Numerical Study on the Behavior of a Biphasic Temperature-sensitive Hydrogel Twisting Actuator:Biphasic temperature-sensitive hydrogel twisting actuator
Bibliographic record
Abstract
This research investigates the twisting behavior of a multi-layered hydrogel-based actuator comprised of two temperature-sensitive hydrogel layers and one elastomeric layer as the main core in different aspects due to temperature changes. Firstly, a suitable model is implemented for the temperature-sensitive hydrogel in the ABAQUS software using the UHYPER subroutine. Then, twisting behavior of the actuator due to temperature changes is simulated, and the twisting angle and reaction torque are obtained for the actuators. A comprehensive parameter study is conducted to investigate the effect of different material and geometric parameters on the performance of the actuator. These parameters include the cross-linking density of the hydrogel, the volumetric percentage of the hydrogel, the geometry of the interface line of the layers, and the aspect ratio (cross-section dimensions) of the actuator. The results identify the actuators with the maximum twisting angle and maximum reaction. In brief, the reaction torque generated at the ends of the twisting actuator is the maximum for the case where the interface line passes through the corners of the actuator cross-section. The results also show that for the maximum twisting angle of the studied actuator, the so-called interface line should not pass through the actuator corners.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".