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Record W2270139585 · doi:10.1021/acs.nanolett.5b04931

Adiabatic Nanofocusing in Hybrid Gap Plasmon Waveguides on the Silicon-on-Insulator Platform

2016· article· en· W2270139585 on OpenAlexfundno aff
Michael P. Nielsen, Lucas Lafone, Aliaksandra Rakovich, Themistoklis P. H. Sidiropoulos, Mohsen Rahmani, Stefan A. Maier, Rupert F. Oulton

Bibliographic record

VenueNano Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEngineering and Physical Sciences Research CouncilDirectorate-General for Research and InnovationRoyal SocietyLeverhulme Trust
KeywordsPlasmonMaterials scienceOptoelectronicsPhotonicsSilicon on insulatorAdiabatic processElectric fieldWaveguideSurface plasmonOpticsSiliconPhysics

Abstract

fetched live from OpenAlex

We present an experimental demonstration of a new class of hybrid gap plasmon waveguides on the silicon-on-insulator (SOI) platform. Created by the hybridization of the plasmonic mode of a gap in a thin metal sheet and the transverse-electric (TE) photonic mode of an SOI slab, this waveguide is designed for efficient adiabatic nanofocusing simply by varying the gap width. For gap widths greater than 100 nm, the mode is primarily photonic in character and propagation lengths can be many tens of micrometers. For gap widths below 100 nm, the mode becomes plasmonic in character with field confinement predominantly within the gap region and with propagation lengths of a few microns. We estimate the electric field intensity enhancement in hybrid gap plasmon waveguide tapers at 1550 nm by three-photon absorption of selectively deposited CdSe/ZnS quantum dots within the gap. Here, we show electric field intensity enhancements of up to 167 ± 26 for a 24 nm gap, proving the viability of low loss adiabatic nanofocusing on a commercially relevant photonics platform.

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 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 score0.023
Threshold uncertainty score0.965

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.226
Teacher spread0.200 · 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.

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

Citations68
Published2016
Admission routes1
Has abstractyes

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