Molecular dynamics simulation and mechanical properties of carbon nanotube/nylon 6 composites
Bibliographic record
Abstract
sIn this paper, the mechanical properties of carbon nanotube reinforced nylon 6 composites were investigated using molecular dynamics simulations. The effects of different types of carbon nanotubes (armchair type and zigzag type) on the mechanical properties of the composites were investigated, and it was found that the elastic modulus of the armchair-type carbon nanotube reinforced composites was higher. The effects of carbon nanotube content and temperature on the mechanical properties of the composites were investigated, and it was found that the mechanical properties of the composites were best at 6.71% carbon nanotube content, and the composites exhibited good thermal stability and the mechanical properties at lower temperatures. The results show that different strain rates have a significant effect on the ultimate stress of the composites, and the mechanical properties of the composites are better at high strain rates. Different degrees of functionalization of carbon nanotubes can improve the mechanical properties of composites. When the number of carboxyl groups is 8, the mechanical properties of the composites are the best, with an ultimate stress of 0.44 GPa and an elastic modulus of 3.64 MPa, which are 19% and 23% higher than that of the unfunctionalized ones, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".