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Record W4400355118 · doi:10.1109/lpt.2024.3423799

Inscription of Long Chirped Fiber Bragg Gratings in a Nonuniform Fiber for Wide-Range Tunable Delay Line

2024· article· en· W4400355118 on OpenAlexaff
Song Gao, Zengrun Wen, Chams Baker, Yangjian Cai, Xiaoyi Bao

Bibliographic record

VenueIEEE Photonics Technology Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersNatural Science Foundation of Shandong ProvinceScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsFiber Bragg gratingMaterials sciencePHOSFOSOpticsFiberPlastic optical fiberLong-period fiber gratingGraded-index fiberPolarization-maintaining optical fiberOptoelectronicsLine (geometry)Optical fiberFiber optic sensorPhysicsComposite materialWavelength

Abstract

fetched live from OpenAlex

A wide-range tunable delayline with long delay time is achieved based on a 50 cm long chirped fiber Bragg grating (FBG) in a nonuniform hybrid fiber. The chirped fiber Bragg grating is inscribed by a standing wave formed by two counter-propagating continuous-wave lights, which induces a periodical refractive index change due to high photosensitivity of As2Se3 core and creates a standard FBG along the whole nonuniform taper section. A 50 cm long chirped FBG is obtained from the inscribed standard FBG by streching the nonuniform As2Se3-PMMA tapered fiber. The delay time is larger than that using traditional chirped FBGs due to the long inscribed section and the high refractive index of the fiber core and the large tunable range is attributed to the the low stiffness of the micron diameter As2Se3 core and PMMA cladding. The maximum delaytime is ~10 ns and the maximum tunable delay time is 0.417 ns at the wavelength of 1553.5 nm, which is, to the best of our knowledge, the largest tunable delay time ever reported in a chirped FBG. The delayed performance can be tailored by designing the nonuniform profile of the inscribed tapered fiber.

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 score0.150
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 integrity0.0000.001
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 head0.008
GPT teacher head0.228
Teacher spread0.219 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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