Electrochemical Exfoliation of Graphite to Graphene-Based Nanomaterials
Bibliographic record
Abstract
Here, we report on a new automated electrochemical process for the production of graphene oxide (GO) from graphite though electrochemical exfoliation. The effects of the electrolyte and applied voltage were investigated and optimized. The morphology, structure and composition of the electrochemically exfoliated GO (EGO) were probed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), energy dispersive X-ray (EDX) spectroscopy, X-ray photoelectron spectroscopy (XPS), FTIR spectroscopy and Raman spectroscopy. Important metrics such as the oxygen content (25.3 at.%), defect density (ID/IG = 0.85) and number of layers of the formed EGO were determined. The EGO was also compared with the GO prepared using the traditional chemical method, demonstrating the effectiveness of the automated electrochemical process. The electrochemical properties of the EGO, CGO and other carbon-based materials were further investigated and compared. The automated electrochemical exfoliation of natural graphite powder demonstrated in the present study does not require any binders; it is facile, cost-effective and easy to scale up for a large-scale production of graphene-based nanomaterials for various applications.
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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".