Green and facile production of high-quality graphene from graphite by the combination of hydroxyl radicals and electrical exfoliation in different electrolyte systems
Bibliographic record
Abstract
; and exfoliation time, 3 h. At these conditions, the yield of the graphene product was 89.7%. TEM results revealed that the graphene product possessed the characteristic features of a thin-layer graphene sheet. XRD results showed that the graphene products still maintained the structures of carbon atoms or molecules. FT-IR and Raman results indicated that the products exhibited the characteristic peaks and the absorption peaks of graphene. AFM test results revealed that the layer number of graphene product obtained was about 2, while the layer numbers of the graphene products obtained from sodium hydroxide, PVP, DTAB and sodium chloride systems were 30, 20, 4 and 3, respectively, at the same experimental conditions. The observed exfoliation effect in the SDBS system was due to its good electrical conductivity, which was favorable for the formation of hydroxyl radicals in exfoliation. Furthermore, SDBS has good hydrophilic properties and can enable even dispersion of graphite in the system. These two effects facilitated the exfoliation of graphite to form good-quality graphene. SDBS as the electrolyte did not corrode the electrode, and it could be recycled; also, it does not pollute the environment and reduces the production cost, which is favorable for mass production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".