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Record W3092299307 · doi:10.1088/1361-648x/abbea5

Magnon-polaritons in graphene/gyromagnetic slab heterostructures

2020· article· en· W3092299307 on OpenAlexafffund
M.S. Vasconcelos, M. G. Cottam, D.H.A.L. Anselmo

Bibliographic record

VenueJournal of Physics Condensed Matter · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsWestern University
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCondensed matter physicsMagnonGraphenePolaritonAntiferromagnetismFerromagnetismSlabFermi levelMaterials sciencePhysicsMagnetic momentFermi surfaceElectronNanotechnologyQuantum mechanicsSuperconductivityGeophysics

Abstract

fetched live from OpenAlex

We present a theoretical study for the surface magnon-polaritons in structures formed by graphene layer(s) on an insulating gyromagnetic medium (that can be either ferromagnetic or antiferromagnetic) surrounded by vacuum. We consider different doping levels to vary the Fermi energies in the graphene, including both semi-infinite and slab magnetic samples. Our results reveal a strong influence, exerted by the presence of graphene, on the surface magnon-polariton modes. The effects include control of the group velocities for the modes as the Fermi energies of the graphene sheet are varied, modified nonreciprocal and reciprocal mode propagation properties depending on the type of magnetic material, and distinct localization properties for the emerging surface modes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.252
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2020
Admission routes2
Has abstractyes

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