Bi-metallic stitched e-textile sensors for sensing salinized liquids
Bibliographic record
Abstract
Sensing in e-textiles has generated great interest in a wide range of applications, including wetness detection. A common application for wetness detection sensors is the detection of urine in absorbent products such as diapers. However, unlike water, salinized liquids like urine accelerate the process of chemical reaction of integrated conductors (especially when exposed to electrical current), limiting the lifespan of e-textile sensors. This paper explores an approach to extending the lifespan of conductive thread sensors by blending copper filament with silver threads, and a comparative bench test of the effect of adding copper on the functional lifespan of a wetness sensor when exposed to saline. We find that while the bi-metallic sensor is effective in lengthening the lifespan of the conductive traces, it is likely due to the increased metal content rather than selective reaction of one metal to preserve the other. We make suggestions for further optimizing sensors.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".