Template-Assisted Self-Assembly of Conductive Polymer Electrodes for Ionic Electroactive Polymers
Bibliographic record
Abstract
Ionic electroactive polymers (EAP) can greatly aid in biomedical applications where micro-sized actuators are required for delicate procedures. Since these types of actuators generally require platinum or gold metallic electrodes, they tend to be expensive, susceptible to wear and tear, and have limited actuation strains. In this study, Nafion is used for the polymeric actuating membrane and is sandwiched between two self-assembled electrodes made from conductive polymers (CP) which allow for a cost-effective fabrication and can theoretically achieve higher actuation strains as they are not constrained to metallic electrodes. This study presents a novel method for fabrication of polymeric actuators made from Nafion by treating the surface of Nafion precursor membrane with NaOH to activate its surface without activating its core. Roughening of the membrane is not required to improve the adhesion between the electrodes and the membrane as the polymeric electrodes interlock with the Nafion membrane at the interfacial layer during the polymerization of CP electrodes. By adjusting concentration and the time of the surface activated fabrication treatment (SAFT), we can control the interfacial layer’s thickness and the polymeric electrode’s growth pattern. The electrodes in this study are made from either polyaniline (PANI) or poly(3,4-ethylenedioxythiophene) (PEDOT) and the SAFT duration allows for PANI electrodes to grow linearly, orthogonally, or randomly along the Nafion surface. The formation of these electrodes is verified by standard electron microscopy (SEM) and by the decrease in sheet resistance after each polymerization cycle. The EAPs that were made with PEDOT shows some negative deformation at the start of actuation. This observation was not found in the actuators made with PANI. Both PANI and PEDOT showed higher actuation strains than the typical all polymeric ionic EAPs and so this fabrication method may be beneficial in the development of grippers for neurosurgical applications.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".