Highly Conducting and Ultra‐Stretchable Wearable Ionic Liquid‐Free Transducer for Wireless Monitoring of Physical Motions
Bibliographic record
Abstract
Abstract Wearable strain transducers are poised to transform the field of healthcare owing to the promise of personalized devices capable of real‐time collection of human physiological health indicators. For instance, monitoring patients' progress following injury and/or surgery during physiotherapy is crucial but rarely performed outside clinics. Herein, multifunctional liquid‐free ionic elastomers are designed through the volume effect and the formation of dynamic hydrogen bond networks between polyvinyl alcohol (PVA) and weak acids (phosphoric acid, phytic acid, formic acid, citric acid). An ultra‐stretchable (4600% strain), highly conducting (10 mS cm −1 ), self‐repairable (77% of initial strain), and adhesive ionic elastomer is obtained at high loadings of phytic acid (4:1 weight to PVA). Moreover, the elastomer displayed durable performances, with intact mechanical properties after a year of storage. The elastomer is used as a transducer to monitor human motions in a device comprising an ESP32‐based development board. The device detected walking and/or running biomechanics and communicated motion‐sensing data (i.e., amplitude, frequency) wirelessly. The reported technology can also be applied to other body parts to monitor recovery after injury and/or surgery and inform practitioners of motion biomechanics remotely and in real time to increase convalescence effectiveness, reduce clinic appointments, and prevent injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".