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Record W2743524496 · doi:10.1063/1.4998182

Optically saturated and unsaturated collective resonances of flat metallic nanoantenna arrays

2017· article· en· W2743524496 on OpenAlexafffund
Seyed M. Sadeghi, Waylin J. Wing, Rithvik R. Gutha, Christina Sharp, Ali Hatef

Bibliographic record

VenueJournal of Applied Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsNipissing University
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsPlasmonRefractive indexNanorodMaterials scienceOpticsMetalDiffractionOptoelectronicsMolecular physicsChemistryNanotechnologyPhysics

Abstract

fetched live from OpenAlex

We study collective optical properties of arrays of flat gold nanoantennas, demonstrating they can support optically saturated and unsaturated plasmonic lattice modes when the incident light is polarized along their short axes. The saturated mode is nearly immune to the variation of the refractive index of the environment, while the unsaturated mode undergoes a large red shift without degradation as the refractive index increases. Our results show that when the incident light becomes polarized along the long axes of the nanoantennas, an increase of the refractive index of the superstrate leads to the formation of secondary plasmon peaks. These peaks are spectrally narrow and can detect variations of the ambient refractive index with a sensitivity of up to 620 nm/RIU (refractive index unit). The results suggest that the periodic arrays of flat metallic nanostructures can support hybridization of their multipolar plasmonic resonances with diffraction orders with distinct similarities and differences compared to those seen in cases of arrays of metallic nanorods.

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 score0.202
Threshold uncertainty score0.482

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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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

Citations13
Published2017
Admission routes2
Has abstractyes

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