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The possibility of improving the properties of the standard polymeric and a water-based coating for military camouflage protection by adding nanoparticles of inorganic fulerene-like tungsten disulphide (IF-WS 2). Nanoparticles were added and dispersed in paint by ultrasonic irradiation. The paints were applied to standard steel plates, and dried, for examinations of chemical resistance to agressive media and of the following physical-mechanical properties: hardness, flexibility, elasticity, abrasion resistance, resistance to steel balls impact and adherence. These properties were compared for paint without and with IF-WS 2 nanoparticles. The effect of adding IF-WS 2 on rheological properties of the paints has been examined using Dynamic Mechanical Analysis (DMA), observing viscosity as a function of the shear rate. Camouflage properties were also examined-IR reflection and colorimetry. Significant improvements of mechanical resistance to abrasion and steel balls impact, as well as hardness, have been achieved

Armies of powerful armed forces are already applying achievements of nanoscience and supporting researches in the field of nanotechnology. This paper surveys the recent research in the area of nanomaterials application in defence technologies, conducted in the Military Technical Institute in Belgrade, Serbia. This research covers the most important results obtained so far in the following areas: chemical biological, radiological and nuclear protection (CBRN protection), nanomodified polymer coatings and camouflage paints, composite structures for military aircraft, ballistic protection composites, and energetic materials. Researches gave promising results in all the named fields and encourage nanomaterials application in future.

The application of the principal component analysis and artificial neural network method in forecasting 137Cs behaviour in the air as the function of meteorological parameters is presented. The model was optimized and tested using 137Cs specific activities obtained by standard gamma-ray spectrometric analysis of air samples collected in Belgrade (Serbia) during 2009-2011 and meteorological data for the same period. Low correlation (r = 0.20) between experimental values of 137Cs specific activities and those predicted by artificial neural network was obtained. This suggests that artificial neural network in the case of prediction of 137Cs specific activity, using temperature, insolation, and global Sun warming does not perform well, which can be explained by the relative independence of 137Cs specific activity of particular meteorological parameters and not by the ineffectiveness of artificial neural network in relating these parameters in general. [Projekat Ministarstva nauke Republike Srbije, br. TR34034]