A method for optical imaging and monitoring of the excretion of fluorescent nanocomposites from the body using artificial neural networks.

In this study, a new approach to the implementation of optical imaging of fluorescent nanoparticles in a biological medium using artificial neural networks is proposed. The studies were carried out using new synthesized nanocomposites - nanometer graphene oxides, covered by the poly(ethylene imine)-poly(ethylene glycol) copolymer and by the folic acid. We present an example of a successful solution of the problem of monitoring the removal of nanocomposites based on nGO and their components with urine using fluorescent spectroscopy and artificial neural networks.

Raman Spectroscopy of Water-Ethanol Solutions: The Estimation of Hydrogen Bonding Energy and the Appearance of Clathrate-like Structures in Solutions.

The structure of aqueous alcohol solutions at the molecular level for many decades has remained an intriguing topic in numerous theoretical and practical investigations. The aberrant thermodynamic properties of water-alcohol mixtures are believed to be caused by the differences in energy of hydrogen bonding between water-water, alcohol-alcohol, and alcohol-water molecules. We present the Raman scattering spectra of water, ethanol, and water-ethanol solutions with 20 and 70 vol % of ethanol thoroughly measured and analyzed at temperatures varying from -10 to +70 °C.

Optical imaging of fluorescent carbon biomarkers using artificial neural networks.

The principle possibility of extraction of fluorescence of nanoparticles in the presence of background autofluorescence of a biological environment using neural network algorithms is demonstrated. It is shown that the methods used allow detection of carbon nanoparticles fluorescence against the background of the autofluorescence of egg white with a sufficiently low concentration detection threshold (not more than 2 μg/ml for carbon dots 3 μg/ml and for nanodiamonds). It was also shown that the use of the input data compression can further improve the accuracy of solving the inverse problem by 1.