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Record W2598600943 · doi:10.1088/1361-6463/aa69aa

Mid-infrared Fano resonance in heavily doped silicon and metallic nanostructures due to coupling of Wood–Rayleigh anomaly and surface plasmons

2017· article· en· W2598600943 on OpenAlexaff
Nghia Nguyen-Huu, Michael Čada, Yuan Ma, Franklin Che, Jaromı́r Pištora, Kiyotoshi Yasumoto, Jiao Lin, Hiroshi Maeda

Bibliographic record

VenueJournal of Physics D Applied Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsMetamaterial Technologies (Canada)Dalhousie University
Fundersnot available
KeywordsFano resonanceRayleigh scatteringPlasmonDopingMaterials scienceAnomaly (physics)Coupling (piping)MetalInfraredSiliconNanostructureSurface plasmonSurface plasmon resonanceOptoelectronicsCondensed matter physicsOpticsNanotechnologyComposite materialPhysicsMetallurgyNanoparticle

Abstract

fetched live from OpenAlex

Abstract This study reports the design and analysis of a plasmonic sensor based on a heavily doped silicon and metallic grating structure working in the mid-infrared region. The numerical results show that the reflection spectrum of the phosphorous-doped Si grating structure with a dopant concentration of 1 × 20 20 has a sharp asymmetric Fano resonance dip, which is strongly dependent on the refractive index change in the surroundings. It yields a sensitivity of 8000 nm/RIU (refractive index unit) and 950 nm/RIU working in the air and water media, respectively, very high values compared with that of existing devices. Moreover, the Fano resonance caused by coupling of the Wood–Rayleigh anomaly and surface plasmon resonances is demonstrated by calculating the magnetic field and Poynting vector patterns. Further, a feasible and easy fabrication process of the sensor featuring high performance is represented.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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 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

Citations16
Published2017
Admission routes1
Has abstractyes

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