Fabrication, modeling and optimization of an ionic polymer gel actuator
Bibliographic record
Abstract
The modeling of the electro-active behavior of ionic polymer gel is studied and the optimum conditions that maximize the deflection of the gel are investigated. The bending deformation of polymer gel under an electric field is formulated by using chemo-electro-mechanical parameters. In the modeling, swelling and shrinking phenomena due to the differences in ion concentration at the boundary between the gel and solution are considered prior to the application of an electric field, and then bending actuation is applied. As the driving force of swelling, shrinking and bending deformation, differential osmotic pressure at the boundary of the gel and solution is considered. From this behavior, the strain or deflection of the gel is calculated. To find the optimum design parameter settings (electric voltage, thickness of gel, concentration of polyion in the gel, ion concentration in the solution, and degree of cross-linking in the gel) for bending deformation, a nonlinear constrained optimization model is formulated. In the optimization model, a bending deflection equation of the gel is used as an objective function, and a range of decision variables and their relationships are used as constraint equations. Also, actuation experiments are conducted using poly(2-acrylamido-2-methylpropane sulfonic acid) (PAMPS) gel and the optimum conditions predicted by the proposed model have been verified by the experiments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".