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Record W4298122654 · doi:10.4302/plp.v14i3.1164

Fiber-coupled fluorescence light source suitable for spectroscopic applications

2022· article· en· W4298122654 on OpenAlexaff
Veselin Vladev, Tinko Eftimov, Stefan Bozhkov, Krastena Nikolova, Stefka Minkova, Denitsa Blazheva, Galena Angelova, Mariya Brazkova

Bibliographic record

VenuePhotonics Letters of Poland · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité du Québec en Outaouais
FundersBulgarian National Science Fund
KeywordsOptical fiberFluorescenceOpticsPhotonicsMaterials scienceFerruleOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

An experimental study of a fluorescent broadband light source fully compatible with optical fibers is presented in the article, with the aim of using it for excitation-emission matrix fluorescence spectroscopy. A fiber optic glass ferrule filled with a solution of Rhodamine 6G in glycerin was used for the basic construction of the light source. The ferrule is coupled with optical fibers to illuminate the dye medium and to receive the fluorescent signal. A tuning of the light spectrum from the source between 528 nm and 660 nm with a shift of 1 nm is achieved by means of a monochromator. Full Text: PDF ReferencesN. Hoinka and T. Fuhrmann-Lieker, "Amplified Spontaneous Emission in Paper", Sci. Rep. 9, 1 (2019), CrossRef J. Włodarski, M. Chychłowski, "Chemically tuned light source with an optical pump", Photonics Lett. Pol. 13(2), 46 (2021). CrossRef J. Żmojda, P. Miluski, M. Kochanowicz, J. Dorosz, A. Baranowska, M. Leśniak and D. Dorosz, "Luminescent properties of active optical fibers", Photonics Lett. Pol. 11(2), 50 (2019). CrossRef K. Jakubowski, W. Kerkemeyer, E. Perret, M. Heuberger, R. Hufenus, "Liquid-core polymer optical fibers for luminescent waveguide applications", Mater. Des. 196, 1 (2020). CrossRef V. Vladev, T. Eftimov, "Fiberized fluorescent dye microtubes", Proc. SPIE 8770, 87700V-1 (2013). CrossRef V. Vladev, T. Eftimov, W. Bock, "Broad-band fluorescent all-fiber source based on microstructured optical fibers", Photonics Lett. Pol., 7(2), 41 (2015). CrossRef V. Vladev, T. Eftimov, W. Bock, "Fluorescent all-fiber light source based on micro-capillaries and on microstructured optical fibers terminated with a microbulb", Opt. Comm. 356, 34 (2015). CrossRef V. Vladev, T. Eftimov, S. Nedev, "Excitation efficiency of a side-pumped fiberized fluorescent dye microcapillary", Opt. Fib. Tech. 28, 28 (2016). CrossRef V. Vladev, M. Todorova, V. Slavchev, M. Brazkova, E. Belina, S. Bozhkov, P. Radusheva, "A new basic structure suitable for a fully integrated all-fiber-optic stimulated emission dye source", J. Phys.: Conf. Ser. 1859 (012059), 1 (2021). CrossRef V. P. Vladev, M. M. Todorova, M. S. Brazkova, S. I. Bozhkov, "Diode-pumped all-fiber-optic liquid dye laser", Laser Phys. Lett. 18 (11), 115103 (2021), CrossRef G. Dyankov, T. A. Eftimov, N. Malinowski, E. Belina, H. Kisov, P. Mikulic, W. J. Bock, "A highly efficient biosensor based on MAPLE deposited hemoglobin on LPGs around phase matching turning point", Opt. Laser Technol. 123, 1 (2020). CrossRef T. Eftimov, G. Dyankov, A. Arapova, P. Kolev and V. Vladev, W4.73 , OFS-27, Optical Fiber Sensor Conference - 2022, 29 Aug- 2 Sept., The Westin Alexandria, Alexandria, Virginia, USA. CrossRef G. Rossi, J. Durek, S. Ojha, O. K. Schlüter, "Fluorescence-based characterisation of selected edible insect species: Excitation emission matrix (EEM) and parallel factor (PARAFAC) analysis", CRFS 4, 862 (2021). CrossRef L. Li, Y. Wang, W. Zhang, S. Yu, X. Wang, N. Gao, "New advances in fluorescence excitation-emission matrix spectroscopy for the characterization of dissolved organic matter in drinking water treatment: A review Author links open overlay panel", Chem. Eng. J. 381, 1 (2020). CrossRef

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.006
GPT teacher head0.206
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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