Chemical vapour deposition of graphene: layer control, the transfer process, characterisation, and related applications
Bibliographic record
Abstract
Graphene, one of the most promising two-dimensional (2D) nanomaterials, has gained substantial attention in several areas of materials science. Due to its unique mechanical, electrical, optical, and thermal properties, graphene-based materials have triggered both numerous fundamental studies and technological applications. Out of several synthetic methods, chemical vapour deposition (CVD) has emerged as one of the most promising methods for the production of large areas of high quality single-crystal graphene. This review introduces the fundamental growth mechanisms of CVD graphene, alongside the various parameters and substrates employed in this process. Furthermore, new developments in the CVD synthesis of monolayer and few-layer graphene are presented, as well as advanced techniques for analysing the fine structure and properties of graphene. Moreover, a detailed discussion of the transfer processes used for practical applications of CVD graphene is provided, with emphasis on their fundamental aspects. This review concludes with an outlook on presently challenging issues, prospects and applications of graphene.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".