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Computer vision vs. spectrofluorometer-assisted detection of common nitro-explosive components with bola -type PAH-based chemosensors.

Igor S KovalevLeila K SadievaOlga S TaniyaVictoria M YurkArtem S MininSougata SantraGrigory V ZyryanovValery N CharushinOleg Nikolaevich ChupakhinMikhail V Tsurkan
Published in: RSC advances (2021)
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.
Keyphrases
  • label free
  • loop mediated isothermal amplification
  • real time pcr
  • high resolution
  • deep learning
  • machine learning
  • polycyclic aromatic hydrocarbons
  • ionic liquid
  • single molecule
  • risk assessment
  • living cells