Highly Stretchable, Transparent, Self‐Healing Ion‐Conducting Elastomers for Long‐Term Reliable Human Motion Detection
Bibliographic record
Abstract
The flexible electronic sensor is a critical component of wearable devices, generally requiring high stretchability, excellent transmittance, conductivity, self-healing capability, and strong adhesion. However, designing ion-conducting elastomers meeting all these requirements simultaneously remains a challenge. In this study, a novel approach is presented to fabricate highly stretchable, transparent, and self-healing ion-conducting elastomers, which are synthesized via photo-polymerization of two polymerizable deep eutectic solvents (PDESs) monomers, i.e., methacrylic acid (MAA)/choline chloride (ChCl) and itaconic acid (IA)/ChCl. The as-prepared ion-conducting elastomers possess outstanding properties, including high transparency, conductivity, and the capability to adhere to various substrates. The elastomers also demonstrate ultra-stretchability (up to 3900%) owing to a combination of covalent cross-linking and noncovalent cross-linking. In addition, the elastomers can recover up to 3250% strain and over 94.5% of their original conductivity after self-healing at room temperature for 5 min, indicating remarkable mechanical and conductive self-healing abilities. When utilized as strain sensors to monitor real-time motion of human fingers, wrist, elbow, and knee joints, the elastomers exhibit stable and strong repetitive electrical signals, demonstrating excellent sensing performance for large-scale movements of the human body. It is anticipated that these ion-conducting elastomers will find promising applications in flexible and wearable 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) | 0.001 | 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 | 0.001 | 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".