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Record W2051156144 · doi:10.1364/oe.18.022255

Experimental and numerical analysis on the optical resonance transmission properties of nano-hole arrays

2010· article· en· W2051156144 on OpenAlexaff
Mohamadreza Najiminaini, Fartash Vasefi, Bożena Kamińska, Jeffrey J. L. Carson

Bibliographic record

VenueOptics Express · 2010
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsLawson Health Research InstituteSimon Fraser University
Fundersnot available
KeywordsOpticsMaterials scienceExtraordinary optical transmissionFinite-difference time-domain methodElectron-beam lithographyResonance (particle physics)Nano-Rigorous coupled-wave analysisTransmission (telecommunications)OptoelectronicsPhysicsPlasmonSurface plasmonTelecommunicationsNanotechnologySurface plasmon polaritonDiffraction gratingAtomic physicsResist

Abstract

fetched live from OpenAlex

In this paper, we present experimental and numerical analysis on Extraordinary Optical Transmission (EOT) or optical resonance transmission through various nano-hole arrays constructed from an optically thick metal film within the visible and near infra-red spectrum. Nano-hole arrays with different geometrical parameters (hole size, hole shape, and hole periodicity) having their EOT properties in the visible and near-infrared regime were simulated based on Finite Difference Time Domain (FDTD). Large nano-hole arrays with geometric properties similar to the simulated arrays were fabricated using Electron Beam Lithography (EBL). The optical resonance transmission properties (resonance position, transmission efficiency, and spectral bandwidth of resonance peak) of the fabricated nano-hole arrays were characterized. Finally, the experimental and numerical results were analyzed to determine the dependencies and discrepancies between optical resonance transmission properties for various nano-hole arrays.

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 score0.320
Threshold uncertainty score0.305

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 head0.017
GPT teacher head0.239
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 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

Citations33
Published2010
Admission routes1
Has abstractyes

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