Green-Solvent-Based Conductive Graphene Ink Prepared via Liquid-Phase Exfoliation of Graphite with Water-Soluble 2,6-Azulene-Based Copolymers as Stabilizers
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
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 .
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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