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Record W1964679426 · doi:10.1364/oe.17.010623

Tunable structures comprising two photonic crystal slabs – optical study in view of multi-analyte enhanced detection

2009· article· en· W1964679426 on OpenAlexaff
Lina Shi, P. Pottier, Maksim Skorobogatiy, Yves-Alain Peter

Bibliographic record

VenueOptics Express · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPhotonic crystalOpticsSlabMaterials scienceGuided-mode resonanceFinite-difference time-domain methodTransmission coefficientElectric fieldResonance (particle physics)WavelengthOptoelectronicsPhotonicsTransmission (telecommunications)PhysicsDiffraction grating

Abstract

fetched live from OpenAlex

Using finite-difference time-domain method, we characterize the normal-incidence transmission properties of a two slab photonic crystal device in a view of its applications in fluorescence enhancement and multi-analyte detection. Individual slabs consist of a square or a triangular lattice of air holes embedded into a silicon nitride slab. The geometrical parameters are chosen so that the individual slabs operate in a guided resonance regime where strong reflectivity under the normal incidence angle is observed in a broad spectral range. When placed in the close proximity of each other, the two photonic crystal slab system exhibits a narrow Fabry-Perot type transmission peak corresponding to the excitation of a resonant mode in the cavity formed by the two slabs. We then study the effects of the size of the air gap between the two photonic crystal slabs on the spectral position and bandwidth of a resonance transmission peak. Finally, we investigate the electromagnetic energy distributions at the wavelength of a transmission resonance in the double slab photonic crystals. As a final result we demonstrate that this structure can provide electric field enhancement at the slab surface, which can be used for fluorescence enhancement.

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 score0.040
Threshold uncertainty score0.694

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 head0.017
GPT teacher head0.301
Teacher spread0.284 · 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

Citations20
Published2009
Admission routes1
Has abstractyes

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