Synthesis of Graphene By Electrochemical Exfoliation of Graphite in Aqueous Solution
Bibliographic record
Abstract
Of the various methods available for the preparation of graphene, thermal exfoliation via the modified Hummer's method provides a low cost, scalable process. However the process has several drawbacks: high temperature processing is required, the quality of the graphene is relatively poor, and chemical reduction is required to reduce the graphene oxide generated by the thermal exfoliation process. Electrochemical exfoliation can be carried out under ambient conditions, and by controlling the exfoliation conditions the properties of the graphene produced can be tuned. In this study we evaluate electrochemical exfoliation in aqueous solution, and in particular investigate the influence of the solution composition on the properties of the synthesized graphene. We find that the salt used influences the quality of the graphene, the surface functionality, thermal stability and the degree of oxidation. Graphene can be produced with a yield of order 65%, with a particle size of order 1 to 10 μm, a high proportion of single layer graphene, and a good electrical conductivity without the need for chemical reduction. Doping of heteroatoms such as nitrogen can be achieved by selecting a suitable salt solution. Furthermore, with a suitable salt a graphene with exceptional thermal stability can be prepared, with minimal mass loss up to 750°C and maintaining good conductivity after prolonged exposure to high temperature.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".