BNNT Coated FBGs in Tapered Optical Fibers for Temperature and Hydrophilic Gas Sensing
Bibliographic record
Abstract
Fiber Bragg gratings (FBGs) were fabricated in tapered fibers using both the plane-by-plane and phase mask approaches with a femtosecond infrared laser. A thin layer of boron nitride nanotubes (BNNTs) was deposited onto the FBGs through a dip-coating process from a BNNT water solution. The reflection spectra of the FBGs were then recorded when they were placed in ammonia (NH3), hydrogen chloride (HCl) and bromine (Br2) gases. It was demonstrated experimentally that, due to the BNNT coating, the return losses of the FBGs increased when they were surrounded with these hydrophilic gas vapors. However, when the FBGs were in other atmospheres such as air, methanol and acetone vapors, no change in the FBG reflection spectra was observed. The results of this work indicate that BNNT coatings on FBGs can play an important role for hydrophilic gas detection with large measurement range and reusability due to their quick release of adsorbed gases. Moreover, the BNNT coating induced losses with different ammonia gas concentrations were quantitatively measured. It was observed that the Bragg wavelengths of the FBGs remained the same when the FBGs were tested in hydrophilic gases suggesting the proposed device can be used for temperature or strain sensing at the same time. The response of the Bragg wavelength of BNNT coated FBGs to temperature was also studied and it was found that the thin BNNT coating did not affect the FBG's temperature sensitivity. Due to the properties of BNNTs and optical fibers, the proposed fiber sensor can be used in harsh environments.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".