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Optical reflection and transmission properties of exfoliated graphite from a graphene monolayer to several hundred graphene layers

2010· article· en· W2094440368 on OpenAlexafffund
H. S. Skulason, Peter Gaskell, Thomas Szkopek

Bibliographic record

VenueNanotechnology · 2010
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsGrapheneMaterials scienceConductanceGraphiteMonolayerRefractive indexOptical conductivityReflection (computer programming)OpticsThin filmCondensed matter physicsOptoelectronicsNanotechnologyComposite materialPhysics

Abstract

fetched live from OpenAlex

The optical reflection contrast and optical transmission contrast of graphitic films on glass ranging in thickness from a monolayer to the limit of bulk graphite have been experimentally measured. For samples with more than 10 graphene layers where optical contrast quantization becomes difficult to observe, atomic force microscopy was used to measure the sample thickness. The visible optical reflection and transmission of thin graphitic films is found to depend strongly on the real component of the optical conductance per graphene layer, and comparatively weakly on the imaginary component of optical conductance. This observation in part explains the significant variation in the refractive index of graphene and graphite reported in the literature to date. Spectroscopic measurements reveal a strong dispersion in the optical conductance of even a 10 layer film, consistent with an imaginary conductance arising from virtual transitions at the band edges of the pi and sigma bands at the M and Gamma points, respectively.

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 score0.009
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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

Citations81
Published2010
Admission routes2
Has abstractyes

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