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Eccentric coaxial gap-plasmon aperture arrays for enhanced extraordinary optical transmission and applications

2009· article· en· W2115285474 on OpenAlexaff
Reuven Gordon, A. I. K. Choudhury, F. Eftekhari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPlasmonCoaxialConcentricOpticsAperture (computer memory)Finite-difference time-domain methodPolarization (electrochemistry)Materials scienceSurface plasmonPhysicsOptoelectronicsAcousticsTelecommunicationsGeometryComputer science

Abstract

fetched live from OpenAlex

The eccentric coaxial metal waveguide is similar to concentric coaxial structure, which has been studied extensively in the plasmonics community. Compared to the concentric structure, the eccentric structure has many benefits, including: (1)stronger subwavelength field localization around the narrowest gap, (2) improved optical coupling to the lowest order mode due to linear polarization, and (3) an increased effective index due to the gap plasmon. Yet, there have been no reports on the plasmonic aspects of this structure so far. This paper investigates on focussed-ion beam fabricated arrays of eccentric coaxial structures. An effective index method is used to analyze the field localization and enhancement. The gap plasmon is assumed dominant over conformal effects which can play an important role. Results show that the analytic theory agrees very well with a commercially-available finite-difference mode-solver(FDMS). Based on the simulations, strong modification to the extraordinary optical transmission peak is expected, with increased transmission over the concentric coaxial and cylindrical structures.

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: 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
Metaresearch0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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