Physics-based and Data-driven Modeling of Electrically Conductive Polymer Nanocomposites
Bibliographic record
Abstract
Polymer nanocomposites reinforced with carbon nanotubes (CNTs) have excellent mechanical, electrical, and electromechanical properties. Since the electrical properties vary with the mechanical applied load, interest in conducting polymer nanocomposite has increased due to their potential applications in strain sensing and structural health monitoring (SHM). An in-depth understanding of the structure-property relations of polymer nanocomposites reinforced with CNTs is needed to develop novel multifunctional materials. In this research program, a physics-based data-driven modeling framework capable of predicting the electrical and piezoresistive properties of CNT/polymer nanocomposites is developed. First, a physics-based stochastic multiscale model is developed using Monte Carlo simulations and representative volume elements. The developed numerical model is used to investigate the influence of the nanoscale parameters of CNTs and the microstructure of the nanocomposite on the percolation threshold, macroscopic electrical conductivity, and piezoresistivity of the CNT/polymer nanocomposites. Next, the numerical results from the developed numerical model are used to create representative datasets to train various machine learning models for efficient prediction of the nanocomposites properties. The developed framework is then used in the quantitative exploration of the structure-property relations of CNT/polymer nanocomposites to improve and accelerate the design of these multifunctional materials. The models developed in this research serve as tools for better understanding the underlying mechanisms of electrical conductivity and piezoresistivity in CNT/polymer nanocomposites. Moreover, the approaches used to develop these models can offer guidelines for modeling other multifunctional nanocomposites with embedded nanofillers. The insights gained from this research could be applied to SHM systems in modern structures like wind turbines, as well as within the aerospace and automotive sectors.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".