Bi-Functional Coated Tapered LPFG Sensor: Gas and Temperature Sensing
Bibliographic record
Abstract
A bi-functional coated tapered Long Period Fiber Grating LPFG as a gas and a temperature sensor is proposed. Obtains a sensor with a high sensitivity is still a matter of challenge. In this work, a taper LPFG is used for sensing purposes. Besides, in order to increase the sensor performance, numbers of Cr and Al layers are coated on the taper region. In this work, different concentrations of four gases Ar, NH3, CH4 and N2 are applied for temperature range (25℃ up to 80℃). however, the experimental results illustrate that the transmission loss is reduce which means a significant interaction between sensing medium and cladding mode is occurred successfully which leads to higher sensitivity. Nevertheless, from the main findings, the temperature sensitivities are 2.9 pm/℃, 4.9 pm/℃, 4.5 pm/℃ and 3.1 pm/℃ for 4% concentrations of Ar, NH3, CH4 and N2 respectively. In addition, the gases sensitives are 0.213 nm%-1, 0.41 nm%-1, 0.39 nm%-1, 0.201 nm%-1 with limit of detection LoD of 0.061%, 0.024%, 0.032% and 0.073% for Ar, NH3, CH4 and N2 respectively. Additionally, the sensor shows a good repeatability, recoverability and stability. In terms of stability, the sensor shows a stable value of wavelength shift under different values of gas concentration and temperatures which was estimated 1.55 µm. Besides, it shows a fast responsivity with 53 s and 60 s as response time and recovery time respectively. Interestingly, with less than 0.06 nm of standard deviation, the signal returned to its original resonance wavelength which confirms that this sensor has a good recoverability. From these results, a coated taper LPFG can be a promising candidate for medical and industrial 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 | 0.001 |
| 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".