Screen printable flexible conductive nanocomposite polymer with applications to wearable sensors
Bibliographic record
Abstract
We have developed a conductive nanocomposite polymer that possesses both good conductivity and flexibility, and screen printed it onto fabric to realize wearable flexible electrodes and electronic routing. The conductive polymer consists of dispersed silver nanoparticles (90~210nm) in a screen printable plastisol polymer. The conductive polymer is conductive for weight-percentages above approximately 61 wt-% of Ag nanoparticles, and has a resistivity of 2.1210-6 ohm·m at 70 wt-% of Ag nanoparticles. To test the screen printed conductive polymer’s flexibility and its effect on conductivity, we measured the resistivity of the Ag-doped composite polymer at different bending angles (-90˚ ~ 90˚) with a 10° step angle at different wt-% of silver particles, and compared the results. We also tested washability of the screen printed conductive polymer as applied to fabric for long-term use in wearable sensors systems. We also used the screen printed Ag composite polymer to realize an example wearable system. Flexible wearable dry electrocardiogram (ECG) electrodes were developed and ECG signal was measured via the electrodes. The sensing ECG electrodes (3mm diameter circle) were chloridized to form Ag/AgCl electrodes. We measured an ECG signal using a simple right-leg driven ECG circuit and observed normal ECG signals even without application of electrolyte gel.
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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".