Critical View on Buffer Layer Formation and Monolayer Graphene Properties in High-Temperature Sublimation
Bibliographic record
Abstract
In this work we have critically reviewed the processes in high-temperature sublimation growth of graphene in Ar atmosphere using enclosed graphite crucible. Special focus is put on buffer layer formation and free charge carrier properties of monolayer graphene and quasi-freestanding monolayer garphene on 4H-SiC. We show that by introducing Ar at different temperatures, TAr one can shift to higher temperatures the formation of the buffer layer for both n-type and semi-insulating substrates. A scenario explaining the observed suppresed formation of buffer layer at higher TAr is proposed and discussed. Increased TAr is also shown to reduce the sp3 hybridization content and defect densities in the buffer layer on n-type conductive substrates. Growth on semi-insulating substrates results in ordered buffer layer with significantly improved structural properties, for which TAr plays only a minor role. The free charge density and mobility parameters of monolayer graphene and quasi-freestanding monolayer graphene with different TAr and different environmental treatment conditions are determined by contactless terahertz optical Hall effect. An efficient annealing of donors on and near the SiC surface takes place in intrinsic monolayer graphene grown at 2000∘C, and which is found to be independent of TAr. Higher TAr leads to higher free charge carrier mobility parameters in both intrinsically n-type and ambient p-type doped monolayer graphene. TAr is also found to have a profound effect on the free hole parameters of quasi-freestanding monolayer graphene. These findings are discussed in view of interface and buffer layer properties in order to construct a comprehensive picture of high-temperature sublimation growth and provide guidance for growth parameters optimization depending on the targeted graphene application.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".