An Elastic Autonomous Self‐Healing Capacitive Sensor Based on a Dynamic Dual Crosslinked Chemical System
Bibliographic record
Abstract
Adopting self-healing, robust, and stretchable materials is a promising method to enable next-generation wearable electronic devices, touch screens, and soft robotics. Both elasticity and self-healing are important qualities for substrate materials as they comprise the majority of device components. However, most autonomous self-healing materials reported to date have poor elastic properties, i.e., they possess only modest mechanical strength and recoverability. Here, a substrate material designed is reported based on a combination of dynamic metal-coordinated bonds (β-diketone-europium interaction) and hydrogen bonds together in a multiphase separated network. Importantly, this material is able to undergo self-healing and exhibits excellent elasticity. The polymer network forms a microphase-separated structure and exhibits a high stress at break (≈1.8 MPa) and high fracture strain (≈900%). Additionally, it is observed that the substrate can achieve up to 98% self-healing efficiency after 48 h at 25 °C, without the need of any external stimuli. A stretchable and self-healable dielectric layer is fabricated with a dual-dynamic bonding polymer system and self-healable conductive layers are created using polymer as a matrix for a silver composite. These materials are employed to prepare capacitive sensors to demonstrate a stretchable and self-healable touch pad.
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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 | 0.001 |
| Open science | 0.001 | 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".