Broad-band fluorescent all-fiber source based on microstructured optical fibers
Bibliographic record
Abstract
The present work demonstrates a miniature all-fiber optic fluorescence source on the basis of organic dyes. A capillary of fused silica has been used to keep the Rhodamine 6G dissolved in glycerine at one end of which a tapered optical fiber is inserted to receive the useful signal. The fluorescence medium is side excited with a CW laser radiation at 532 nm by a fiber taper. The functioning of the system is demonstrated using a conventional optical fiber, a photonic crystal fiber and a hollow-core fiber. Full Text: PDF References S. James, R. Tatam, Optical fibre long-period grating sensors: characteristics and application, Meas. Sci. Technol. 14(5), R49, (2003). CrossRef Y.P. Wang, L. Xiao, D.Wang, W. Jin, Highly sensitive long-period fiber-grating strain sensor with low temperature sensitivity, Opt. Lett. 31(23), 3414, (2006). CrossRef Y. Wang, W. Jin, J. Ju, H. Xuan, H. Ho, L. Xiao, D. Wang, Long period gratings in air-core photonic bandgap fibers, Opt. Express 16(4), 2784, (2008). CrossRef L. Rindorf, O. Bang, Highly sensitive refractometer with a photonic-crystal-fiber long-period grating, Opt. Lett. 33(6), 563, (2008). CrossRef G. Durana, J. Gomez, G. Aldabaldetreku, J. Zubia, A. Montero, I. Saez de Ocariz, Assessment of an LPG mPOF for Strain Sensing, IEEE Sens. J. 12(8), 2668, (2012). CrossRef D.Vezenov, B.Mayers, D.Wolfe, G.Whitesides, Integrated fluorescent light source for optofluidic applications, App. Phys. Lett. 86(4), 041104, (2005). CrossRef B.Mayers, D.Vezenov, V.Vullev, G.Whitesides, Arrays and Cascades of Liquid?Liquid Waveguides: Broadband Light Sources for Spectroscopy in Microchannels, Anal. Chem. 77(5), 1310, (2005). CrossRef J.M. Lim, S-H. Kim, J-H. Choi, S.-M. Yang, Fluorescent liquid-core/air-cladding waveguides towards integrated optofluidic light sources, Lab Chip 8(9), 1580, (2008) CrossRef V. Vladev, T. Eftimov, Union of Scientists in Bulgaria-Plovdiv, 16, 73, (2014).
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".