Solvent-assisted electrospun fibers with ultrahigh stretchability and strain sensing capabilities
Bibliographic record
Abstract
Abstract Large strain flexible strain sensors have recently been the focus of many studies due to their wide range of applications in wearable technologies. However, development of a thin, conformable, and flexible strain sensor with a high maximum stretchability and a high gauge factor has still remained a challenge. In resistive-type sensors specifically, there is a trade-off between these two competing factors which has left a gap in development of large strain flexible strain sensors. To increase the sensitivity of the sensor, tuning the microstructure of the sensor through introducing a larger surface area is suggested. Using a solvent-assisted electrospinning technique and a highly stretchable copolymer of styrenebutadiene-styrene, super elastic mats composed of microfibers with a large surface area are obtained. Coating the fibers with different conductive materials and coating methods, a flexible strain sensor able to detect up to 1000% strain is fabricated. The sensors also show low hysteresis under cyclic-induced applied loadings.
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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".