Machine learning algorithms to control concentrations of carbon nanocomplexes in a biological medium via optical absorption spectroscopy: how to choose and what to expect?

A solution of spectroscopic inverse problems, implying determination of target parameters of the research object via analysis of spectra of various origins, is an overly complex task, especially in case of strong variability of the research object. One of the most efficient approaches to solve such tasks is use of machine learning (ML) methods, which consider some unobvious information relevant to the problem that is present in the data. Here, we compare ML approaches to the problem of nanocomplex concentrations determination in human urine via optical absorption spectra, perform preliminary analysis of the data array, find optimal parameters for several of the most popular ML methods, and analyze the results.

In vitro temperature sensing with up-conversion NaYF:Yb/Tm-based nanocomposites: Peculiarities and pitfalls.

The luminescence intensity ratio method, exploiting the temperature-dependent luminescence of the thermally coupled energy levels, is regarded as a very promising approach for optical temperature measurement at the cellular level. In this study, it was found that bare NaYF:Yb/Tm nanoparticles cannot be used as a cellular thermosensor in principle because of their tendency to aggregate, which significantly affects the luminescent properties of the complex, introducing uncertainty in the intensity ratio measurement. NaYF:Yb/Tm up-conversion nanoparticles, coated with polyethylene glycol (PEG) and carboxyl groups (COOH), on the other hand, proved to be promising candidates for the role of thermosensors.

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.