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Record W2625169905 · doi:10.1088/2040-8986/aa945d

Characterization of surface-plasmon polaritons at lossy interfaces

2017· article· en· W2625169905 on OpenAlexaff
Nafiseh Sang-Nourpour, Benjamin R. Lavoie, Reza Kheradmand, Mohsen Rezaei, Barry C. Sanders

Bibliographic record

VenueJournal of Optics · 2017
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of Calgary
Fundersnot available
KeywordsSurface plasmon polaritonPermittivityMetamaterialPolaritonIsotropySurface plasmonOpticsCharacterization (materials science)PlanarMaterials scienceRefractive indexLossy compressionMaxwell's equationsPlasmonLocalized surface plasmonPhysicsDielectricOptoelectronicsClassical mechanicsComputer science

Abstract

fetched live from OpenAlex

Abstract We characterize surface-plasmon polaritons at lossy planar interfaces between one dispersive and one nondispersive linear isotropic homogeneous media, i.e. materials or metamaterials. Specifically, we solve Maxwell’s equations to obtain strict bounds for the permittivity and permeability of these media, such that satisfying these bounds implies surface-plasmon polaritons successfully propagate at the interface, and violation of the bounds impedes propagation, i.e. the field delocalizes from the surface into the bulk. Our characterization of surface-plasmon polaritons is valuable for checking the viability of a proposed application, and, as an example, we employ our method to falsify a previous prediction that surface-plasmon propagation through a surface of a double-negative refractive index medium occurs for any permittivity and permeability; instead, we show that propagation can occur only for certain medium parameters.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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 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

Citations15
Published2017
Admission routes1
Has abstractyes

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