A Numerical Model to Study the Effect of Temperature on Electrical Conductivity of Polymer-CNT Nanocomposites
Bibliographic record
Abstract
In this study the effect of the temperature on the electrical conductivity of nanocomposites with carbon nanotube (CNT) fillers was investigated. A three-dimensional continuum Monte Carlo model was developed and employed first to form a CNT percolation network. CNT fillers were randomly generated and dispersed in a cubic representative volume element. Periodic boundary conditions were applied in this model to minimize size effects while decreasing computational cost. CNT fibers that connected electrically to each other through electron hopping were recognized and grouped as clusters. In addition to tunneling resistance, the effect of intrinsic CNT resistivity was considered. A three-dimensional resistor network was subsequently developed to evaluate nanocomposite electrical properties. Modeling employing the finite element method was conducted to evaluate the electrical conductivity of the percolation network. Considering the determining role of tunneling resistance on electrical conductivity of CNT based nanocomposites, as well as results obtained from experimental studies, temperature was expected to play an important role in nanocomposite electrical properties. The effect of temperature on electrical conductivity of CNT nanocomposites was thus investigated through employing the developed Monte Carlo and finite element models. Other aspects, including the electrical behavior of the polymer, tunneling resistivity and the intrinsic resistivity of CNT were considered in this study as well. The comprehensiveness of the developed modeling approach enables an evaluation of results in conjunction with experimental data in future works.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".