Influence of yarn geometry on electrical properties of silver-coated nylon filaments for e-textiles: a fundamental study
Bibliographic record
Abstract
Conductive fibrous assemblies and yarns play a crucial role in wearable electronic textiles (e-textiles), through their use in flexible sensors and interconnects. This study investigated the influence of yarn twist and geometrical parameters on the electrical properties of silver-coated nylon multifilament yarns, ranging from 1-ply to 4-ply, with twist levels of 30 twists per meter (TPM) and up to 600 TPM. Increase in twist level resulted in decreasing yarn linear resistance, with a plateau at 300 TPM, along with limiting values for yarn specific volume (1.6-1.9 cc/g), and fibre orientation angle (12-18°). The increase in yarn conductivity with higher twist was explained by greater contact between the fibrous assembly, that bridges electrically conductive pathways in the yarn structure. Twisted yarns (2-ply) were fabricated into electrode structures via embroidery, and a progressive increase in contact impedance was observed, followed by a stabilization and plateau within the range of measured impedance from 210 to 300 TPM. This observation was attributed to the decrease in the yarn specific volume, and subsequently the longitudinal diameter with increasing twist level, which decreased the contact area between the skin and electrode interface. The electrodes fabricated from varying yarn twist levels were used for electrocardiogram (ECG) measurement, and demonstrated comparable signal quality to standard gel electrodes. This experimental and theoretical work forms the basis in defining relationships between established yarn twist mechanics and geometrical properties with electrical properties. This can guide materials and design parameter selection of suitable conductive yarns for e-textiles used in biopotential monitoring 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 | 0.001 |
| 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".