Autonomous Self‐Healing Elastomers with Unprecedented Adhesion Force
Bibliographic record
Abstract
Abstract Self‐healable elastomers are extremely attractive due to their ability to prolong product lifetime. An additional function that could further expand their applications is strong adhesion force to clean and dusty surfaces. This study reports a series of autonomous self‐healable and highly adhesive elastomers (ASHA‐Elastomer) that are fabricated via a simple, efficient, and scalable process. The obtained elastomers exhibit outstanding mechanical properties with elongation at break up to 2102% and toughness (modulus of toughness) of 1.73 MJ m–3. The damaged ASHA‐Elastomer can autonomously self‐heal with full recovery of functionalities, and the healing process is not affected by the presence of water. The elastomers are found to possess an ultrahigh adhesion force up to 3488 N m−1, greatly outperforming previously reported self‐healing adhesive elastomers. Furthermore, the adhesion force of the ASHA‐Elastomer is negligibly affected by dust on the surface, in stark contrast with regular adhesive polymers that have adhesion strengths extremely sensitive to dust. The successful development of high‐toughness, autonomous self‐healable, and ultra‐adhesive elastomers will enable a wide range of applications with enhanced longevity and versatility, including their use in sealants, adhesives, and stretchable devices.
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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".