Dramatic performance enhancement of evanescent-wave multimode fiber fluorometer using non-Lambertian light diffuser
Bibliographic record
Abstract
To enhance the performance of an evanescent-wave (EW) based sensor, efforts are usually made to modify the sensor architecture rather than the excitation source. In this paper, we theoretically examine the role of meridian and skew rays under total internal reflection (TIR) as well as tunneling rays with the emphasis on sensor performance. Our further investigation indicates that the intensity profile of the light source enormously influences the EW power, and thus the collectable fluorescent emission level as well. A non-Lambertian fiber-optic side-emitting diffuser (FOSED) is proposed and experimentally verified, revealing that a proper alignment of this FOSED can dramatically improve the signal quality and reduce the level of stray excitation light. In particular, the adoption of a FOSED or other light diffusers with similar output profiles will ensure that the excitation power is used more efficiently, suggesting a lower demand on the excitation source power level, and the performance of the stray light filter and detector. The superiority of this innovation is further addressed by comparing it with a long period grating (LPG) fiber-optic sensor, which claims highly efficient core to cladding mode coupling. This study presents a new concept for the construction of a high-performance and cost-effective EW-based sensor system.
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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) | 0.000 | 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 | 0.001 |
| 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".