Pressure Sensing in High-Refractive-Index Liquids Using Long-Period Gratings Nanocoated with Silicon Nitride
Bibliographic record
Abstract
The paper presents a novel pressure sensor based on a silicon nitride (SiNx) nanocoated long-period grating (LPG). The high-temperature, radio-frequency plasma-enhanced chemical-vapor-deposited (RF PECVD) SiNx nanocoating was applied to tune the sensitivity of the LPG to the external refractive index. The technique allows for deposition of good quality, hard and wear-resistant nanofilms as required for optical sensors. Thanks to the SiNx nanocoating it is possible to overcome a limitation of working in the external-refractive-index range, which for a bare fiber cannot be close to that of the cladding. The nanocoated LPG-based sensing structure we developed is functional in high-refractive-index liquids (nD>1.46) such as oil or gasoline, with pressure sensitivity as high as when water is used as a working liquid. The nanocoating developed for this experiment not only has the highest refractive index ever achieved in LPGs (n>2.2 at λ=1,550 nm), but is also the thinnest (<100 nm) able to tune the external-refractive-index sensitivity of the gratings. To the best of our knowledge, this is the first time a nanocoating has been applied on LPGs that is able to simultaneously tune the refractive-index sensitivity and to enable measurements of other parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".