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Record W2059487580 · doi:10.1364/oe.17.002334

Rayleigh anomaly-surface plasmon polariton resonances in palladium and gold subwavelength hole arrays

2009· article· en· W2059487580 on OpenAlexfundno aff
Hanwei Gao, Jeffrey M. McMahon, Min Hyung Lee, Joel Henzie, Stephen K. Gray, George C. Schatz, Teri W. Odom

Bibliographic record

VenueOptics Express · 2009
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of EnergyNational Science Foundation
KeywordsPlasmonSurface plasmon polaritonOpticsRayleigh scatteringSurface plasmonMaterials scienceGratingWavelengthPolaritonExtraordinary optical transmissionOptoelectronicsMolecular physicsPhysics

Abstract

fetched live from OpenAlex

Surface plasmon polaritons (SPPs) and Rayleigh anomalies (RAs) are two characteristic phenomena exhibited by periodic grating structures made of plasmonic materials. For Au subwavelength hole arrays, SPPs and RAs from opposite sides of the film can interact under certain conditions to produce highly intense, narrow spectral features called RA-SPP resonances. This paper reports how RA-SPP effects can be achieved in subwavelength hole arrays of Pd, a weak plasmonic material. Well-defined resonances are observed in measured and simulated optical transmission spectra with RASPP peaks as narrow as 45 nm (FWHM). Dispersion diagrams compiled from angle-resolved spectra show that RA-SPP resonances in Pd hole arrays shift in wavelength but do not decrease significantly in amplitude as the excitation angle is increased, in contrast with RA-SPP peaks in Au hole arrays. The apparent generality of the RA-SPP effect enables a novel route to optimize resonances in non-traditional plasmonic media.

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 score0.000
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)0.0000.000

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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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

Citations178
Published2009
Admission routes1
Has abstractyes

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