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Record W2804810197 · doi:10.23919/ropaces.2018.8364112

Triple Fano resonances in plasmonic heptamer nano-hole arrays: Symmetric and asymmetric structures

2018· article· en· W2804810197 on OpenAlexaff
Akram Hajebifard, Pierre Berini

Bibliographic record

Venue2018 International Applied Computational Electromagnetics Society Symposium (ACES) · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFano resonanceSurface plasmon polaritonPlasmonFano planeFigure of meritPolaritonExcitationSurface plasmon resonanceResonance (particle physics)Surface plasmonPhysicsCondensed matter physicsOptoelectronicsMolecular physicsOpticsMaterials scienceNanoparticleQuantum mechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

The optical properties of gold heptamer-arranged nanoholes array have been studied numerically, for both symmetric and asymmetric structures. This array supports three Fano resonances arising from the excitation of localized surface plasmon polaritons (LSPPs), propagating surface plasmon polaritons (PSPPs), or/and waves related to Wood's anomaly (WA). To study the nature of each Fano resonance, electromagnetic near-field distributions have been computed, and the effects of the geometrical parameters has been carried out. In addition, the sensing performance of the structure has been examined, revealing that the highest bulk sensitivity (400 nm/RIU) and surface sensitivity (2 nm/nm) belong to LSPP Fano resonance, whereas, the highest figure of merit (24 RIU-1) is obtained by a coupled WA-PSPP Fano resonance, which is only supported by the asymmetric 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 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 communication0.0000.000
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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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