Liquid Phase Exfoliation of Graphite into Graphene Nanoplatelets using Graphene Oxide as Surfactant
Bibliographic record
Abstract
In this study, graphene nanoplatelets (GNP) are prepared using graphene oxide (GO), a derivative of graphene, as the dispersing agent in the liquid phase exfoliation of raw fake graphite. Graphene oxide has similar properties to that of graphene, however, it contains functional groups such as epoxides, hydroxyls and carboxyls. This added functionality makes GO amphiphilic and a suitable dispersing agent in water. In order to prepare stable GNP dispersions during exfoliation, the sheets need to overcome the van der Waals attractive forces and π-π interactions. Preliminary research shows that more widely stable GNP dispersions can be achieved by adjusting the pH of dispersions to 10. The major variables influencing the exfoliation of graphite are: the concentration of GO and graphite, along with shear rate, and time. Using dimensional reasoning and according to the factorial design of experiments, we obtain a correlation to optimize the GNP yield by a response surface methodology. The exfoliated GNP content in the dispersions is determined from UV-vis spectroscopy, via a GO/GNP standard calibration curve. Furthermore, the exfoliated GNP is characterized using, atomic force microscopy, Raman spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. Finally, the GO/GNP dispersions are then concentrated and used to synthesize graphene hydrogels. The resulting hydrogels are used as an electrode material for making flexible supercapacitors and their performance is evaluated.
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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".