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Record W2556740097 · doi:10.3390/ma9110941

Numerical Approach to Modeling and Characterization of Refractive Index Changes for a Long-Period Fiber Grating Fabricated by Femtosecond Laser

2016· article· en· W2556740097 on OpenAlexaff
Akram Saad, Yonghyun Cho, Farid Ahmed, Martin Byung‐Guk Jun

Bibliographic record

VenueMaterials · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Victoria
FundersKorea Carbon Capture and Sequestration R and D Center
KeywordsMaterials scienceLong-period fiber gratingOpticsGratingNormalized frequency (unit)FemtosecondGraded-index fiberRefractive indexStep-index profileLaserFiber Bragg gratingOptical fiberOptoelectronicsWavelengthFiber optic sensorPhysics

Abstract

fetched live from OpenAlex

A 3D finite element model constructed to predict the intensity-dependent refractive index profile induced by femtosecond laser radiation is presented. A fiber core irradiated by a pulsed laser is modeled as a cylinder subject to predefined boundary conditions using COMSOL5.2 Multiphysics commercial package. The numerically obtained refractive index change is used to numerically design and experimentally fabricate long-period fiber grating (LPFG) in pure silica core single-mode fiber employing identical laser conditions. To reduce the high computational requirements, the beam envelope method approach is utilized in the aforementioned numerical models. The number of periods, grating length, and grating period considered in this work are numerically quantified. The numerically obtained spectral growth of the modeled LPFG seems to be consistent with the transmission of the experimentally fabricated LPFG single mode fiber. The sensing capabilities of the modeled LPFG are tested by varying the refractive index of the surrounding medium. The numerically obtained spectrum corresponding to the varied refractive index shows good agreement with the experimental findings.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.486

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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
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
Published2016
Admission routes1
Has abstractyes

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