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Record W2162801607 · doi:10.1364/oe.14.011616

Design of the microstructured optical fiber-based surface plasmon resonance sensors with enhanced microfluidics

2006· article· en· W2162801607 on OpenAlex

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

VenueOptics Express · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsPolytechnique Montréal
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsMaterials scienceSurface plasmon resonancePhotonic-crystal fiberPlasmonOptical fiberSurface plasmonRefractive indexOpticsMicrofluidicsMicrostructured optical fiberFiber optic sensorOptoelectronicsSingle-mode optical fiberWaveguideWavelengthNanotechnologyGraded-index fiberNanoparticle

Abstract

fetched live from OpenAlex

The concept of a Microstructured Optical Fiber-based Surface Plasmon Resonance sensor with optimized microfluidics is proposed. In such a sensor plasmons on the inner surface of large metallized channels containing analyte can be excited by a fundamental mode of a single mode microstructured fiber. Phase matching between plasmon and a core mode can be enforced by introducing air filled microstructure into the fiber core, thus allowing tuning of the modal refractive index and its matching with that of a plasmon. Integration of large size microfluidic channels for efficient analyte flow together with a single mode waveguide of designable effective refractive index is attractive for the development of integrated highly sensitive MOF-SPR sensors operating at any designable wavelength.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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