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Ferrite-containing polymer composites are of great interest for the development of radar-absorbing and -shielding materials (RAMs and RSMs). The main objective of RAM and RSM development is to achieve a combination of efficient electromagnetic wave (EMW) absorption methods with advantageous technological and mechanical properties as well as acceptable weight and dimensions in the final product. This work deals with composite RAMs and RSMs containing spinel-structured ferrites. These materials are chosen since they can act as efficient RAMs in the form of ceramic plates and as fillers for radar-absorbing polymer composites (RAC) for electromagnetic radiation (EMR). Combining ferrites with conducting fillers can broaden the working frequency range of composite RAMs due to the activation of various absorption mechanisms. Ferrite-containing composites are the most efficient materials that can be used as the working media of RAMs and RSMs due to a combination of excellent dielectric and magnetic properties of ferrites. This work contains a brief review of the main theoretical standpoints on EMR interaction with materials, a comparison between the radar absorption properties of ferrites and ferrite-polymer composites and analysis of some phenomenological aspects of the radar absorption mechanisms in those composites.

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., (Copyright © 2018 Elsevier Inc. All rights reserved.)