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Record W2015658817 · doi:10.1364/oe.19.005868

Bragg grating-based optical switching in a bismuth-oxide fiber with strong χ^(3)-nonlinearity

2011· article· en· W2015658817 on OpenAlexaff
Irina V. Kabakova, Dan Grobnic, Stephen J. Mihailov, Eric Mägi, C. Martijn de Sterke, Benjamin J. Eggleton

Bibliographic record

VenueOptics Express · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsFiber Bragg gratingOpticsMaterials scienceLong-period fiber gratingBismuthExtinction ratioGratingSelf-phase modulationPHOSFOSOptical fiberNonlinear opticsOptoelectronicsPolarization-maintaining optical fiberFiber optic sensorPhysicsLaserWavelength

Abstract

fetched live from OpenAlex

We report the first experimental demonstration of Bragg grating-based nonlinear switching in a bismuth-oxide single-mode fiber. Exploiting the strong χ3-nonlinearity of this fiber in a cross-phase modulation scheme, we change the transmission of a probe near the grating stop band from 90 % to 20 %, a 6.5 dB extinction ratio, at powers as low as 55 W. This is an 18-fold improvement in the switching power compared to the best demonstrations in silica. The experimental results agree well with numerical simulations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.020
GPT teacher head0.229
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2011
Admission routes1
Has abstractyes

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