Production of High-Concentration Graphene Dispersions in Low-Boiling-Point Organic Solvents by Liquid-Phase Noncovalent Exfoliation of Graphite with a Hyperbranched Polyethylene and Formation of Graphene/Ethylene Copolymer Composites
Bibliographic record
Abstract
We report in this paper the successful production of stable high-concentration graphene dispersions in low-boiling-point, low-polarity conventional organic solvents (chloroform and THF) by liquid-phase noncovalent exfoliation of graphite assisted with a hyperbranched polyethylene (HBPE) as the stabilizer. In the exfoliation process, HBPE adsorbs onto the surface of exfoliated graphene flakes, providing steric stabilization against their restacking. A systematic investigation on the effects of exfoliation conditions, including the solvent and the amounts of graphite and HBPE, has been conducted. Graphene dispersions with the concentration up to 0.18 mg/mL in chloroform and 0.045 mg/mL in THF have been obtained. It is also demonstrated that the dispersions can be further concentrated by solvent evaporation to give highly concentrated stable dispersions at 3.4 mg/mL. Through their characterizations with transmission electron microscopy, atomic force microscopy, and Raman spectroscopy, the majority of the graphene products is found to be high-quality, defect-free, few-layer graphene flakes with the layer number between 2 and 4 and the lateral dimension in the range of 0.2–0.5 μm. The dispersions can be fabricated into flexible conductive free-standing graphene films and be used to prepare graphene/ethylene copolymer composites through solution blending, which show significant enhancements in both thermal and mechanical properties.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".