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Record W2126021877 · doi:10.1186/1556-276x-6-388

Plasmonic propagations distances for interferometric surface plasmon resonance biosensing

2011· article· en· W2126021877 on OpenAlexafffund
Dominic Lepage, Dominic Carrier, Álvaro Jimenez, Jacques Beauvais, Jan J. Dubowski

Bibliographic record

VenueNanoscale Research Letters · 2011
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSurface plasmon resonancePlasmonInterferometrySurface plasmonBiosensorLocalized surface plasmonOpticsOptoelectronicsDielectricSurface roughnessGratingSurface plasmon polaritonNanosensorNanotechnologyNanoparticlePhysics

Abstract

fetched live from OpenAlex

A surface plasmon resonance (SPR) scheme is proposed in which the local phase modulations of the coupled plasmons can interfere and yield phase-sensitive intensity modulations in the measured signal. The result is an increased traceability of the SPR shifts for biosensing applications. The main system limitation is the propagation distance of the coupled plasmon modes. This aspect is therefore studied for thin film microstructures operating in the visible and near-infrared spectral regions. The surface roughness of the substrate layer is examined for different dielectrics and deposition methods. The Au layer, on which the plasmonic modes are propagating and the biosensing occurs, is also examined. The surface roughness and dielectric values for various deposition rates of very thin Au films are measured. We also investigate an interferometric SPR setup where, due to the power flux transfer between plasmon modes, the specific choice of grating coupler can either decrease or increase the plasmon propagation length.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.078
GPT teacher head0.297
Teacher spread0.220 · 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

Citations20
Published2011
Admission routes2
Has abstractyes

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