In-fiber interferometry sensors for refractive index
Bibliographic record
Abstract
Compact interferometers based on waveguiding structures have found countless applications in refractive index measurements, chemical sensing, as well as temperature and pressure measurements. The most common fiber devices are based on Mach–Zehnder interferometry and Michelson interferometry—two design concepts that can readily be implemented using simple fiber optic components, such as mode splitters and combiners, fiber optic gratings, and fiber tapers, among others. Fiber interferometry can also be conducted based on the Sagnac effect and the Young (double-slit) interferometer. In this review, we examine and compare over 400 fiber optic interferometers as well as more than 60 fiber optic refractive sensors based on fiber optic cavities. Even though many of the devices show temperature-, strain-, and pressure-sensitivity, we focus our review on refractive index measurements, as these are the most common applications. Many devices were characterized by their inventors using their sensitivity to refractive index changes. While the sensitivity is an important characteristic of the device, it does not easily relate to the smallest resolvable refractive index change or the limit of detection when applied to chemical measurements. Instead, we propose here that one should use the figure of merit, which is defined through the refractive index sensitivity and the width of an interferometer fringe. Using simple assumptions, we were able to mathematically relate the sensitivity and the figure of merit to common design parameters, such as the length of the interferometer arms, the operating wavelength, refractive indices of the fiber and the sample, as well as an overlap parameter, which describes the fraction of the guided wave in the sensing arm that interacts with the sample. We determined this overlap parameter for each reviewed device from the reported interferograms. Our meta-analysis provides for the first time simple and easily applicable guidance to increase the figure of merit of fiber optic interferometers and fiber optic cavities with regard to their ability to detect small refractive index changes. A high figure of merit allows measuring very small refractive index changes such as those of gases at different pressures or of very dilute solutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".