Mechanically and electrically robust stretchable e-textiles by controlling the permeation depth of silver-based conductive Inks
Bibliographic record
Abstract
Abstract E-textiles, electronics-integrated textiles, require stretchable interconnects with mechanical and electrical reliability over repeated deformation cycles. Whereas elastomers filled with conductive metallic particles have shown promises for e-textiles, conductive inks printed at the top of the textile substrate are prone to suffer brittleness that leads to failure. Here, we report that controlled permeation of silver particle filled fluoroelastomer ink can indeed strengthen nanofibrous textile substrates in terms of ultimate strain and stress, resulting in a reliable electrical conduction over harsh deformation cycles. The permeated ink forms a cladded-layer on the surface of polyurethane nanofiber strands, where the cladded-layer is intrinsically stronger and tougher than the nanofiber substrate. Selecting a solvent that swells the nano-textile substrate can facilitate deep permeation. Pressing treatment changes the internal structure drastically, which results in a further improvement of mechanical properties of the printed nano-textile. As a result, the strain-to-failure of the e-textile increases ∼3 times and the initial conductivity is 3399 S cm −1 . The resistance increases less than the factor of 2.5 over 4000 cycles of 20% uniaxial strain. The high performance of the stretchable interconnects envisions wearable healthcare and internet-of-things 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".