Towards development of nanofibrous large strain flexible strain sensors with programmable shape memory properties
Bibliographic record
Abstract
Abstract Electrospun polymeric fibers can be used as strain sensors due to their large surface to weight/volume ratio, high porosity and pore interconnectivity. Large strain flexible strain sensors are used in numerous applications including rehabilitation, health monitoring, and sports performance monitoring where large strain detection should be accommodated by the sensor. This has boosted the demand for a stretchable, flexible and highly sensitive sensor able to detect a wide range of mechanically induced deformations. Herein, a physically cross-linked polylactic acid (PLA) and thermoplastic polyurethane (TPU) blend is made into nanofiber networks via electrospinning. The PLA/TPU weight ratio is optimized to obtain a maximum attainable strain of 100% while maintaining its mechanical integrity. The TPU/PLA fibers also allowed for their thermally activated recovery due to shape memory properties of the substrate. This novel feature enhances the sensor’s performance as it is no longer limited by its plastic deformation. Using spray coating method, a homogeneous layer of single-walled carbon nanotube is deposited onto the as-spun fiber mat to induce electrical conductivity to the surface of the fibers. It is shown that stretching and bending the sensor result in a highly sensitive and linear response with a maximum gauge factor of 33.
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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".