Self‐Healing and Shape‐Editable Wearable Supercapacitors Based on Highly Stretchable Hydrogel Electrolytes
Bibliographic record
Abstract
Shape editability combined with a self-healing capability and long-term cycling durability are highly desirable properties for wearable supercapacitors. Most wearable supercapacitors have rigid architecture and lack the capacity for editability into desirable shapes. Through sandwiching hydrogel electrolytes between two electrodes, a suite of wearable supercapacitors that integrate desirable properties namely: repeated shape editability, excellent self-healing capability, and long-term cycling durability is demonstrated. A strategy is proposed to enhance the long-term cycling durability by utilizing hydrogel electrolytes with unique cross-linking structures. The dynamic crosslinking sites are formed by quadruple H bonds and hydrophobic association, stabilizing the supercapacitors from inorganic ion disruption during charge-discharge processes. The fabricated supercapacitors result in the capacitance retention rates of 99.6% and 95.8% after 5000 and 10 000 charge-discharge cycles, respectively, which are much higher than others reported in the literature. Furthermore, the supercapacitor sheets can be repeatedly processed into various shapes without any capacitance loss. The supercapacitors exhibit a 95% capacitance retention rate after five cutting/self-healing cycles, indicative of their excellent self-healing performance. To demonstrate real-life applicability, the wearable supercapacitors are successfully used to power a light-emitting diode and an electronic watch.
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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".