Evanescent field fiber Bragg grating sensors for index of refraction sensing with applications to structural health monitoring
Bibliographic record
Abstract
Fiber Bragg grating sensing is a relatively mature fiber optic sensor technology currently being used in structural health monitoring systems. Therefore, there are significant benefits to using this technology as a platform for other sensing modalities. In this work, a side polished fiber Bragg sensor is described for sensing refractive index changes. The effective refractive index of a fiber Bragg grating is a function of the refractive index of the media surrounding it, and its sensitivity may be optimized with appropriate design. As the external refractive index changes, the wavelength at which incident light experiences a maximum reflection from the grating will shift. The sensitivity of a fiber Bragg grating to external refractive index changes increases when the grating is polished on one side. This side-polishing technique enables the Bragg grating to preserve a greater portion of its mechanical strength compared with other techniques such as chemical etching. This work utilizes side-polished fiber Bragg grating sensors centered at a 1542.9 nm wavelength with cladding thickness values of approximately 1-2 μm. The response of these sensors to small refractive index changes was studied. Previous work on fiber Bragg grating sensors has shown that the peak wavelengths can be measured with 3 pm repeatability. With this repeatability, this study demonstrated that a 0.001 refractive index change can be observed. By using materials that change index with moisture or pH, this technique can be used to construct both pH and moisture sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".