High temperature resistance temperature sensor based on the hydrogen loaded fiber Bragg grating
Bibliographic record
Abstract
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.
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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 | 0.001 |
| 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".