Highly Sensitive and Stretchable Carbon Nanotube/Fluoroelastomer Nanocomposite with a Double‐Percolated Network for Wearable Electronics
Bibliographic record
Abstract
Abstract A smart stretchable material is developed from a composite of carbon nanotube (CNT) and fluoroelastomer (FKM), which is fabricated via an internal melt‐mixer method. A unique, double‐percolated, electrically conductive network is observed with ultralow percolation thresholds of 0.45 phr and 1.40 phr CNT. This provides the CNT/FKM nanocomposites with a wide range of strain sensitivity. Thin‐film nanocomposites at the first plateau of conductivity show an ultrahigh sensitivity with a gauge factor (GF) of 1010 at 23% strain for 0.6 phr and of 6750 at 34% strain for 1 phr. At the second plateau of conductivity, 1.5 phr nanocomposite corresponds to higher levels of strain of 78% strain with ultrahigh GF of more than 4 × 104 and 2 phr nanocomposite to almost 100% strain with GF of 1.3 × 105. The CNT/FKM nanocomposites possess a high elongation at break of 430% and up to 232% strain sensitivity. The unique distribution of CNTs in the polar fluoroelastomer FKM facilitates simultaneous high sensitivity and high stretchability, and improved mechanical strength over reported polymer‐based nanocomposite stretchable sensors. The novel, stretchable CNT‐based FKM conductors have great potential for wearable electronics such as stretchable sensors, stretchable light‐emitting diodes (LEDs), and human motion monitoring.
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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".