Sensitivity enhancement of an in-fiber Michelson interferometer evanescent wave sensor using a silver nanoparticle-polymer composite overlay
Bibliographic record
Abstract
Many configurations of fiber optic evanescent wave sensors have recently been explored, with various structural and material modifications applied in attempt to increase their resolution and/or sensitivity. With the aid of long period gratings inscribed within the core of standard single mode fibers, fiber optic evanescent wave sensors with in-fiber interferometric configurations have been realized and have been shown to have excellent resolution due to sharp spectral features. The Michelson interferometer configuration, whereby a single long period grating acts as a beam splitter for the core and cladding modes, is of interest because it operates in reflection mode, which allows for easy signal detection schemes. In this work, it is experimentally demonstrated for the first time that the deposition of a nanoparticle-polymer composite high refractive index overlay film onto the cladding arm surface of such an interferometric sensor greatly increases its sensitivity. Film refractive indices of > 1.7 are achieved and can be further increased to > 2 upon repetition of the nanoparticle synthesis cycle. Sensitivity enhancement factors as large as 16.7 occur in the film index range of 1.9 - 2.1. Experimental data are presented and compared to the theoretical simulation results.
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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 | 0.001 |
| 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".