Photonic microstructures in optical fiber and their sensing applications
Bibliographic record
Abstract
Optical fiber sensors have attracted significant interest in their physical properties and potential applications due to their advantages of compact size, light weight, immunity to electromagnetic interference, and viability in harsh environments. To achieve the potential for applications, it is essential to reveal the underlying physics through both experimentation and simulation, which is an ongoing task now. In this study, microstructures in optical fiber, such as tapers and long-period grating (LPG), are investigated by simulation with COMSOL Multiphysics software, which is also compared with experiments in this study or the experimental results achieved by this group. In the experimentation of this study, the effects of different fabrication parameters on the formation of micro-taper in optical fiber and the transmission spectra of the resulting LPGs are carried out, which indicates that the average diameter of the micro-taper decreases and a blueshift in the transmission spectrum of the LPG occurs by increasing any of the three fabrication parameters such as arc current, arc duration, and tension along the fiber. The sensitivities of LPG to environmental refractive index and temperature are studied computationally, which shows the highest sensitivity of refractive index of 171.82 nm/RIU (Refractive Index Unit) and the temperature sensitivity of 10 pm/℃. Furthermore, tapered fibers in the configurations of either single or in-line Mach-Zehnder interferometer are studied as biosensors for detecting the concentration of Streptavidin (SV) protein computationally and compared with the experimental results previously obtained in our research group. The simulation results show good agreement with the experimental results indicating the effective refractive indices of the coating materials play a significant role in determining the sensitivity. The findings achieved from this study are helpful for designing LPG-based and tapered fiber-based optical fiber sensors and biosensors, which enable the applications of these optical sensors in various fields, such as telecommunication, chemistry, medical and environmental sciences.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".