MétaCan
Menu
Back to cohort
Record W2981881973 · doi:10.1109/iceaa.2019.8879148

Variable Pitch Crossed Surface Relief Gratings for Point-of-Care Sensing

2019· article· en· W2981881973 on OpenAlexaff
Juan Gomez-Cruz, Yazan Bdour, Eduardo Carrasco, Ribal Georges Sabat, Carlos Escobedo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsSurface plasmon resonancePlasmonMaterials scienceDielectricGratingSurface plasmonOpticsWavelengthOptoelectronicsPhysicsNanotechnologyNanoparticle

Abstract

fetched live from OpenAlex

Nanostructures consisting of crossed surface relief gratings (CSRGs) support surface plasmon resonances (SPRs) that are compatible with biosensing applications [1], [2]. At normal incidence, surface plasmons are excited between a metal and a dielectric at a light wavelength ( λSPR) given by λSPR=nΛ[√{εm/(n2+εm)}], where n is the index of refraction of the dielectric, and εm is the real part of the permittivity of the metal and Λ is the grating pitch. When placed between crossed linear polarizers, CSRGs allow for plasmonic energy exchange between the two superimposed gratings that eliminate any incident polychromatic light, except for the narrow SPR bandwidth where polarization conversion occurs. The result is an effective transmission of the SPR signal with a high signal-to-noise ratio. The single pitch of CSRGs, however, limit their operation to a specific plasmonic resonance wavelength. Here, we present a plasmonic sensor based on variable-pitch CSRG that allows plasmonic resonances at different wavelength bandwidth depending on the illuminated region. The variable-pitch CSRGs (VP-CSRGs) are fabricated on azobenzene-functionalized films through a simple two-step procedure. Fig. 1a presents an actual AFM image of the surface of a nanofabricated VP-CSRGs. The plasmonic response of the VP-CSRG sensor was evaluated using Finite-Difference Time-Domain (FDTD) simulations to show the e-field intensity distribution on the gold-coated VP-CSRG surface. Electric field enhancement and distribution, due to the plasmonic conversion, was assessed through the change of the RI of the dielectric medium in contact with the metallic crossed gratings, and through the spectral diversity of the light source to excite the surface plasmons. For the simulations, the surface of the CSRGs with pitches of 520, 540 and 560 nm were modeled using the function, f(x, y)=G(cos[(2π/p)x]+cos[(2 π/p)y]), where G is the amplitude and p the period of the structure (Fig. 1a). The simulations were used to obtain the e-field intensity distribution, normalized with respect to the incident plane wave. Periodic boundary conditions in both x and y directions and a perfectly matched layer (PML) in the z direction were used for the analysis region. A uniform mesh size of 3 nm was used for the envelope of the nanostructure, comprising the azobenzene layer, the gold film and the dielectric medium, in all the directional axes. A plane wave, polarized along the y-axis and orthogonal to the x-y plane, was employed to induce a SPR in the structure. Fig. 1b demonstrates the plasmonic excitation in the x-direction, when using p-polarized light and the absence of plasmonic excitation in the y-direction. However, when s-polarized light is used, the nanostructures are excited in the y-direction. These results present an evidence on the unique plasmonic energy transfer between the crossed gratings that has been hypothesised before [1]. Experimentally, these results can be used to confirm that the transmitted light acquired in a collinear setup using VP-CSRGs between two orthogonal polarizers corresponds, only, to the plasmonic signature of the nanostructure.

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 score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicPlasmonic and Surface Plasmon ResearchFrench-language works237,207