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Record W1486853081 · doi:10.1063/1.4930090

Plasmonic Fano interference produced by gold nano-disks on a dielectric Bragg stack

2015· article· en· W1486853081 on OpenAlexaff
Akram Hajebifard, Pierre Berini

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLaser linewidthFano resonancePlasmonFano planeStack (abstract data type)OpticsMaterials scienceFigure of meritOptoelectronicsSurface plasmonPhysicsLaser

Abstract

fetched live from OpenAlex

Fano interference is predicted in a structure consisting of a periodic array of uncoupled gold nano-disks (AuNDs) supported by a Bragg stack. The phenomenon occurs due to resonant interference between the reflection spectrum of the Bragg stack and localized surface plasmon resonances (LSPRs) on the AuNDs. Fitting the computational results to the Fano lineshape formula yields the Fano parameters of the resonances including their linewidth. One of the most important advantages of the structure is that the linewidth of the Fano resonances (∼9 nm) is much narrower than the bandwidth of the LSPR nano-disks (∼80 nm) and the reflection bandwidth of the Bragg stack on which the disks are supported (∼100 nm). The narrow Fano linewidth and the ease with which they can be interrogated using perpendicularly incident light makes the structure attractive for biosensing applications. The bulk sensitivities and figure of merit of Fano resonances in a proposed design are 44 nm/RIU (redshift) and −52 nm/RIU (blueshift), and ∼4 RIU−1, respectively.

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 score0.003

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)0.0000.000

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 head0.027
GPT teacher head0.248
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 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

Citations4
Published2015
Admission routes1
Has abstractyes

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