Effects of inter-tube distance and alignment on tunnelling resistance and strain sensitivity of nanotube/polymer composite films
Bibliographic record
Abstract
A model for carbon nanotube (CNT)/polymer composite conductivity is developed, considering the effect of inter-tube tunnelling through the polymer. The statistical effects of inter-tube distance and alignment on the tunnelling are investigated through numerical modelling, to highlight their role in the conductance and piezoresistance of the composite film. The impact of critical parameters, including the concentration, alignment and aspect ratio of the CNTs and the tunnelling barrier height of the polymer is statistically evaluated using a large number of randomly generated CNT/polymer composite films. A numerical model is presented for the tunnelling resistance as a function of CNT concentration and polymer properties, which provides good agreement with the reported conductance in the literature. In particular, for a low concentration of CNTs close to the percolation threshold, we demonstrate how tunnelling dominates the conductance properties and leads to significant increase in the piezoresistance of the composite. This is important for gaining insight into the optimum concentration and alignment of the CNTs in the composite film for applications such as strain sensors, anisotropic conductive films, transparent electrodes and flexible electronics.
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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 | 0.001 | 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".