Micromechanics Modeling of Bi-Axial Stretching Effects on the Electrical Conductivity of CNT-Polymer Composites
Bibliographic record
Abstract
In this paper, the bi-axial stretching effects on the electrical conductivity of carbon nanotube (CNT)-polymer composites are studied by a mixed micromechanics model with the consideration of the electrical conductive mechanisms. The bi-axial stretching effects are characterized by volume expansion of composite, re-orientation of CNTs and change of conductive networks. Simulation results demonstrate that the bi-axial stretching decreases the electrical conductivity of the composites due to the dominant role of the stretching-induced change in conductive networks, i.e., the increase in the percolation threshold, the separation distance among CNTs and the breakdown of the networks. It is also found that the bi-axial stretching enhances the decreasing rate of the electrical conductivity and increases the distribution randomness of the CNTs in the bi-axial stretching plane, as compared to a uni-axial stretching case. Furthermore, the dependency of the variation of electrical conductivity on the CNT concentration and sizes is also investigated. Possible reasons for the variation trends are interpreted. The study in this paper is expected to provide an increased understanding on the stretching effects upon the electrical conductivity of CNT-polymer composites.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".