Selective Process To Extract High-Quality Reduced Graphene Oxide Leaflets
Bibliographic record
Abstract
One popular approach to prepare graphene on a large scale consists of converting graphene oxide (GO) into reduced graphene oxide (RGO). However, this procedure yields graphene flakes with various amounts of oxygenated defects. Using a double liquid phase extraction technique (DLPE) assisted by cholesterol-based polymers, we demonstrate that the RGO flakes of the highest quality, i.e., those with the highest π-conjugated network and with the lowest number of oxygenated defects, can be selectively extracted in isooctane, while lower quality flakes remain in water. Thus, it is possible to collect single-layer graphene sheets of high quality, as characterized by Raman spectroscopy (ID/IG below 0.2) starting from a RGO containing a heterogeneous mixture of leaflets (ID/IG ∼ 1.3). The high quality of the RGO leaflets extracted by DLPE was also confirmed by X-ray photoelectron spectroscopy, photoluminescence, Fourier transform infrared spectroscopy, X-ray diffraction, and atomic force microscopy. The conductivity of the films prepared with DLPE RGO flakes is an order of magnitude higher than the one of the films prepared with as-prepared RGO. The thermal stability of the extracted leaflets, as measured by thermal gravimetric analysis, is also greatly enhanced. Thus, sorting RGO by DLPE is a valuable process for the large-scale production of high-quality graphene.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".