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Record W2587075381 · doi:10.1021/acsphotonics.6b00930

Bloch Long-Range Surface Plasmon Polaritons on Metal Stripe Waveguides on a Multilayer Substrate

2017· article· en· W2587075381 on OpenAlexaff
Norman R. Fong, M. Menotti, Ewa Lisicka-Skrzek, Howard Northfield, Anthony Olivieri, R. Niall Tait, Marco Liscidini, Pierre Berini

Bibliographic record

VenueACS Photonics · 2017
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceGratingOpticsPhotonic crystalSurface plasmon polaritonSurface plasmonOptoelectronicsWaveguideStopbandPlasmonResonatorPhysics

Abstract

fetched live from OpenAlex

We propose and demonstrate a thin Au stripe on a truncated 1D dielectric photonic crystal covered with Cytop as a waveguide for Bloch long-range surface plasmon polaritons. High-quality mode outputs were observed and a mode power attenuation of 12–17 dB/mm measured at λ 0 = 1310 nm for propagation in the plane of the truncated photonic crystal and within its stopband. The truncated 1D photonic crystal advantageously enables the use of a large range of materials for the substrate, breaking free from the constraint of material symmetry to support long-range plasmons. An input grating coupler implemented as a periodic array of nanoscale Au ridges on a Au stripe was used to excite the mode via perpendicularly incident p -polarized light. The output was provided by adding a second grating coupler near the end of a waveguide to diffract light upward or by polishing the output facet and allowing the mode to radiate into a free-space beam. Advantageously, grating coupling eliminates the need for high-quality end facets, and optical alignment is simplified. Given its practicality, the structure proposed is of strong interest for biosensing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.034
GPT teacher head0.279
Teacher spread0.245 · 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 teacher head, not a consensus.

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

Citations37
Published2017
Admission routes1
Has abstractyes

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