Combined hydrogel and elastomer coatings for cooling supercoiled nylon actuators
Bibliographic record
Abstract
Silver-coated nylon actuators - a form of artificial muscle - are potential candidates in biomedical applications, , as they yield a large strain (5-20%+), high force (>20 MN/m2 ), are compact and low-cost. But the on skin or internal application of these thermal actuators is limited by the heat released and the high activation temperatures (typically >80°C), which could cause tissue damage. We present a hybrid coating that reduces the temperature at the interface of the nylon actuator and surrounding tissue/skin, while maintaining the inner nylon actuator activation temperature. By taking advantage of the high heat capacity of water-swollen polyacrylamide (PAAm) hydrogel and the low thermal conductivity of silicone elastomer, we develop a hybrid coating for nylon actuators that provides effective heat dissipation and encapsulation without impacting strain. Hydrogel is used to absorb and dissipate heat. Using it alone dissipates heat quickly, and in turn, excess power is needed to achieve full strain. Therefore, silicone is used as a thin, inner insulating layer to retain the heat, so full strain can be achieved without excess power. We examined the strain and temperature of uncoated nylon fibres (control), single-layered silicone-coated nylon fibres, single-layered hydrogel nylon fibres and hybrid-coated (inner layer of silicone, outer layer of hydrogel) nylon fibres. At a constant current of 0.55 A, the mean strains of hybrid coated nylon fibres (6.0 %), and silicone coated nylon fibres (5.5%) were comparable to uncoated nylon fibres (5.3%). The mean strain for the hydrogel-coated nylon fibres was considerably lower (1.4%). The hybrid coating effectively maintains the fibre temperature (80-87°C) while cooling the outer surface (hydrogel) of the hybrid-coated nylon fibre (30-35°C). This provides a possible solution for use of these actuators in temperature sensitive 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 | 0.000 |
| 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".