Strain-Insensitive Supercapacitors for Self-Powered Sensing Textiles
Bibliographic record
Abstract
Yarn-based supercapacitors and sensors can be easily integrated into textiles to form flexible and lightweight self-powered wearable electronic devices, which enable stable and continuous signal detection without an external power source. However, most current supercapacitors for self-powered systems lack the stretchability to adapt to complex human body deformations, which restricts their application as a stable wearable power source. This study presents a high-performance strain-insensitive yarn supercapacitor via prestretching in situ polymerization strategy, which can be integrated into self-powered wearable sensing textiles. The supercapacitor delivers a high specific capacitance of 20.79 mF cm –1 (116.94 F g –1 ), a power density of 37.54 μW cm –1 (211.22 W kg –1 ), and an energy density of 1.85 μWh cm –1 (10.39 Wh kg –1 ). The strain-insensitive ability is demonstrated with nearly unchanged performance at a high static strain of 200%, dynamic strain rates of 10% s –1, and retains 96.46% of its capacitance after 3500 cycles under 50% strain. The pressure sensor, featuring a striped coating structure, shows a high sensitivity of 0.67 kPa –1 and a short response time of 100 ms. The strain-insensitive yarn supercapacitors with superior reliability serve as an energy source to power pressure sensors that efficiently recognize Morse code, showing great potential in truly wearable health monitoring and rehabilitation training applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".