Delamination‐Resistant and Light‐Induced Shape‐Memory Supercapacitors for Wearable Devices
Bibliographic record
Abstract
Abstract Shape‐memory supercapacitors (SMCs) offer promising energy solutions for powering wearable sensors and electronic devices, enabling form adaptability on moving bodies and objects. However, traditional SMCs require high temperatures for shape‐editing, posing safety risks for devices, and often suffer from delamination during repeated shape transformations. In this study, a new class of wearable supercapacitors is reported that combine light‐induced shape ‐ memory with strong interfacial adhesion, achieved through a rehydration‐based assembly strategy using hydrogel electrolytes and electrodes. Key to this advancement are cinnamate‐functionalized materials, which undergo reversible [2+2] photocycloaddition under ultraviolet light at different wavelengths, enabling repeatable shape reconfiguration under mild, ambient conditions. The interfacial adhesive stress between the electrode and electrolyte reaches up to 40 kPa, substantially improving structural integrity. The supercapacitors exhibit outstanding electrochemical performance, with capacitance retention rates of 98.6% and 94.8% after 5000 and 10 000 charge–discharge cycles, respectively – surpassing values reported in previous studies. Additionally, they maintain 96.8% capacitance after ten shape‐memory cycles and can be reconfigured into multiple shapes without performance loss. Demonstrations powering a light‐emitting diode and an electronic watch further highlight their practical applicability in wearable and flexible electronics.
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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".