Polymer-Coated Fiber Bragg Grating Sensors for Simultaneous Monitoring of Soluble Analytes and Temperature
Bibliographic record
Abstract
A new fiber-optic sensor for simultaneous measurement of water-soluble analytes and temperature with polymer-coated fiber Bragg gratings (FBGs) is proposed. As an application of the approach, simultaneous monitoring of the concentration of sugar or potassium chloride (KCl) and temperature has been achieved. Changes in these environmental parameters result in different extents of either red- or blue-shifts of the Bragg resonance wavelengths of the gratings. It has been found that polyimide-coated FBG responds to variations of both temperature and concentrations of soluble analytes, while acrylate-coated FBG is sensitive to environmental temperature only. The experimental results showed that the temperature sensitivity of the acrylate-coated FBG, temperature, sugar, and KCl concentration sensitivities of the polyimide-coated FBG are 0.0102$~\hbox{nm}/^{\circ}\hbox{C}$, 0.0094$\hbox{nm}/^{\circ}\hbox{C}$, 0.0012$\hbox{nm}/^{\circ}\hbox{Bx}$, and 0.0126 nm/M, respectively. The sensing mechanism of the polyimide-coated FBG lies in the hygroscopic properties of the polyimide coating, which result in the change of the strain of the fiber and, thus, the optical properties of the grating. Since the sensor detects the analytes that swell the polyimide coating and different analytes induce different swelling effects, the sensor can detect different analytes without prior knowledge once a calibration curve is developed.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".