MétaCan
Menu
Back to cohort
Record W2032491762 · doi:10.1364/oe.18.011464

Intrinsic temperature sensitivity of tilted fiber Bragg grating based surface plasmon resonance sensors

2010· article· en· W2032491762 on OpenAlex
Liyang Shao, Yanina Shevchenko, Jacques Albert

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 · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceSurface plasmon resonanceRefractive indexFiber Bragg gratingOpticsFiber optic sensorCore (optical fiber)Resonance (particle physics)Surface plasmonOptical fiberGratingPhotonic-crystal fiberOptoelectronicsLocalized surface plasmonPlasmonWavelengthNanotechnologyNanoparticlePhysics

Abstract

fetched live from OpenAlex

A miniature surface plasmon resonance sensor is fabricated from a gold-coated standard optical fiber with an in-core tilted fiber Bragg grating fabricated by UV exposure. The sensor has a measured refractive index sensitivity of 571.5 nm/RIU (refractive index unit) at constant temperature. We show here that the intrinsic temperature sensitivity of this device is reduced to less than 6.3 pm/degrees C (between 23 degrees C and 59 degrees C) when measurements are referenced to a core mode reflection resonance of the grating. This residual sensitivity is essentially that of the 50 nm thick deposited gold layer but it is bigger by one order of magnitude than the expected value (0.51 pm/degrees C) for a gold-water interface.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
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 integrity0.0000.001
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 head0.210
Teacher spread0.204 · 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