Pressure effect on radial breathing modes of multiwall carbon nanotubes
Bibliographic record
Abstract
This paper studies the pressure effect on radial breathing modes (RBMs) of multiwall carbon nanotubes (MWNTs). The analysis is based on a multiple-elastic shell model which assumes that each of the concentric tubes of a MWNT is an individual elastic shell and coupled with adjacent tubes through van der Waals interaction. The pressure effect on RBMs of MWNTs is mainly attributed to the pressure-induced reduction of interlayer spacing and the increase of the interlayer vdW interaction coefficient defined by the second derivative of the energy-interlayer spacing relation of MWNTs. In the absence of external pressure, the RBM frequencies and vibration modes predicted by the present shell model are found to agree very well with the available experimental and molecular-dynamics simulation results. In the presence of an external pressure, our results show that high external pressure considerably raises the vdW interaction coefficients especially between the outermost few layers of MWNTs. As a result, some of the RBM frequencies of MWNTs increase significantly with increasing external pressure. The most significant pressure effect occurs for the highest-frequency mode of large-diameter MWNTs (with the innermost diameter greater than 2nm) or an intermediate-frequency mode of small-diameter MWNTs (with the innermost diameter less than 2nm), and is always associated with those RBMs in which adjacent outermost layers vibrate in opposite directions with significant change in interlayer spacing.
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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".