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Record W1964494355 · doi:10.1109/jphot.2013.2278983

Generation of Multiple Plasmon Resonances in a Nanochannel

2013· article· en· W1964494355 on OpenAlexafffund
Wei Peng, Yuzhang Liang, Lixia Li, Yun Liu, Jean‐François Masson

Bibliographic record

VenueIEEE photonics journal · 2013
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversité de Montréal
FundersSpecialized Research Fund for the Doctoral Program of Higher Education of ChinaMinistry of Education of the People's Republic of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSurface plasmonPlasmonSurface plasmon resonanceResonance (particle physics)Materials scienceLocalized surface plasmonDelocalized electronRefractive indexOpticsOptoelectronicsSurface plasmon polaritonWavelengthGratingDielectricPhysicsNanoparticleNanotechnologyAtomic physics

Abstract

fetched live from OpenAlex

In this paper, we investigate the coupling of surface plasmon based on Kretschmann configuration (KC) to localized and delocalized surface plasmon modes supported by metallic nanowire grating (MG) at the oblique incidence. The designed structure consists of MG deposited on a substrate positioned upside down above a conventional KC at a certain distance. Mode coupling and resonant interactions between KC and MG create two new hybrid plasmon modes with narrow bandwidths and unique spectra features, resulting in two obvious dips in the reflection spectrum. Although both dips respond nonlinearly to the refractive index (RI) of the dielectric layer, surprisingly, the sum of two resonance wavelengths has a good linear approximation in a wider RI range. The simultaneous measurement of two resonance dips makes it more attractive as a surface plasmon resonance analysis technology, which is valuable for high-sensitivity optical sensors.

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 communication0.0000.001
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 head0.045
GPT teacher head0.252
Teacher spread0.207 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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