Self-healing electronic skin with high fracture strength and toughness
Bibliographic record
Abstract
Human skin is essential for perception, encompassing haptic, thermal, proprioceptive, and pain-sensing functions through ion movement. Additionally, it is mechanically resilient and self-healing for protection. Inspired by these unique properties, researchers have attempted to develop stretchable, self-healing sensors based on ion dynamics. However, most self-healing sensors reported to date suffer from low fracture strength and toughness. In this work, we present an ion-based self-healing electronic skin with exceptionally high fracture strength and toughness. We enhanced self-healing polymers and ionic conductors by introducing two types of orthogonal dynamic crosslinking bonds: dynamic aromatic disulfide bonds and 2-ureido-4-pyrimidone moieties. These dynamic bonds provide autonomous self-healing and high mechanical toughness even in the presence of ionic liquids. As a result, our self-healing polymer and self-healing ionic conductor exhibit remarkable stretchability (700%, 850%), fracture strength (34 MPa, 30 MPa), and toughness (78.5 MJ/m3, 87.3 MJ/m3), the highest values reported among self-healing ionic conductors to date. Using our materials, we developed various fully self-healing sensors and a soft gripper capable of autonomously recovering from mechanical damage. By integrating these components, we created a comprehensive self-healing electronic skin suitable for soft robotics applications. Stretchable and self-healing sensors based on ion dynamics usually suffer from low fracture strength and toughness. Here, the authors describe an ion-based self-healing electronic skin with autonomous self-healing, high mechanical toughness and fracture strength.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".