One-Pot Electrochemical Exfoliation and Functionalization of Graphene Sheets
Bibliographic record
Abstract
Graphene is currently attracting a lot of interest because of their potential applications in several different fields. Various approaches are being developed to prepare it at large-scale, by green method and cost-effectively. To this end, graphene sheets can be prepared by electrochemical exfoliation of graphite in aqueous electrolyte. In this work, graphene was produced by electrochemical exfoliation of graphite in acidic and neutral electrolytes. With this procedure, graphene sheets with low oxygen content are produced. Graphene sheets were also functionalized during the electrochemical exfoliation process by using appropriate reagents and experimental conditions. The resulting materials were characterized by several techniques such as Fourier transform infrared, X-ray photoelectron and Raman spectroscopy, thermogravimetric analysis, elemental analysis, electronic conductivity measurements and electrochemical techniques.
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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".