Coupled Effects of Film Thickness and Filler Length on Conductivity and Strain Sensitivity of Carbon Nanotube/Polymer Composite Thin Films
Bibliographic record
Abstract
The coupled effects of varying composite film thicknesses and filler lengths on the conductivity and strain sensitivity of carbon nanotube (CNT)/polymer composite films are investigated through modeling and experiments. Change in average intertube distance is calculated statistically through the Monte Carlo simulations for samples with different CNT concentrations and film thicknesses for a given filler aspect ratio. The composite conductivity is then estimated from the intertube distance with a semi-analytical model based on a tunneling current. The dependence of conductivity on mechanical strain is investigated for varying film thickness for strain sensor applications. A partial alignment of CNTs introduced at film thicknesses less than the CNT length is observed to have a significant influence on the composite conductivity and strain sensitivity, specially at low CNT concentrations. The modeling results can explain the observed experimental results of conductivity for CNT composites, which illustrate a unique dip in conduction with increasing thickness. These results are important for understanding the composite characteristics with different filler orientations and film thicknesses for a given filler length, and useful for the design optimization of high performance composite electronic films for applications in electronic skin and sensors.
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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 | 0.000 |
| 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".