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Record W3126892536 · doi:10.1038/s41598-020-78594-7

Directional excitation of surface plasmon using multi-mode interference in an aperture

2021· article· en· W3126892536 on OpenAlexaff
Md. Zahangir Alam, Zhou Yang, M. Sheik-Bahae, J. Stewart Aitchison, Mo Mojahedi

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of TorontoQueen's University
FundersDivision of Materials ResearchNational Science Foundation
KeywordsPlasmonSurface plasmonNanophotonicsExcitationSurface plasmon polaritonOpticsInterference (communication)Materials scienceNanolithographyOptoelectronicsLocalized surface plasmonWaveguidePhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Plasmonics is a promising technology that can find many applications in nanophotonics and biosensing. Local excitation of surface plasmons with high directionality is required for many of these applications. We demonstrate that by controlling the interference of light in a metal slot with the adjustment of the angle of incidence, it is possible to achieve highly directional surface plasmon excitation. Our numerical analysis of the structure showing a strong directionality of excited surface plasmon is confirmed by near field scanning measurements. The proposed structure can be useful for many applications including excitation of plasmonic waveguides, nanolithography, and optical sensing. To illustrate its usefulness, we experimentally demonstrate that it can be used for highly directional excitation of a dielectric loaded plasmonic waveguide. We also propose a simple structure for surface plasmon interference lithography capable of providing high image contrast using this scheme.

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 head0.043
GPT teacher head0.305
Teacher spread0.263 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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