Elucidation of structure-to-property relationships of piezoresistive polymer-carbon nanotube nanocomposites
Bibliographic record
Abstract
Polymeric nanocomposites (PNC) filled with carbon nanotubes (CNTs) possess superior multifunctionality, including electrical, thermal, and mechanical properties, making them an emerging family of advanced and multifunctional materials. In recent years, flexible polymer/CNT nanocomposites are increasingly being considered as promising alternatives to conventional smart materials. Their piezoresistive behaviours have led to many potential applications in strain sensing. Despite extensive experimental and theoretical research, the underlying mechanisms for polymer/CNT nanocomposites' piezoresistive behaviours have yet been elucidated. This paper reports comprehensive investigations on the mechanisms and the structure-to-property relationships of these piezoresistive nanocomposites. Quantitative analyses revealed that piezoresistivity of polymer/CNT nanocomposites is predominantly governed by the three mechanisms related to the strain-induced morphological evolution of the CNT network embedded in the polymer matrix. Furthermore, both CNT content and CNT alignment are key structural parameters that affect the contribution of different mechanisms on PNCs' piezoresistivity and the sensitivity of flexible PNCs as strain sensors. For PNC filled with high content of randomly dispersed CNTs, the piezoresistivity was predominantly caused by the breakage of a complex conductive network into two or more simpler conductive paths. For PNC filled with low content of highly aligned CNTs, the piezoresistivity was mainly contributed by the complete disruption of originally interconnected CNTs in electrically conductive pathways.
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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) | 0.001 | 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".