Modeling electrical conductivity of nanocomposites by considering carbon nanotube deformation at nanotube junctions
Bibliographic record
Abstract
This paper investigates the effect of carbon nanotube (CNT) deformation on the electrical conductivity of CNT polymer composites at crossed nanotube junctions using a revised 3-dimensional CNT percolating network model. Two aspects of the work are considered. The first is concerned with the effect of CNT deformation on its intrinsic and contact resistances at CNT-CNT junctions. An analytical model based on electron ballistic tunneling theory and Landauer-Büttiker formula is proposed to describe the variation of CNT-CNT contact resistance at the CNT-CNT junction in terms of local deformation of CNT walls and CNT-CNT distance. In addition, a model exclusively based on experimental data to describe the change of CNT intrinsic resistance in terms of its cross-section deformation is adopted. The second is concerned with the relationship among the CNT-CNT distance, the angle between two adjacent CNTs, and the dimensions of local deformation of CNT walls and its impact on the corresponding intrinsic and contact resistance of CNTs near and at a CNT-CNT junction. Finally, Monte Carlo simulations are conducted to evaluate these effects on the electrical conductivity of nanocomposites for different CNT weight fractions. Our results reveal that the local deformation of CNT walls plays a significant role in the evaluation of electrical conductivity of CNT polymer composites. The intrinsic resistance in the deformed part of CNTs near a CNT-CNT junction increases much faster than the decrease of CNT-CNT contact resistance at the same junction when two CNTs are getting closer, resulting in a net increase of resistance at the junction. Numerical results show that the current model agrees with existing experimental data better than existing models without considering the effect of CNT deformation, which tends to overestimate the electrical conductivity of CNT polymer composites containing homogeneously dispersed percolating CNT network.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".