Electron tunnelling and hopping effects on the temperature coefficient of resistance of carbon nanotube/polymer nanocomposites
Bibliographic record
Abstract
Effectively tailoring the temperature coefficient of resistance (TCR) is critical for multifunctional carbon nanotube (CNT) polymer composites with sensing capability. By developing a new multiscale percolation network model, this work reveals theoretically that the zero-TCR could be achieved by adjusting competing contributions from thermally assisted tunnelling transport at CNT junctions and thermal expansion of matrices. On the other hand, the negative temperature coefficient of nanocomposites above glass transition temperature could be greatly enhanced because the transport mechanism at the CNT junctions experienced a transition from tunnelling to hopping. Both tube-tube and/or tube-matrix interactions at conjunction and the structural distortion of nanotubes are considered in the newly proposed model. To validate the model, CNT/polymer nanocomposites with nearly constant resistance values (zero-TCR) below the glass transition temperature and a high TCR (98% resistance change ratio) resulting from the glass transition of the polymer matrix are successfully developed. The study also suggests that the desired parameters to achieve the zero-TCR property and the potential resistance change ratio could be improved by the glass transition in nanocomposites. This could be beneficial for the development of high quality sensing materials.
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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 | 0.000 |
| 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".