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Record W2556892520 · doi:10.1515/nanoph-2016-0120

Optical transmission theory for metal‐insulator‐metal periodic nanostructures

2016· article· en· W2556892520 on OpenAlexaff
André-Pierre Blanchard-Dionne, Michel Meunier

Bibliographic record

VenueNanophotonics · 2016
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDielectricMaterials scienceDiffractionOpticsCondensed matter physicsNanostructureClassification of discontinuitiesScatteringComputational physicsPhysicsOptoelectronicsNanotechnologyMathematical analysisMathematics

Abstract

fetched live from OpenAlex

Abstract A semi‐analytical formalism for the optical properties of a metal‐insulator‐metal periodic nanostructure using coupled‐mode theory is presented. This structure consists in a dielectric layer in between two metallic layers with periodic one‐dimensional nanoslit corrugation. The model is developed using multiple‐scattering formalism, which defines transmission and reflection coefficients for each of the interface as a semi‐infinite medium. Total transmission is then calculated using a summation of the multiple paths of light inside the structure. This method allows finding an exact solution for the transmission problem in every dimension regime, as long as a sufficient number of diffraction orders and guided modes are considered for the structure. The resonant modes of the structure are found to be related to the metallic slab only and to a combination of both the metallic slab and dielectric layer. This model also allows describing the resonant behavior of the system in the limit of a small dielectric layer, for which discontinuities in the dispersion curves are found. These discontinuities result from the out‐of‐phase interference of the different diffraction orders of the system, which account for field interaction for both inner interfaces of the structure.

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 score0.230
Threshold uncertainty score0.660

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 head0.010
GPT teacher head0.234
Teacher spread0.223 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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