Modeling and optimization of ionic polymer gel actuators
Bibliographic record
Abstract
The electro-active behavior of ionic polymer gel was modeled and the optimum condition of decision parameters that maximize the deflection of gel was investigated. An actuation model characterizing the bending deformation of polymer gel under electric field was proposed considering the chemo-electro-mechanical parameters. In the modeling, swelling or shrinking phenomenon due to the difference of concentration at the boundary between the gel and solution was considered first before the electric field is applied. Then, bending deformation under the concentration difference of ions was calculated. Differential osmotic pressure at the boundary of gel and solution determine the degree of swelling or shrinking of gel. From this actuation behavior, strain or deformation of gel is calculated. To find the optimum conditions for the deformation of gel, a non-linear constrained optimization model was proposed, where the equation for bending deflection of gel is used as the objective function and the relationship among the decision variables and the range of the variables are used as constraints. In the optimization model, electric voltage, thickness of gel, concentration of polyion in the gel, ion concentration in the solution and degree of cross-linking in gel were considered as decision variables. The predictions by the proposed model were compared with experimental data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".