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Record W4362528544 · doi:10.1002/adma.202300595

Polarization‐Tuned Fano Resonances in All‐Dielectric Short‐Wave Infrared Metasurface

2023· article· en· W4362528544 on OpenAlexafffund
Anis Attiaoui, Gérard Daligou, Simone Assali, Oliver Skibitzki, Thomas Schroeder, Oussama Moutanabbir

Bibliographic record

VenueAdvanced Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsPolytechnique Montréal
FundersArmy Research OfficeHORIZON EUROPE Framework ProgrammeMitacsPRIMA QuébecCanada Research ChairsNatural Sciences and Engineering Research Council of CanadaEuropean CommissionCanada Foundation for Innovation
KeywordsFano resonanceNanophotonicsMaterials scienceOptoelectronicsPolarization (electrochemistry)PlasmonDielectricOpticsInfraredSiliconLight scatteringNanosensorWaferRefractive indexMetamaterialScatteringNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Abstract The short‐wave infrared (SWIR) is an underexploited portion of the electromagnetic spectrum in metasurface‐based nanophotonics despite its strategic importance in sensing and imaging applications. This is mainly attributed to the lack of material systems to tailor light–matter interactions in this range. Herein, this limitation is addressed and an all‐dielectric silicon‐integrated metasurface enabling polarization‐induced Fano resonance control at SWIR frequencies is demonstrated. The platform consists of a 2D Si/Ge 0.9 Sn 0.1 core/shell nanowire array on a silicon wafer. By tuning the light polarization, it is shown that the metasurface reflectance can be efficiently engineered due to Fano resonances emerging from the electric and magnetic dipoles competition. The interference of optically induced dipoles in high‐index nanowire arrays offers additional degrees of freedom to tailor the directional scattering and the flow of light while enabling sharp polarization‐modulated resonances. This tunablity is harnessed in nanosensors yielding an efficient detection of 10 −2 changes in the refractive index of the surrounding medium.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.048
GPT teacher head0.306
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

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

Citations23
Published2023
Admission routes2
Has abstractyes

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