ABSTRACT
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. However, the proposed method is applicable for optical imaging of any fluorescent nanoparticles used as theranostic agents in biological tissue.
Subject(s)
Nanocomposites/chemistry , Nanoparticles/chemistry , Neural Networks, Computer , Graphite/chemistry , Optical Imaging , Polyethylene Glycols/chemistry , Polymers/chemistryABSTRACT
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. Application of the MCR-ALS method allowed for each spectrum to extract contributions of molecules with different strengths of hydrogen bonding. The energy (enthalpy) of formation/weakening of hydrogen bonds was calculated using the slope of Van't Hoff plot. The energy of hydrogen bonding in 20 vol % of ethanol was found the highest among all the samples. This finding further supports appearance of clathrate-like structures in water-ethanol solutions with concentrations around 20 vol % of ethanol.
ABSTRACT
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 1.5 times.
