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Record W4220951536 · doi:10.3390/photonics9030189

Fano Resonance Hybrid Waveguide-Coupled Plasmonic Sensor Using Transparent Conductive Oxide in the Near-Infrared Range

2022· article· en· W4220951536 on OpenAlexaff
Anum Khattak, Li Wei

Bibliographic record

VenuePhotonics · 2022
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of WaterlooWilfrid Laurier University
Fundersnot available
KeywordsFano resonanceMaterials scienceOptoelectronicsPlasmonSurface plasmon resonanceRefractive indexWaveguideSurface plasmon polaritonOpticsResonance (particle physics)Coupled mode theorySurface plasmonDielectricInfraredCoupling (piping)NanoparticleNanotechnologyPhysics

Abstract

fetched live from OpenAlex

We proposed an ultra-sensitive refractive index sensor by using indium-doped cadmium oxide as a plasmonic material operating in near-infrared based on Fano resonance. The proposed sensor has a hybrid multilayer waveguide structure that supports both a long-range surface plasmon polariton (LRSPP) mode and a dielectric waveguide (DWG) mode. The design strategy of the structure parameters of the inner layers is elaborated in detail through the numerical analysis of the two modes. By suitably tailoring the thickness of the coupling layer, a strong mode coupling between the two modes could be achieved, leading to a sharp asymmetric Fano resonance. With the designed optimal physical parameters, our proposed sensor could achieve a maximum intensity sensitivity of 19,909 RIU−1, a 193-fold enhancement than that of a conventional long-range SPR (LRSPR) based scheme. The proposed design can be a promising platform for biochemical sensing in the near-infrared region.

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0000.000

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 head0.046
GPT teacher head0.266
Teacher spread0.220 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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