Graphene Epitaxy by Chemical Vapor Deposition on SiC
Bibliographic record
Abstract
We demonstrate the growth of high quality graphene layers by chemical vapor deposition (CVD) on insulating and conductive SiC substrates. This method provides key advantages over the well-developed epitaxial graphene growth by Si sublimation that has been known for decades. (1) CVD growth is much less sensitive to SiC surface defects resulting in high electron mobilities of ∼1800 cm(2)/(V s) and enables the controlled synthesis of a determined number of graphene layers with a defined doping level. The high quality of graphene is evidenced by a unique combination of angle-resolved photoemission spectroscopy, Raman spectroscopy, transport measurements, scanning tunneling microscopy and ellipsometry. Our measurements indicate that CVD grown graphene is under less compressive strain than its epitaxial counterpart and confirms the existence of an electronic energy band gap. These features are essential for future applications of graphene electronics based on wafer scale graphene growth.
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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 | 0.001 |
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".