Active cooling techniques for coiled nylon actuators
Bibliographic record
Abstract
Low-cost, highly versatile thermally driven coiled nylon actuators have demonstrated great tensile stress (>10 MPa) and large stroke (>5%). The work density of this material is 100 times greater than mammalian muscle, which makes coiled nylon actuators good candidates for applications in soft robotics. Similar to other thermally driven actuators, heat transfer rate limits their frequency response and benefits from extensive cooling. The cooling time for these actuators is dependent on heat conduction and convection. For instance, an 860 µm multi-stranded coiled nylon actuator is limited to 0.2 Hz frequency of actuation, above which tensile stroke drops due to heat accumulation. We analyzed the thermal behavior of silver-coated nylon actuators and investigated the actuation under air flow, in hydrogel, and in water, to improve the frequency response. An improved frequency response was observed under air flow (compressed air) in relation to still air. The measured heat transfer coefficient under air flow reaches 137 W/m2 /K enabling 5% strain at 0.8 Hz. The fastest frequency responses were observed in water and within hydrogel, where the nylon actuators demonstrated ~10% strain at 1 Hz (add water and hydrogel heat transfer coefficient). The application of a hydrogel coated actuator is demonstrated through an actuated 3D printed finger, which makes use of antagonistic coiled nylon actuators.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".