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Record W2802201320 · doi:10.1109/lpt.2018.2828199

High Sensitive Z-Shaped Fiber Interferometric Refractive Index Sensor: Simulation and Experiment

2018· article· en· W2802201320 on OpenAlexaff
Youqiao Ma, Di Guo, Yangyuanlong Gao, Sabrina Sarah, Qiang Wu, Jun Zhou, Jaromı́r Pištora, Michael Čada

Bibliographic record

VenueIEEE Photonics Technology Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsNova Scotia Community CollegeDalhousie University
Fundersnot available
KeywordsCladding (metalworking)InterferometryOpticsRefractive indexBent molecular geometryFabricationMaterials scienceOptical fiberCladding modeGraded-index fiberFiber optic sensorCore (optical fiber)Single-mode optical fiberIntensity modulationPolarization-maintaining optical fiberPhysicsPhase modulationPhase noise

Abstract

fetched live from OpenAlex

A robust fiber-optic interferometer, which is formed by introducing two bends (i.e., z shape) to the standard telecommunication single mode fiber, is designed and analyzed theoretically and experimentally for the refractive index (RI) sensing. The first (second) bend couples (re-couples) the core (cladding) mode to the cladding (core) modes. The RI-sensitive phase difference between the core and cladding modes gives rise to the modulation of the transmitted intensity. The experimental results show that the z-shaped interferometric sensor possesses an RI sensitivity as high as 196 nm/RIU and fit well with the theoretical predictions. An investigation of the effect of perturbations of bent angle reveals that the sensor possesses relative high sensitivities as the bent angle ranges from 13° to 17° with the difference between the maximum and minimum sensitivities only 2.5% indicating the structure has a good fabrication tolerance to the inaccuracy of the bent angles. In addition, the sensor has advantages of low cost, simple structure, and an ease of fabrication, showing great potential in many sensing applications.

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 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 score0.180
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
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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.

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

Citations12
Published2018
Admission routes1
Has abstractyes

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