A highly stable PI-PU conductive copolymer for wearable
Bibliographic record
Abstract
Smart textiles require conductors that maintain electrical stability under repeated deformation and laundering while preserving textile-like softness. Here, we develop a poly(imide-urethane) (PI–PU) copolymer that combines the high-temperature dimensional stability, abrasion, and dielectric properties of polyimide (PI) with the elasticity of polyurethane (PU). The PI–PU copolymer was synthesized from polytetramethylene ether glycol (PTMEG 3000), 3,3′,4,4′-benzophenonetetracarboxylic dianhydride (BTDA), and m-xylylene diisocyanate (XDI) in N-methyl-2-pyrrolidone (NMP) under nitrogen; optional melamine crosslinking improved mechanical robustness. Conductive pastes were obtained by dispersing 62 wt% Ag powder in the PI–PU using a high-speed vacuum defoaming mixer. Printed traces on TPU film exhibited a resistivity of 3.5 × 10 -5 Ω·cm and negligible resistance change after 20% elongation and recovery (0–8% variation, best case 0%). After AATCC 135 laundering 50 times, the resistance on knitted and woven laminates increased modestly from ∼2.0–2.8 Ω to 5.1–5.5 Ω, indicating durable conductivity and superior wash-wear resistance compared with a neoprene-based benchmark. The results demonstrate that PI–PU/Ag is a promising platform binder for printed circuits in wearable electronics, enabling stable signal transmission under cyclic strain and laundering. • A new elastic conductive slurry from polymer polymerization, material ratio, homogenization technology, and process design. • The basic slurry in this study is a high-strength elastic PI-PU material with high hardness on the surface, and surface protection is required during use. • The PI-PU elastic conductive paste in this study has lower resistivity (3.5 × 10 -5 Ω‧cm). • The PI-PU copolymer has a lower elastic resistance variation rate (<10%) • The PI-PU conductive slurry has better washing/ resistance (50 times).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".