Two‐Layered and Stretchable e‐Textile Patches for Wearable Healthcare Electronics
Bibliographic record
Abstract
Abstract Wearable healthcare systems require skin‐adhering electrodes that allow maximal comfort for patients as well as an electronics system to enable signal processing and transmittance. Textile‐based electronics, known as “e‐textiles,” is a platform technology that allows comfort for patients. Here, two‐layered e‐textile patches are designed by controlled permeation of Ag‐particle/fluoropolymer composite ink into a porous textile. The permeated ink forms a cladding onto the nanofibers in the textile substrate, which is beneficial for mechanical and electrical properties of the e‐textile. The printed e‐textile features conductivity of ≈3200 S cm−1, whereas 1000 cycles of 30% uniaxial stretching causes the resistance to increase only by a factor of ≈5, which is acceptable in many applications. Controlling over the penetration depth enables a two‐layer design of the e‐textile, where the sensing electrodes and the conducting traces are printed in the opposite sides of the substrate. The formation of vertical interconnected access is remarkably simple as an injection from a syringe. With the custom‐developed electronic circuits, a surface electromyography system with wireless data transmission is demonstrated. Furthermore, the dry e‐textile patch collects electroencephalography with comparable signal quality to commercial gel electrodes. It is anticipated that the two‐layered e‐textiles will be effective in healthcare and sports applications.
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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".