Low Temperature Sensor Based on Etched LPFG with Different Materials Coating
Bibliographic record
Abstract
A low temperature sensor based on etched Long Period Fiber Grating LPFG is proposed and demonstrated. A chemically etched LPFG sensor coated with (Indium In, Aluminum Al, Silver Ag, Palladium Pd and Titanium Ti) embedded within a build low temperature setup. The sensor investigation was carried out under temperature range of 20℃ to -150℃; and the resonance wavelength shift was collected with different cooling rates of (10, 15, 20, 25℃/min) in order to investigate the effect of cooling rates on the sensor performance. However, the experimental results show that 10 mm LPFG sensor with grating period of 400 µm offer temperature sensitivity of 1.5 times, 2 times, 2.5 times, 3 times and 3.5 times higher than bare LPFG for Ti-coated LPFG, Pd-coated LPFG, Ag-coated LPFG, Al-coated LPFG and In-coated LPFG respectively. The maximum measuring error is less than ±0.5℃, which confirms the effectively using of LPFG sensor in cryogenic application. Moreover, the overall resonance wavelength shifts are 1.213 nm, 1.532 nm, 1.935 nm, 2.015 nm, 2.397 nm and 2.671 for bare LPFG, Ti-coated LPFG, Pd-coated LPFG, Ag-coated LPFG, Al-coated LPFG and In-coated LPFG sensors respectively. According to the cooling rates, the results illustrate that as cooling rate increase, the sensor sensitivity decrease due to the sensor response. For more investigations, simulation work is carried out using MATLAB and the sensor shows a good agreement results between experimental and simulation measurements. It is worth to mention that the main findings will essentially contribute to choose suitable materials for coating LPFG for low temperature sensing purposes and will increase the existing knowledge about optical fiber sensor applications.
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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) | 0.001 | 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".