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Record W4361805564 · doi:10.1063/5.0119268

The study of temperature and refractive index sensitivity of polyimide coated etched (3LPGs-FBG) sensor

2023· article· en· W4361805564 on OpenAlexaff
Zahraa S. Alshaikhli, Wasan A. Hekmat, Hui Wang

Bibliographic record

VenueAIP conference proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceFiber Bragg gratingRefractive indexPolyimideRepeatabilitySensitivity (control systems)Fiber optic sensorFabricationOptical fiberOpticsOptoelectronicsGratingFiberPhotonic-crystal fiberElectronic engineeringComposite materialWavelengthLayer (electronics)

Abstract

fetched live from OpenAlex

In this study, a new sensor configuration based on optical fiber grating sensor is fabricated and evaluated experimentally. A combination sensor of three Long Period Gratings LPGs and one Fiber Bragg Grating FBG is fabricated and coated by polyimide in order to enhance the performance and the sensitivity of the new sensor for temperature and refractive index measurements. The gratings were written on the chemical etchant Single Mode Fiber SMF and then coated by polyimide. The new sensor was tested under different temperatures range from 25 °C to 38 °C and under different concentration of NaCl solution ranging from 0% to 5% corresponding to 1.3305 RIU to 1.3410 RIU. An investigation of sensor performance, repeatability and stability tests were carried out. The sensor exhibits a linear performance and a good stability during one hour of testing. For the (3LPGs-FBG) sensor, the temperature sensitivity was estimated to be 2.8 pm/°C and the refractive index sensitivity was estimated to be 16.078 nm/ RIU. The new (3LPGs-FBG) sensor provides number of advantages among other configurations such as low costs, compactness, accurate sensitivity and finally easy fabrication process.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.248
Teacher spread0.232 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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