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Record W2029393196 · doi:10.1364/josaa.20.001644

Properties and sensing characteristics of surface-plasmon resonance in infrared light

2003· article· en· W2029393196 on OpenAlex
Sergiy Patskovsky, Andrei V. Kabashin, Michel Meunier, John H. T. Luong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the Optical Society of America A · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsBiotechnology Research InstituteNational Research Council CanadaPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSurface plasmon resonanceMaterials sciencePrismPlasmonSurface plasmonInfraredSiliconOpticsResonance (particle physics)Dispersion (optics)OptoelectronicsCoupling (piping)ExcitationLocalized surface plasmonNanotechnologyNanoparticlePhysicsAtomic physics

Abstract

fetched live from OpenAlex

Conditions of surface-plasmon resonance (SPR) production with use of IR pumping light (800-2300 nm) in the Kretschmann-Raether prism arrangement were investigated. Both calculations and experimental data showed that SPR characteristics in the IR are strongly influenced by the properties of the coupling prism material. Indeed, quite different regularities of plasmon excitation, polarity of sensing response, and sensitivity are observed for two different glasses and silicon. The observed differences in SPR properties are related to essentially different behavior of dispersion characteristics of materials near the SPR coupling point. Methods for improving sensor performance and miniaturizing the SPR technique using novel coupling materials (silicon) are discussed.

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.

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 score0.181
Threshold uncertainty score0.289

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 head0.011
GPT teacher head0.199
Teacher spread0.188 · 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