Tailoring the Growth Rate and Surface Facet for Synthesis of High-Quality Continuous Graphene Films from CH<sub>4</sub> at 750 °C via Chemical Vapor Deposition
Bibliographic record
Abstract
Previous work has demonstrated that the growth temperature for the chemical vapor deposition of graphene can be decreased by tailoring the precursor composition. Here, we fix the precursor as CH 4 and instead explore the effects of the catalyst facet, synthesis conditions, and growth rate on the quality of graphene grown at reduced temperature on Cu. We find that in order to obtain graphene films with low defect density, it is critical to maintain a slow growth rate, which is achieved using a low CH 4 partial pressure. Furthermore, growth on Cu(111) is more efficient than on other low index Cu facets. Using optimized growth conditions, we achieve high-quality continuous monolayer graphene films, with a low nucleation density of 4 × 10 –2 μm –2 and a negligible Raman D:G ratio, at 750 °C, which is 125 °C cooler than previous reports achieving full graphene coverage on Cu. The field-effect mobility is 2600 cm 2 V –1 s –1 at a carrier concentration of 10 12 cm –2, and low-energy electron microscopy indicates that there is one predominant graphene crystallographic orientation with respect to the underlying Cu(111). These results demonstrate that by tailoring the catalyst facet and growth rate, the temperature required to achieve high-quality continuous films, using a given precursor, can be reduced without sacrificing the excellent properties 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) | 0.000 | 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) | 0.001 | 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".