Luminescent Polymer Composites for Optical Fiber Sensors
Bibliographic record
Abstract
Optical fiber sensors incorporating luminescent materials are useful for detecting physical parameters and biochemical species. Fluorescent materials integrated on the tips of optical fibers, for example, provide a means to perform fluorescence thermometry while monitoring the intensity or the spectral variations of the fluorescence signal. Similarly, certain molecules can be tracked by monitoring their characteristic emission in the UV wavelength range. A key element for these sensing approaches is the luminescent composite, which may be obtained upon allocating luminescent nanomaterials in glass or polymer hosts. In this work, we explore the fluorescence features of two composites incorporating lanthanide-doped fluorescent powders using polydimethylsiloxane (PDMS) as a host. The composites are obtained by a simple mixing procedure and can be subsequently deposited onto the end faces of optical fibers via dip coating or molding. Whereas one of the composites has shown to be useful for the fabrication of fiber optic temperature sensors, the other shows promising result for detection of UV radiation. The performance of both composites is first evaluated for the fabrication of membranes by examining features such as fluorescent stability. We further explore the influence of parameters such as particle concentration and density on the fluorescence features of the polymer blends. Finally, we demonstrate the incorporation of these PDMS fluorescent composites onto optical fibers and evaluate their sensing capabilities.
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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 | 0.000 |
| 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) | 0.001 | 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 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".