Experimental-Computational Study of Shear Interactions within Double-Walled Carbon Nanotube Bundles
Bibliographic record
Abstract
The mechanical behavior of carbon nanotube (CNT)-based fibers and nanocomposites depends intimately on the shear interactions between adjacent tubes. We have applied an experimental-computational approach to investigate the shear interactions between adjacent CNTs within individual double-walled nanotube (DWNT) bundles. The force required to pull out an inner bundle of DWNTs from an outer shell of DWNTs was measured using in situ scanning electron microscopy methods. The normalized force per CNT-CNT interaction (1.7 ± 1.0 nN) was found to be considerably higher than molecular mechanics (MM)-based predictions for bare CNTs (0.3 nN). This MM result is similar to the force that results from exposure of newly formed CNT surfaces, indicating that the observed pullout force arises from factors beyond what arise from potential energy effects associated with bare CNTs. Through further theoretical considerations we show that the experimentally measured pullout force may include small contributions from carbonyl functional groups terminating the free ends of the CNTs, corrugation of the CNT-CNT interactions, and polygonization of the nanotubes due to their mutual interactions. In addition, surface functional groups, such as hydroxyl groups, that may exist between the nanotubes are found to play an unimportant role. All of these potential energy effects account for less than half of the ~1.7 nN force. However, partially pulled-out inner bundles are found not to pull back into the outer shell after the outer shell is broken, suggesting that dissipation is responsible for more than half of the pullout force. The sum of force contributions from potential energy and dissipation effects are found to agree with the experimental pullout force within the experimental error.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".