Poly(3,4-ethylenedioxythiophene):Poly(styrene sulfonate) Inkjet Inks Doped with Carbon Nanotubes and a Polar Solvent: The Effect of Formulation and Adhesion on Conductivity
Bibliographic record
Abstract
A conductive aqueous polymer suspension of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate), or PEDOT:PSS, was used as the basis for an inkjet-printable and translucent conductive material. Several types of surfactants were used to achieve suitable particle sizes for inkjet printing, with Zonyl™ FS-300 non-ionic surfactant providing suitable surface tension, stability and dispersion. Viscosity was controlled using water and glycerol. Glycerol was also included as a humectant. 10 w/w% polar solvent (dimethyl sulfoxide) was used to increase conductivity, as a co-solvent and as a viscosity modifier. Carbon nanotubes, both single- and multi-walled, were dispersed in the ink to further improve its conductivity. The optimized ink was printed onto coated photo-paper and cellulose acetate (CA) substrates and characterized for ink layer thickness and conductivity. The effect of paper folding and peeling of an adhesive strip from the ink surface on the conductivity of the printed samples was also characterized. Optical microscopy showed that the conductive ink was contained almost entirely in the pores of the photo-paper coating layer and fibres, but remained as a film on the CA surface. In the case of photo-paper, failure occurred primarily at the coating–paper interface. The cohesive failure compromised the conductivity of the PEDOT:PSS layer contained in the coating. In the case of CA (within the coating layer), the PEDOT:PSS film's conductivity was not significantly affected by folding or peeling. This suggests that PEDOT:PSS is a robust conductive material well-suited to applications requiring significant flexibility.
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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".