Stretchable Polyurethane-Based Conductive Ink for E-Textile Applications
Bibliographic record
Abstract
ABSTRACT E-textiles play an important role in wearable electronics such as sensor, supercapacitor and nanogenerator applications. Coating or printing conductive ink on textiles is a simple, inexpensive and large-scale manufacturing method. However, to obtain highly conductive conductors, most conductive inks that are metal-based and carbon-based suffer from their poor adhesion to the textile; the cured inks are prone to be wiped off and washed away. Some inks on textiles crack because of the rigid property of their binder and the porous and deformable structure of the textile. In addition, organic solvents are often used in conductive inks which are harmful to the environment and human body. In this paper, a new aqueous-based conductive ink, which can penetrate into the textile effectively and obtain high conductivity, high stretchability and high durability is described and analyzed. This ink uses a highly stretchable water-based polyurethane as a binder. It is demonstrated that a textile coated with this ink can maintain high conductivity after 10 stretch cycles and 20 wash cycles. A strain E-textile sensor on the human body shows the potential application for tracking movement.
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 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".