Printable, adhesive, and self-healing dry epidermal electrodes based on PEDOT:PSS and polyurethane diol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Printable, self-healing, stretchable, and conductive materials have tremendous potential for the fabrication of advanced electronic devices. Poly(3,4-ethylenedioxithiopene) doped with polystyrene sulfonate (PEDOT:PSS) has been the focus of extensive research due to its tunable electrical and mechanical properties. Owing to its solution-processability and self-healing ability, PEDOT:PSS is an excellent candidate for developing printable inks. In this study, we developed printable, stretchable, dry, lightly adhesive, and self-healing materials for biomedical applications. Polyurethane diol (PUD), polyethylene glycol, and sorbitol were investigated as additives for PEDOT:PSS. In this study, we identified an optimal printable mixture obtained by adding PUD to PEDOT:PSS, which improved both the mechanical and electrical properties. PUD/PEDOT:PSS free-standing films with optimized composition showed a conductivity of approximately 30 S cm −1 , stretchability of 30%, and Young’s modulus of 15 MPa. A low resistance change (<20%) was achieved when the strain was increased to 30%. Excellent electrical stability under cyclic mechanical strain, biocompatibility, and 100% electrical self-healing were also observed. The potential biomedical applications of this mixture were demonstrated by fabricating a printed epidermal electrode on a stretchable silicone substrate. The PUD/PEDOT:PSS electrodes displayed a skin-electrode impedance similar to commercially available ones, and successfully captured physiological signals. This study contributes to the development of improved customization and enhanced mechanical durability of soft electronic materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it