Shear and Friction between Carbon Nanotubes in Bundles and Yarns
Bibliographic record
Abstract
We perform a detailed density functional theory assessment of the factors that determine shear interactions between carbon nanotubes (CNTs) within bundles and in related CNT and graphene structures including yarns, providing an explanation for the shear force measured in recent experiments (Filleter, T. etal. Nano Lett. 2012, 12, 73). The potential energy barriers separating AB stacked structures are found to be irrelevant to the shear analysis for bundles and yarns due to turbostratic stacking, and as a result, the tube-tube shear strength for pristine CNTs is estimated to be <0.24 MPa, that is, extremely small. Instead, it is pinning due to the presence of defects and functional groups at the tube ends that primarily cause resistance to shear when bundles are fractured in weak vacuum (∼10(-5) Torr). Such defects and groups are estimated to involve 0.55 eV interaction energies on average, which is much larger than single-atom vacancy defects (approximately 0.039 eV). Furthermore, because graphitic materials are stiff they have large coherence lengths, and this means that push-pull effects result in force cancellation for vacancy and other defects that are internal to the CNTs. Another important factor is the softness of cantilever structures relative to the stiff CNTs in the experiments, as this contributes to elastic instability transitions that account for significant dissipation during shear that has been observed. The application of these results to the mechanical behavior of yarns is discussed, providing general guidelines for the manufacture of strong yarns composed of CNTs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".