Synergistic Effect of Hybrid Long Silver Nanowires and Carbon Nanotubes on Strain Sensing Behavior of Fluoroelastomer Nanocomposites
Bibliographic record
Abstract
In this study, a novel hybrid stretchable sensor is developed with a combination of carbon nanotubes (CNTs) and long silver nanowires (AgNWs) in fluoroelastomer (FKM) via melt-mixing method. An electrically conductive hybrid structure is created which provides a wider detection range of strain, i.e. from 23% strain sensing for single filler to more than 135% strain sensing for the hybrid CNT/AgNW fillers. Very long silver nanowires (AgNWs) are synthesized using an optimized polyol method producing high-aspect-ratio AgNWs with 75-100μm in length and 150nm in diameter. This comprehensive report includes a scalable fabrication process, morphology characterization, electrical properties and route to improve the strain sensing performance. The overall properties of hybrid FKM nanocomposite with 80/20 volume ratio of CNT/AgNW meet the requirements for many applications for flexible and stretchable electronics.
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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".