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Record W2047470055 · doi:10.1364/ol.39.003946

Multimode spectroscopy using dielectric grating coupled to a surface plasmon resonance sensor

2014· article· en· W2047470055 on OpenAlexafffund
Farshid Bahrami, J. Stewart Aitchison, Mo Mojahedi

Bibliographic record

VenueOptics Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSurface plasmon resonanceMaterials scienceOpticsGratingRefractive indexSurface plasmonMulti-mode optical fiberRigorous coupled-wave analysisSpectroscopyDielectricResonance (particle physics)OptoelectronicsPlasmonDiffraction gratingOptical fiberPhysicsNanotechnologyNanoparticle

Abstract

fetched live from OpenAlex

A new platform is proposed to solve one of the main shortcomings of surface plasmon resonance biosensors, namely, the cross sensitivity to surface and bulk effects. This approach is based on multimode spectroscopy in which three different modes are excited simultaneously. The proposed design consists of an SPR sensor loaded with a dielectric grating. The design parameters (dimensions and wavelength) are optimized with a genetic algorithm. The optimized design has two resonance modes excited with TM polarized light, each sensitive to surface effects, and one TE mode mostly sensitive to variations in the bulk fluid refractive index. Numerical and analytical methods are used to justify the simulation results, which are in good agreement. Finally, it is shown that, by applying three-mode spectroscopy, decoupling the properties of the attached biomaterial from the background index variations is possible with the proposed design.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.245
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 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

Citations8
Published2014
Admission routes2
Has abstractyes

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