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Record W2260149901 · doi:10.1364/oe.23.030227

Wedge and gap plasmonic resonances in double nanoholes

2015· article· en· W2260149901 on OpenAlexafffund
Yuanyuan Chen, Abhay Kotnala, Li Yu, Jiasen Zhang, Reuven Gordon

Bibliographic record

VenueOptics Express · 2015
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Victoria
FundersNational Key Research and Development Program of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsOpticsWedge (geometry)PlasmonCurvatureResonance (particle physics)Materials scienceExtraordinary optical transmissionPhysicsNonlinear opticsSurface plasmonSurface plasmon polaritonAtomic physicsLaserGeometry

Abstract

fetched live from OpenAlex

We study the plasmonic resonances of double nanoholes (DNHs) in metal films. These apertures exhibit the usual gap-mode Fabry-Pérot resonances, where the zeroth order resonance is determined by the waveguide cut-off and the first order resonance shows sensitivity to the film thickness. An additional wedge resonance is observed, which is sensitive to the curvature of the cusps in the DNHs, analogous to the wedge modes of single wedges. While the gap mode intensity increases dramatically with decreasing gap-width, the wedge mode intensity saturates since its field enhancement arises from the curvature of the metal film, like cylindrical Sommerfeld waves. Experimental transmission spectra agree well with finite-difference time-domain simulations showing these separate resonances. The controlled design of these resonances is critical for applications including optical tweezers, nonlinear conversion, sensing and spectroscopy.

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 score0.001
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 communication0.0000.001
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 head0.049
GPT teacher head0.271
Teacher spread0.222 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

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