Hydrogen-loaded fiber Bragg grating for high-temperature sensor applications
Bibliographic record
Abstract
Bragg gratings have widespread applications in the rapidly growing field of optical sensors. Although fiber Bragg gratings are often referring to permanent refractive index structures, exposure in increased temperature usually results the decay of the refractive index modulation. Basically, the stability of the grating competence at high temperature is an important criterion for high temperature sensor applications. This report is a part on going research to develop high temperature optical sensors. We report our design and analyze of a hydrogen loaded fiber Bragg grating temperature sensor range from room temperature to around 1000°C. A basic setup has been built in our lab to examine the performance of the point temperature sensor based on the hydrogen loaded fiber Bragg grating. Until now, a grating has been shown to stabilize at temperatures in excess of 700°C and to survive at temperatures in excess of 930°C. The tested operation gratings around 700°C retain up to 80% reflectivity after one and a half hours. The thermal treatment of the tested hydrogen loaded fiber Bragg gratings is demonstrated capable to enhance effectively the grating's thermal stability. Our experimental results provide a better understanding of thermal response to the hydrogen loaded fiber Bragg gratings and their decay behavior at elevated temperatures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".