Graphene‐Coated Spandex Sensors Embedded into Silicone Sheath for Composites Health Monitoring and Wearable Applications
Bibliographic record
Abstract
This study presents a low-cost, tunable, and stretchable sensor fabricated based on spandex (SpX) yarns coated with graphene nanoplatelets (GnP) through a dip-coating process. The SpX/GnP is wrapped into a stretchable silicone rubber (SR) sheath to protect the conductive layer against harsh conditions, which allows for fabricating washable wearable sensors. Dip-coating parameters are optimized to obtain the maximum GnP coating rate. The covering sheath is tailored to achieve high stretchability beyond the sensing limit of 104% for SpX/GnP/SR sensors. Adjustable sensitivity is attained by manipulating SpX immersion times broadening its application for a wide range of strains: Gauge factors as high as two orders of magnitude are achieved at tensile strains greater than ≈40%. The fabricated sensors are tested for two applications: First, the SpX/GnP sensors are integrated into composite fabrics (with no negative impact on the structural integrity of the part) for screening the yarn displacements, resin flow, solidification during the hot press forming process, and structural health monitoring under mechanical loads with minimal cross-sensitivity to temperature/humidity. Second, the capability of SpX/GnP/SP sensors in detection of a wide range of bodily motions (from the joint motion to arterial blood pressure) is demonstrated.
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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".