Yarn-electrospun PVDF-HFP/CNC smart textiles for self-powered sensor in wearable electronics
Bibliographic record
Abstract
• PVDF-HFP/CNC composite nanofiber yarns were produced by yarn electrospinning. • CNC improved β phase content, piezoelectric, and mechanical properties of PVDF-HFP. • PVDF-HFP/CNC smart textile device output 21.2 V under compression of 20 N. • The device was integrated with touchscreen glove as a real-time motion sensor. The advancement of portable or wearable electronics has promoted research into flexible power sources that can be integrated seamlessly into devices. Wearable electronics, such as fitness tracking device, smart clothing, and medical sensors, require power sources that not only generate energy but also adapt to dynamic environments. To address such demand, we produced a self-powered device composed of electrospun PVDF-HFP/cellulose nanocrystal (CNC) composite yarns, which serve both as a flexible power source converting mechanical energy to electrical output and as a sensor providing real-time motion monitoring. As an example of its application, the self-powered device was integrated with touchscreen gloves to explore its functionality. Our results showed that CNC promoted β phase formation in PVDF-HFP, improving its piezoelectric and mechanical properties. The maximum voltage output obtained from the PVDF-HFP/CNC self-powered device was 21.2 V under compressive loads of 20 N at 0.5 Hz. The touchscreen glove integrated with the device offered good sensing performance to detect finger motions, such as single- and double-click or dragging even under sub-zero temperatures. The success of developing such sensor-integrated touchscreen gloves paves new avenues for human-technology interactions, highlights the dual functionality of these yarns as power sources and sensors, and represents a milestone in broadening the applications of wearable technologies.
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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 | 0.001 | 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".