Highly Conducting, Transparent PEDOT:PSS Polymer Electrodes from Post‐Treatment with Weak and Strong Acids
Bibliographic record
Abstract
Abstract The origin of high conductivity in polymer electrodes based on poly(3,4‐ethylene dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) is investigated and the resilience against water exposure is tested. Post‐treatment with weak and strong acids, namely, hydrochloric acid (HCl), formic acid (HCOOH), nitric acid (HNO3), and sulfuric acid (H2SO4), is performed and compared to the commonly used ethylene glycol treatment. PEDOT:PSS electrodes with electrical conductivities of up to ≈3000 S cm−1 and high transmittance are obtained. The underlying mechanisms for enhanced conductivity are elucidated by means of electrical (4‐point probe), optical (UV‐Vis spectroscopy), compositional (X‐ray photo‐electron spectroscopy), and structural (grazing‐incidence wide‐angle X‐ray scattering, GIWAXS) characterizations. Selective PSS removal and structural rearrangement of PEDOT‐rich domains due to an enhanced lamellar stacking is identified as major influence on the improvement in electrical conductivity. This beneficial high order is evidenced via additional signals in the GIWAXS patterns, which are altered by subsequent H2O treatment. The PSS removal and structural rearrangement is linked to the acids' strength and dielectric constant. High conductivities are reached by efficient PSS removal via HNO3 or H2SO4 treatment with the drawback of high sensitivity against H2O. By contrast, HCl and HCOOH treatment obtaining a medium enhanced conductivity differ in the amount of PSS removal but show higher H2O resistance.
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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".