Layer-by-Layer Deposition of Kraft Lignin and PEDOT:PSS
Bibliographic record
Abstract
Kraft lignin (KL) is a sustainable carbon-based substance with a potential use in photovoltaic materials. However, its conductivity is low, but it can be improved via incorporation with a conductive polymer, such as poly(3,4-ethylene dioxythiophene) (PEDOT): poly(styrenesulfonate) (PSS). This study examines the factors affecting the interaction of KL and PEDOT:PSS (PS) in a solution state using a quartz crystal microbalance with dissipation (QCM-D) and a stagnation point refractometer (SPAR). The results confirmed that aqueous environments, e.g., pH and ionic strengths, considerably affected particle size and zeta potential of KL and PS due to protonation, deprotonation, particle aggregation, and charge screening. The polymers exhibited the largest adsorbed mass and thickness at pH 6 and 10 mM NaCl on a solid surface, which was attributed to the relatively linear structure of PEDOT chains, exposing more adsorptive sites for interaction with KL. A 10 mM NaCl concentration facilitated the screening of charges on PS and KL surfaces, diminishing repulsive forces and enabling hydrophobic and cationic-π interaction, which led to increased adsorption. Contact angle and SEM investigations of the adsorbed layer revealed the water contact angle increasing and the morphology changing from a smoother layer to a porous surface, providing further evidence of adsorption. Furthermore, the conductivity was improved by the introduction of a PS adlayer on ITO glass when it was sandwiched between KL adsorbed layers. These findings provide insight into KL and PS interaction and suggest that KL can be used with PS for conductive materials, such as photovoltaics, imparting the waterproofness of the films.
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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".