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 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 | 0.001 | 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".