Through-The-Coating Fabrication of Fiber Bragg Grating Relative Humidity Sensors Using Femtosecond Pulse Duration Infrared Lasers and a Phase Mask
Bibliographic record
Abstract
Fiber Bragg grating (FBG) relative humidity (RH) sensors are fabricated in commercially available polyimide (PI)-coated optical fibers with diameters of 50 and 125 μm. Infrared (800 nm) femtosecond pulse duration laser pulses and a phase mask are used to inscribe Type-I and Type-II FBGs directly through the protective polyimide coatings of both 50 and 125 μm diameter fibers without typical fiber processing such as hydrogen loading, cryogenic storage, stripping, recoating or annealing. The devices are then evaluated for their performance as humidity sensors. At telecom wavelengths, the 50 μm diameter fiber devices with a 10 μm thick PI coating had a wavelength shift of the Bragg resonance at a constant temperature of 2.7 pm/%RH, whereas the 125 μm diameter fiber devices with a 17 μm thick PI coating had a wavelength shift of 1.8 pm/%RH. The humidity sensors in the 50 µm diameter fiber demonstrated a more rapid response time to small changes in humidity and a weaker hysteresis when compared to the 125 µm diameter fiber devices. No modification to the PI coatings was observed during fabrication. No difference in RH sensitivity was observed for Type-I devices when compared with Type-II devices with the same fiber. The applicability of this approach for fabricating distributed RH sensing arrays with hundreds of sensing elements on a single fiber is discussed.
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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 | 0.000 |
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".