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Record W2021001395 · doi:10.1021/nl200390e

Graphene Epitaxy by Chemical Vapor Deposition on SiC

2011· article· en· W2021001395 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNano Letters · 2011
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsGrapheneChemical vapor depositionMaterials scienceRaman spectroscopyEpitaxyNanotechnologyGraphene nanoribbonsSublimation (psychology)WaferPhotoemission spectroscopyGraphene oxide paperScanning tunneling microscopeOptoelectronicsX-ray photoelectron spectroscopyChemical engineeringOptics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.020
GPT teacher head0.238
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it