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Computer vision vs. spectrofluorometer-assisted detection of common nitro-explosive components with bola-type PAH-based chemosensors.
Kovalev, Igor S; Sadieva, Leila K; Taniya, Olga S; Yurk, Victoria M; Minin, Artem S; Santra, Sougata; Zyryanov, Grigory V; Charushin, Valery N; Chupakhin, Oleg N; Tsurkan, Mikhail V.
Afiliação
  • Kovalev IS; Ural Federal University named after the first President of Russia B. N. Yeltsin 19 Mira Str., K-2 Yekaterinburg 620002 Russian Federation ekls85@yandex.ru sougatasantra85@gmail.com gvzyryanov@gmail.com.
  • Sadieva LK; Ural Federal University named after the first President of Russia B. N. Yeltsin 19 Mira Str., K-2 Yekaterinburg 620002 Russian Federation ekls85@yandex.ru sougatasantra85@gmail.com gvzyryanov@gmail.com.
  • Taniya OS; I. Ya. Postovskiy Institute of Organic Synthesis, Ural Division of the Russian Academy of Sciences 22 S. Kovalevskoy Str. Yekaterinburg 620219 Russian Federation.
  • Yurk VM; Ural Federal University named after the first President of Russia B. N. Yeltsin 19 Mira Str., K-2 Yekaterinburg 620002 Russian Federation ekls85@yandex.ru sougatasantra85@gmail.com gvzyryanov@gmail.com.
  • Minin AS; I. Ya. Postovskiy Institute of Organic Synthesis, Ural Division of the Russian Academy of Sciences 22 S. Kovalevskoy Str. Yekaterinburg 620219 Russian Federation.
  • Santra S; Ural Federal University named after the first President of Russia B. N. Yeltsin 19 Mira Str., K-2 Yekaterinburg 620002 Russian Federation ekls85@yandex.ru sougatasantra85@gmail.com gvzyryanov@gmail.com.
  • Zyryanov GV; Ural Federal University named after the first President of Russia B. N. Yeltsin 19 Mira Str., K-2 Yekaterinburg 620002 Russian Federation ekls85@yandex.ru sougatasantra85@gmail.com gvzyryanov@gmail.com.
  • Charushin VN; M. N. Mikheev Institute of Metal Physics, Ural Branch of the Russian Academy of Sciences 18 S. Kovalevskoy Str Yekaterinburg 620219 Russian Federation.
  • Chupakhin ON; Ural Federal University named after the first President of Russia B. N. Yeltsin 19 Mira Str., K-2 Yekaterinburg 620002 Russian Federation ekls85@yandex.ru sougatasantra85@gmail.com gvzyryanov@gmail.com.
  • Tsurkan MV; Ural Federal University named after the first President of Russia B. N. Yeltsin 19 Mira Str., K-2 Yekaterinburg 620002 Russian Federation ekls85@yandex.ru sougatasantra85@gmail.com gvzyryanov@gmail.com.
RSC Adv ; 11(42): 25850-25857, 2021 Jul 27.
Article em En | MEDLINE | ID: mdl-35479431
ABSTRACT
Computer vision (CV) algorithms are widely utilized in imaging processing for medical and personal electronics applications. In sensorics CV can provide a great potential to quantitate chemosensors' signals. Here we wish to describe a method for the CV-assisted spectrofluorometer-free detection of common nitro-explosive components, e.g. 2,4-dinitrotoluene (DNT) and 2,4,6-trinitrotoluene (TNT), by using polyaromatic hydrocarbon (PAH, PAH = 1-pyrenyl or 9-anthracenyl) - based bola-type chemosensors. The PAH components of these chemical bolas are able to form stable, bright emissive in a visual wavelength region excimers, which allows their use as extended matrices of the RGB colors after imaging and digital processing. In non-polar solvents, the excimers have poor chemosensing properties, while in aqueous solutions, due to the possible micellar formation, these excimers provide "turn-off" fluorescence detection of DNT and TNT in the sub-nanomolar concentrations. A combination of these PAH-based fluorescent chemosensors with the proposed CV-assisted algorithm offers a fast and convenient approach for on-site, real-time, multi-thread analyte detection without the use of fluorometers. Although we focus on the analysis of nitro-explosives, the presented method is a conceptual work describing a general use of CV for quantitative fluorescence detection of various analytes as a simpler alternative to spectrofluorometer-assisted methods.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: RSC Adv Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: RSC Adv Ano de publicação: 2021 Tipo de documento: Article