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Record W3124656579 · doi:10.1364/ol.418137

Stimulated Brillouin scattering in high-birefringence elliptical-core As<sub>2</sub>Se<sub>3</sub>-PMMA microfibers

2021· article· lv· W3124656579 on OpenAlexafffund
Haiyang Wang, Chams Baker, Liang Chen, Xiaoyi Bao

Bibliographic record

VenueOptics Letters · 2021
Typearticle
Languagelv
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOpticsBrillouin scatteringMaterials scienceMicrofiberBirefringenceCore (optical fiber)ScatteringForward scatterLight scatteringRefractive indexOptical fiberPhysicsComposite material

Abstract

fetched live from OpenAlex

In this Letter, we design and fabricate elliptical-core (ECORE) chalcogenide-polymethyl methacrylate ( A s 2 S e 3 -PMMA) microfibers to explore the birefringence impact on stimulated Brillouin scattering. Numerical simulations based on the finite-element method and elastodynamic equation are utilized to calculate the phase and group birefringence and Brillouin gain spectra of the fundamental mode in three ECORE A s 2 S e 3 -PMMA microfibers at different core diameters. Experimentally measured and numerically calculated results show that as the core diameter of the minor axis of an ECORE microfiber with a ratio of 1.108 is reduced from 1.50 µm to 0.87 µm, a high group birefringence of ∼ 10 − 3 to ∼ 10 − 2 and a large Brillouin frequency shift difference of ∼ 6 M H z to ∼ 30 M H z are achieved, while the Brillouin gain spectra are broadened significantly from ∼ 70 M H z to ∼ 140 M H z . The high-birefringence ECORE A s 2 S e 3 -PMMA microfiber is important for Brillouin sensing due to the tailorable high birefringence and ultrahigh nonlinearity.

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)0.0010.000

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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