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Record W1987228670 · doi:10.1063/1.4794169

Parallel plate waveguide with anisotropic graphene plates: Effect of electric and magnetic biases

2013· article· en· W1987228670 on OpenAlexaff
Ali Malekabadi, Serge A. Charlebois, Dominic Deslandes

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsUniversité du Québec à MontréalQuébec Science (Canada)Université de Sherbrooke
Fundersnot available
KeywordsGrapheneWaveguideCondensed matter physicsElectric fieldMaterials scienceIsotropyAnisotropyGraphene nanoribbonsMagnetic fieldTransverse planeOpticsPhysicsOptoelectronicsNanotechnology

Abstract

fetched live from OpenAlex

The performances of a parallel plate waveguide (PPWG) supported by perfect electric conductor (PEC)-graphene and graphene-graphene plates are evaluated. The graphene plate behavior is modeled as an anisotropic medium with both diagonal and Hall conductivities derived from Kubo formula. The PPWG modes supported by PEC-graphene and graphene-graphene plates are studied. Maxwell's equations are solved for these two waveguides, while the graphene layers are biased with an electric field only and with both electric and magnetic fields. It is shown that when both electric and magnetic biases are applied to the graphene, a hybrid mode (simultaneous transverse electric (TE) and transverse magnetic (TM) modes) will propagate inside the waveguide. The intensity of each TE and TM modes can be adjusted with the applied external bias fields. Study of different waveguides demonstrates that by decreasing the plate separation (d), the wave confinement improves. However, it increases the waveguide attenuation. A dielectric layer inserted between the plates can also be used to improve the wave confinement. The presented analytical procedure is applicable to other guiding structures having walls with isotropic or anisotropic conductivities.

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 score0.127
Threshold uncertainty score0.444

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 head0.006
GPT teacher head0.215
Teacher spread0.208 · 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

Citations30
Published2013
Admission routes1
Has abstractyes

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