Piezoresistance in Polymer Nanocomposites with High Aspect Ratio Particles
Bibliographic record
Abstract
In this paper, we address the problem of positive piezoresistance in high aspect ratio particle based polymer nanocomposites, a hybrid system at the center of research on flexible piezoresistive materials. We introduce a percolation theory based model relating the variation in electrical resistance to compressive strain and show that it gives accurate theoretical fits to experimental data presented in this paper, as well as to much of the available data in the literature. In contrast to existing theories, the model captures the characteristics of the particle network through experimentally definable parameters and does not rely on assumptions regarding the nature of the particles and/or the configuration of the network. It is further demonstrated that the presented theoretical framework is not limited to polymer nanocomposites with high aspect ratio particle but that it can explain piezoresistance in bulk electroconductive polymer nanocomposites in general. We find that the piezoresistive effect in such materials is rooted in a mechanical deformation induced change in the distribution of local conductances within the particle network, and we show that this change in the distribution of local conductances is well described by a strain dependent conductivity exponent, which scales with the magnitude of mechanical deformation. Besides, we demonstrate that these findings can be applied to the experimentally observed concentration dependence of the piezoresistance in polymer nanocomposites and, thus, to predicting the electric response to mechanical deformation at any particle concentration, which is expected to be highly instrumental in applied materials selection and performance evaluation.
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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) | 0.000 | 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".