Green-Solvent-Based Conductive Graphene Ink Prepared via Liquid-Phase Exfoliation of Graphite with Water-Soluble 2,6-Azulene-Based Copolymers as Stabilizers
Bibliographic record
Abstract
Liquid-phase exfoliation (LPE) of graphite is a promising strategy to prepare stabilized graphene inks for various applications. However, the stabilizing agents that have been used generally suffer from poor electrical properties, harming the properties of the resulting composites once they are deposited on a surface. In this study, we investigate the use of a water-soluble, conjugated 2,6-azulene-based copolymer as a stabilizing agent for aqueous graphene dispersion. Due to the presence of azulene within the copolymer main chain, the copolymer exhibits proton responsiveness and conductivity in the solid state upon protonation. The LPE process was optimized in a mixture of water and ethanol (1:1), and stable inks can be obtained after 2 h of sonication using a copolymer concentration as low as 0.09 mg·mL –1 . The resulting inks were deposited on various surfaces and characterized using Raman spectroscopy, UV–visible spectroscopy, and atomic force microscopy (AFM), which led us to conclude that few- and multilayer graphenes were obtained. Finally, the optimized ink was printed on glossy photographic paper using a modified FDM 3D printer to create printed circuits with a sheet resistance of 60 kΩ·□ –1 .
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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) | 0.000 | 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 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".