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Record W2998420802 · doi:10.1088/1361-6463/ab6518

Theory for polaritons in graphene photonic crystals in an applied magnetic field

2019· article· en· W2998420802 on OpenAlexafffund
M.S. Vasconcelos, M. G. Cottam

Bibliographic record

VenueJournal of Physics D Applied Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsGrapheneCondensed matter physicsPolaritonMagnetic fieldSurface plasmon polaritonPhotonic crystalTerahertz radiationMaterials scienceDispersion relationScatteringDispersion (optics)PlasmonSurface plasmonOpticsPhysicsOptoelectronicsNanotechnology

Abstract

fetched live from OpenAlex

Abstract The dispersion relations for bulk and surface plasmon-polaritons in a semi-infinite 1D photonic crystal interlayered with graphene are calculated in the presence of an applied magnetic field. The results are applied to SiO 2 as the constituent material in geometry where the layers are arranged in a periodic array with the same layer thickness. The static magnetic field is applied perpendicular to the plane of layers. Numerical results are presented for the modes in THz range, up to 10 THz, to illustrate the important role of the applied magnetic field on the graphene sheets in modifying the polariton dispersion curves, especially for magnetic fields of the order of 1 T. It is found that the polariton frequencies and band gaps have a sensitive dependence on the electron scattering rate parameter (and hence the applied magnetic field strength) in the graphene sheets. Electromagnetic retardation effects are fully taken into account for the bulk bands, while for the surface modes (which are shown to have a novel non-reciprocal propagation characteristics) it is convenient to focus on the regime where retardation is small.

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 score0.281
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.240
Teacher spread0.228 · 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

Citations10
Published2019
Admission routes2
Has abstractyes

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