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Record W2160985099 · doi:10.1109/icsens.2005.1597776

High temperature resistance temperature sensor based on the hydrogen loaded fiber Bragg grating

2005· article· en· W2160985099 on OpenAlexafffund
Bowei Zhang, Mojtaba Kahrizi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsMaterials scienceFiber Bragg gratingRefractive indexOptical fiberTemperature measurementOptoelectronicsThermal diffusivityThermal stabilityAnnealing (glass)GermaniumHydrogen sensorHydrogenOpticsComposite materialSiliconChemistry

Abstract

fetched live from OpenAlex

Fiber Bragg grating (FBG) temperature sensor and sensor arrays were applied widespread particularly in harsh environments. Although FBGs are often referring to permanent refractive index modulation in the fiber core, exposure to high temperature environments usually results the bleach of the refractive index modulation. The maximum temperature reported for the conventional FBG temperature sensor is around 600 /spl deg/C due to its weak bonds of germanium and oxygen. In this paper we report design and development of a novel high temperature resistance FBG temperature sensor, based on the hydrogen loaded germanium doped FBG. The refractive index modulation in the FBG is induced by the molecular water. The results of our experiments have shown that the stability of the device at high temperatures is substantially increased. Due to the high bonds energy of hydroxyl and the low diffusivity of the molecular water, the thermal testing results of this temperature sensor show the thermal stability of hydrogen loaded FBG can be increased using annealing treatment; moreover, the highest erasing temperature for the device is above 1000 /spl deg/C. Furthermore, the experimental results has provided a better understanding of the formation of the hydrogen loaded FBGs and the chemical transfers at elevated temperatures in the fiber core.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
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 integrity0.0000.001
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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

Citations4
Published2005
Admission routes2
Has abstractyes

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