Nanophase materials and nanocomposites, characterized by an ultra fine grain size (less than 100 nm) have attracted wide spread interest in recent years by virtue of their unusual mechanical, electrical, optical, magnetic, and energetic properties. Studies have shown that the thermal behavior of nano‐scaled materials is quite different from micron‐sized powders. Nanosized metallic and explosive powders have been used as solid propellant and explosive mixtures to increase efficiency. At the same time recent studies reveal that the presence of nanosized metals in propellants does not necessary translate into an increased burning rate and burning temperature. The reasons of this effect are far from being clear. This paper presents a new approach to the production of nanocomposites of some energetic materials – ammonium nitrite, cyclotrimethylene trinitramine (RDX), and aluminum – by the vacuum co‐deposition technique. The thermal behavior of the synthesized nanopowder and nanocomposites is investigated. A substantial difference in burning rate of RDX nanopowder has been found in comparison to micron‐sized material. Experimental results allow investigating the effects of nanosized materials on the combustion characteristics.

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Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence systems with efficient information processing. However, a major bottleneck in the physical implementation of these systems is the strong dependence of their performance on the unavoidable variations (cycle‐to‐cycle, c2c, or device‐to‐device, d2d) of memristive devices. Recently, reservoir computing (RC) and spiking neuromorphic systems (SNSs) are separately proposed as valuable options to partially mitigate this problem. Herein, both approaches are combined to create a fully organic system based on 1) volatile polyaniline memristive devices for the reservoir layer and 2) nonvolatile parylene memristors for the SNS readout layer. This combination provides a simpler SNS training procedure compared with the formal neural networks and results in greater robustness to device variability, while ensuring the extraction and encoding of the input critical features (performed by the polyaniline reservoir) and the analysis and classification performed by the SNS layer. Furthermore, the spatiotemporal pattern recognition of the system brings us closer to the implementation of efficient and reliable brain‐inspired computing systems built with partially unreliable analog elements.

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