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Record W2140792638 · doi:10.1109/jlt.2011.2168946

Evanescent-Wave Fiber-Optic Sensor: On Power Transfer From Core-Cladding Interface to Fiber End-Face

2011· article· en· W2140792638 on OpenAlexaff
Yasser Chiniforooshan, Jianjun Ma, Wojtek J. Bock

Bibliographic record

VenueJournal of Lightwave Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsOpticsCladding (metalworking)Multi-mode optical fiberOptical fiberMaterials scienceTotal internal reflectionMaximum power transfer theoremCore (optical fiber)Optical powerFiber optic sensorOptoelectronicsPower (physics)PhysicsLaser

Abstract

fetched live from OpenAlex

In this paper, the enhancement of collection efficiency in fiber-optic evanescent-wave (EW) sensors is studied. Both theory and experimental results are presented. The key is to consider the roughness conditions at the end-face of large-core fibers. The theory is based primarily on ray optics, but for the sake of simplicity and accuracy, wave optics is also considered. Fluorescent light is coupled into the core of a partly unclad multimode fiber. Most power is carried to the unclad end-face by tunneling modes. Reflection from this rough end-face, which is modeled as a diffuse source, mixes the initial modes. Bound rays also play an important role, carrying the power to the other end-face. The amount of output power of the bound rays there is calculated. We also study the output power in relation to the surface condition of the far end-face, which may be smooth or rough. The comparison of these cases in terms of output power shows that a rough near end-face enhances the collection of coupled fluorescent light. In contrast, roughening of the far end-face while the near end-face is rough causes a transfer of the initial, mostly tunneling, modes to the radiation modes and decreases the collectable signal.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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)0.0010.000
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)0.0030.001

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 head0.024
GPT teacher head0.232
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2011
Admission routes1
Has abstractyes

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