asymmetric behavior with increasing 13C isotope concentrations which is in good agreement with the 13C isotope dependence localization length of single-layer graphene. We noticed that a typical localization length is on the order of ∼1 nm at 70% isotope concentrations. The combined effect of 13C isotope and vacancies shows an abruptly decreasing localization length with increasing defect densities. These results are important to understand the heat conduction as well as nanoscopic vibrational studies such as tip-enhanced Raman spectra in carbon nanotubes where the local phonon energies may be mapped.
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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".