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Record W4408651032 · doi:10.3390/s25061937

Long-Term Wavelength Stability of Large Type II FBG Arrays in Different Silica-Based Fibers at High Temperature

2025· article· en· W4408651032 on OpenAlexaff
Robert B. Walker, Stephen J. Mihailov, Cyril Hnatovsky, Manny De Silva, Ping Lü, Huimin Ding, Abdullah Rahnama

Bibliographic record

VenueSensors · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceWavelengthFiber Bragg gratingIsothermal processOpticsLaserOptical fiberFiber optic sensorOptoelectronicsFiberWavelength-division multiplexingFabricationPhysicsComposite material

Abstract

fetched live from OpenAlex

Fiber Bragg gratings (FBGs) are useful components in fiber optic sensing systems, which can be highly multiplexed and distributed. In recent years, fabrication using ultrafast lasers has made these devices much more versatile and robust, but questions concerning their high-temperature performance remain. The wavelength resonance of an FBG is naturally sensitive to various parameters of its environment; in particular, changes in the temperature or strain of a fiber tend to induce observable shifts in the Bragg wavelength. Thus, FBGs can offer reliable sensing solutions, provided they are isolated from other influences and their wavelength responses remain well characterized. Nonetheless, it is important to be aware that the isothermal wavelength drift of unstrained FBGs has been previously observed. When this occurs, it can lead to measurement errors and a requirement for sensor recalibration. This study presents a comparison of long-term isothermal wavelength drifts observed at 600 °C, 800 °C, 900 °C and 1000 °C for large numbers of Type II FBGs in different kinds of single-mode fibers. The results provide guidance for the design of high-temperature sensing systems, both in terms of fiber selection and for estimating the maximum time before recalibration becomes necessary to maintain a specified accuracy.

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 score0.308
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
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.229
Teacher spread0.222 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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