Fabrication of ionic polymer-metal composite actuators with durable and quality-enhanced sputtered electrodes
Bibliographic record
Abstract
In this study, a sputter-based procedure was developed for fabricating IPMC's wrinkled metal stack electrodes. Micro-texturing pre-treatments such as sandblasting and manual sandpapering followed by plasma treatment were individually used to wrinkle the surfaces of the Nafion membranes and to enhance the adhesion between membranes and sputtered electrodes. It was analytically indicated that increasing surface roughness increases the endurance of electrodes against the tensile stress. The effects of pre-treatment processes on the mechanical properties of the Nafion membranes and strength of the electrodes were investigated by means of the tensile test and theoretically discussed. The results show that micro-texturing of the surface of the membrane, resulting in a decrease in the elastic modulus and an increase in the yield stress. The surface roughening by sandblasting technique followed by plasma treatment before the sputtering process resulted in high-quality wrinkled electrodes, which can easily tolerate tensile strain caused by volume expansion of Nafion or IPMC bending, and significantly reduce cracks and prevent electrodes peeling off phenomenon. The proposed IPMC manufacturing approach is short, low cost, controllable, and reproducible. Investigation of the electromechanical performance of the IPMC actuator exhibits an average bending deformation of 115° and an average blocking force of 75 mN under a driving voltage of 5 V.
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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".