A continuum model with a percolation threshold and tunneling-assisted interfacial conductivity for carbon nanotube-based nanocomposites
Bibliographic record
Abstract
A continuum model that possesses several desirable features of the electrical conduction process in carbon-nanotube (CNT) based nanocomposites is developed. Three basic elements are included: (i) percolation threshold, (ii) interface effects, and (iii) tunneling-assisted interfacial conductivity. We approach the first one through the selection of an effective medium theory. We approach the second one by the introduction of a diminishing layer of interface with an interfacial conductivity to build a "thinly coated" CNT. The third one is introduced through the observation that interface conductivity can be enhanced by electron tunneling which in turn can be facilitated with the formation of CNT networks. We treat this last issue in a continuum fashion by taking the network formation as a statistical process that can be represented by Cauchy's probability density function. The outcome is a simple and yet widely useful model that can simultaneously capture all these fundamental characteristics. It is demonstrated that, without considering the interface effect, the predicted conductivity would be too high, and that, without accounting for the additional contribution from the tunneling-assisted interfacial conductivity, the predicted conductivity beyond the percolation threshold would be too low. It is with the consideration of all three elements that the theory can fully account for the experimentally measured data. We further use the developed model to demonstrate that, despite the anisotropy of the intrinsic CNT conductivity, it is its axial component along the CNT direction that dominates the overall conductivity. This theory is also proved that, even with a totally insulating matrix, it is still capable of delivering non-zero conductivity beyond the percolation threshold.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".