MétaCan
Menu
Back to cohort
Record W1917337311 · doi:10.1103/physrevb.84.205407

Raman spectroscopy of the internal strain of a graphene layer grown on copper tuned by chemical vapor deposition

2011· article· en· W1917337311 on OpenAlexaff
Victor Yu, Eric Whiteway, Jesse Maassen, Michael Hilke

Bibliographic record

VenuePhysical Review B · 2011
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsGrapheneRaman spectroscopyMaterials scienceChemical vapor depositionCopperStrain (injury)Substrate (aquarium)Fermi levelLayer (electronics)Graphene nanoribbonsChemical physicsNanotechnologyChemistryMetallurgyOptics

Abstract

fetched live from OpenAlex

Strain can be used as an alternate way to tune the electronic properties of graphene. Here we demonstrate that it is possible to tune the uniform strain of graphene simply by changing the chemical-vapor-deposition growth temperature of graphene on copper. Due to the cooling of the graphene on a copper substrate, we can induce a uniform compressive strain on graphene. The strain is analyzed by Raman spectroscopy, where a shift in the two-dimensional peak is observed and compared to our ab initio calculations of the graphene on a copper system as a function of strain, exhibiting a change in Fermi energy as a function of strain.

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.

How this classification was reachedexpand

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 score0.002
Threshold uncertainty score0.224

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)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.316
Teacher spread0.286 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2011
Admission routes1
Has abstractyes

Explore more

Same venuePhysical Review BSame topicGraphene research and applicationsFrench-language works237,207