Soft and Strong: Elastic Conductors with Bio‐Inspired Self‐Protection
Bibliographic record
Abstract
Skin is soft yet strong - a combination achieved by integrating compliant elastin with stiff but wavy collagen, producing non-linear mechanical properties. Inspired by this structure, stiff conductive wires are engineered into sinusoidal patterns and mechanically interlocked them with highly elastic fibers using a reimagined woven fabric approach. The result is a highly conducting and stretchable yarn that also has high tensile strength - a combination that is attractive for wearable applications where comfort and durability are valued. With a diameter of ≈1 mm-comparable to many commercial yarns-the fabric-based yarn exhibits low stiffness across a broad strain range (up to 270% under 2 N of force) while demonstrating a self-protective transition to high stiffness and strength (up to 30 MPa) as it nears failure. Additionally, this yarn offers excellent flexibility, high strain tolerance (exceeding 500%), inherent breathability, and superior weavability. By tuning the number of elastic fibers and electrode fibers, it can further tailor these stretchable conductive yarns into strain-insensitive connecting yarns (low impedance at MHz frequencies, GF = 0.0003) and mechanical sensing yarns with dual strain and proximity sensing capabilities. The integration of these functional yarns enables system-level smart textile applications, such as wristband controllers.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".