Recent Advances in Flexible/Stretchable Supercapacitors for Wearable Electronics
Bibliographic record
Abstract
The popularization of personalized wearable devices has accelerated the development of flexible/stretchable supercapacitors (SCs) that possess remarkable features of miniaturization, high security, and easy integration to build an all-in-one integrated system, and realize the functions of comfortable, noninvasive and continuous health monitoring, motion records, and information acquisition, etc. This Review presents a brief phylogeny of flexible/stretchable SCs, represented by planar micro-supercapacitors (MSCs) and 1D fibrous SCs. The latest progress and advantages of different flexible/stretchable/self-healing substrate, solid-state electrolyte and electrode materials for the fabrication of wearable SCs devices are summarized. The various configurations used in planar MSCs and 1D fibrous SCs aiming at the improvement of performance are also discussed. In addition, from the viewpoint of practical value and large-scale production, a survey of integrated systems, from different types of SC powered wearable sensing (gas, pressure, tactile…) systems, wearable all-in-one systems (including energy harvest, storage, and functional groups), to device packaging is presented. Finally, the challenges and future perspectives of wearable SCs are also considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".