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Record W2466057219 · doi:10.1364/oe.24.015687

Squeezed hollow-core photonic Bragg fiber for surface sensing applications

2016· article· en· W2466057219 on OpenAlexaff
Jingwen Li, Hang Qu, Maksim Skorobogatiy

Bibliographic record

VenueOptics Express · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceFiber Bragg gratingPhotonic-crystal fiberCore (optical fiber)OpticsOptical fiberFiber optic sensorFiberPhotonicsOptoelectronicsPhysicsWavelength

Abstract

fetched live from OpenAlex

We propose to use squeezed hollow-core photonic bandgap Bragg fibers for surface sensing applications. We demonstrate theoretically and confirm experimentally that squeezing a section of the Bragg fiber core increases overlap between the optical fields of the core guided modes and the modes bound to the sensing layer, thus, significantly enhancing their interaction via anticrossing phenomenon, which, in turn, enhances surface sensitivity of the fiber sensor. As a practical demonstration, we apply our fiber sensor to in situ monitoring of the dissolution dynamics of a sub-micron-thick polyvinyl butyral (PVB) film coated on the surface of the liquid-filled Bragg fiber core. Strong spectral shift is observed during the dissolution of the PVB film, and a surface spectral sensitivity of ~0.07nm/nm is achieved experimentally with aqueous analytes. The proposed fiber sensor offers a new sensing modality and opens new sensing applications for photonic bandgap fibers, such as real-time detection of binding and affinity, study of kinetics, etc. for a range of chemical and biological samples.

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 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: Methods · Consensus signal: Methods
Teacher disagreement score0.325
Threshold uncertainty score0.739

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 head0.017
GPT teacher head0.248
Teacher spread0.230 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations19
Published2016
Admission routes1
Has abstractyes

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