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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 OpenAlexafffund
Sergiy Patskovsky, Andrei V. Kabashin, Michel Meunier, John H. T. Luong

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.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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

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

Citations99
Published2003
Admission routes2
Has abstractyes

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Same venueJournal of the Optical Society of America ASame topicAdvanced Fiber Optic SensorsFrench-language works237,207