Synthesis of Graphene and Graphene Films with Minimal Structural Defects
Bibliographic record
Abstract
Graphene is a carbon material with extraordinary properties that has been drawing a significant amount of attention in the recent decade. High-quality graphene can be produced by different methods, such as epitaxial growth, chemical vapor deposition, and micromechanical exfoliation. The reduced graphene oxide route is, however, the only current approach that leads to the large-scale production of graphene materials at a reasonable cost. Unfortunately, graphene oxide reduction normally yields graphene materials with a high defect density. Here, we introduce a new route for the large-scale synthesis of graphene that minimizes the creation of structural defects. The method involves high-quality hydrogen functionalization of graphite followed by thermal dehydrogenation. We also demonstrated that the hydrogenated graphene synthesis route can be used for the preparation of high-quality graphene films on glass substrates. A reliable method for the preparation of these types of films is essential for the widespread implementation of graphene devices. The structural evolution from the hydrogenated form to graphene, as well as the quality of the materials and films, was carefully evaluated by Raman spectroscopy.
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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) | 0.002 | 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".