Highly stretchable PEDOT:PSS organic electrochemical transistors achieved via polyethylene glycol addition
Bibliographic record
Abstract
Abstract Organic electrochemical transistors (OECTs) are widely used in biosensing and bioelectronics, due to their ability to convert ionic signals into electronic signals and their high transconductance. Stretchable OECTs are particularly suited for on-skin and on-organ bioelectronics, since they are able to record or transmit signals under mechanical strain. Most stretchable OECTs are based on the conducting polymer poly(3, 4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS), which needs to be appropriately processed to yield stretchable films. Here we report stretchable OECTs that are obtained by modifying the mechanical properties of PEDOT:PSS films via the addition of low-molecular weight polyethylene glycol (PEG), which acts as a plasticizer. The presence of PEG in the films prevents the formation of cracks under strain while maintaining a high electrical conductivity, thus resulting in improved electromechanical properties. In particular, the additon of PEG leads to a higher channel thickness and increased ion mobility in the films, thus resulting in stretchable OECTs with high transconductance and fast response time. This work shows that high stretchability, high transconductance and fast repsonse time can be simultaneously obtained in OECTs, paving the way for their applications in conformable devices at the human-machine interface.
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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) | 0.001 | 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".