Two-dimensional carbon material incorporated and PDMS-coated conductive textile yarns for strain sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In recent years, innovative technology based upon conductive textile yarns has undergone rapid growth. Nanocomposite-based wearable strain sensors hold great promise for a variety of applications, but specifically for human body motion detection. However, improving the sensitivity of these strain sensors while maintaining their durability remains a challenge in this arena. In the present investigation, polydopamine-treated and two-dimensional nanostructured material, e.g., reduced graphene oxide (rGO)-coated conductive cotton and polyester yarns, was encapsulated using polydimethylsiloxane (PDMS) to develop robustly wash durable and mechanically stable conductive textile yarns. Flexibility and extensibility of all textile yarns of every stage were analyzed using texture analysis. The chemical interactions essential for measuring coating performance among all components were confirmed by Fourier transform infrared and scanning electron microscopy. The rGO-coated cotton and polyester yarns exhibited an extensibility of 11.77 and 73.59%, respectively. PDMS-coated conductive cotton and polyester yarns also showed an electrical resistance of 12.22 and 20.33 kΩ, respectively, after 10 washing cycles. The PDMS coating layer acted as a physical barrier against impairment of conductivity during washing. Finally, the mechanically stable and flexible conductive textile yarns were integrated into a knitted cotton glove and armband to create a highly stretchable and flexible textile-based strain sensor for measuring finger and elbow movement. Truly wearable garments able to record proprioceptive maps are critical for further developing this field of application.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it