Asymmetric PEDOT:PSS Trilayers as Actuating and Sensing Linear Artificial Muscles
Bibliographic record
Abstract
Abstract Soft ionic actuators and sensors have been intensively studied over the last 20 years. The bending trilayer configuration has been considered a standard architecture, allowing the development of new materials and their optimization. However, bending deformation remains low in force output and presents limited integration capabilities in fast emerging fields like humanoid robotics and actuating textiles. A generalizable architecture of asymmetric supercapacitor‐like trilayers is presented to develop open‐air linear artificial muscles that actuate and sense. First, tuning of electromechanical properties of asymmetric electrodes is performed separately by combining poly(3,4‐ethyl‐enedioxythiophene):poly(styrene sulfonate) with a polyethylene oxide network and 1‐ethyl‐3methylimidazolium bis(trifluoromethanesulfonyl)imide as additives. By varying their content, electronic conductivity can be tuned between 20 and 457 S cm−1, Young's modulus from 1.5 to 0.27 GPa, and volumetric charge density increased by 36%. Asymmetric trilayers are then fabricated using a simple layer stacking process by selecting the optimal combination of materials according to an electromechanical model. Linear strain of 0.5% is obtained in 30 s under ±2 V with 70% of the deformation within 5 s and blocking stress as high as 0.3 MPa. When mechanically stimulated, these asymmetric trilayers demonstrate linear sensing as well, with a sensitivity of 0.38 mV %−1.
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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) | 0.000 | 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".