Self-sustaining exothermal reaction of synthesis in multilayer foil consisting of intermetallic forming elements may proceed by means of self-propagation of high-temperature synthesis reaction front through foil (self-propagating high-temperature synthesis (SHS) or as a result of synthesis reaction running through the entire volume (autoinflammation (AI)). The latter is realized under the condition of foil heating up to a certain critical temperature, at which the synthesis reaction runs in the entire volume without external heat supply. In the work influence of foil heating rate on AI temperature was studied in the case of Ti/Al multilayer foil. It is shown that there exists a certain critical heating rate, below which foil AI is not observed, and at heating rates above the critical rate AI temperature decreases with increase of heating rate. Dependence of AI temperature on heating rate is nonmonotonic: at low heating rates AI temperature abruptly decreases, and at further increase of heating rate AI temperature remains practically unchanged. Such nonmonotonic dependence of foil AI temperature on heating rate is associated with running of the process of thermally activated solid phase reactions in it, which are accompanied by formation of intermetallic interlayers on the boundary between titanium and aluminium layers, preventing diffusion mixing of elements. With increase of heating rate, interlayer thickness decreases, promoting AI temperature lowering. 11 Ref., 4 Figures.

Convolutional neural networks trained using supervised learning can improve visual perception for human-robot walking. These advances have been possible due to large-scale datasets like ExoNet and StairNet - the largest open-source image datasets of real-world walking environments. However, these datasets require vast amounts of manually annotated data, the development of which is time consuming and labor intensive. Here we present a novel semi-supervised learning system (ExoNet-SSL) that uses over 1.2 million unlabelled images from ExoNet to improve training efficiency. We developed a deep learning model based on mobile vision transformers and trained the model using semi-supervised learning for image classification. Compared to standard supervised learning (98.4%), our ExoNet-SSL system was able to maintain high prediction accuracy (98.8%) when tested on previously unseen environments, while requiring 35% fewer labelled images during training. These results show that semi-supervised learning can improve training efficiency by leveraging large amounts of unlabelled data and minimize the size requirements for manually annotated images. Future research will focus on model deployment for onboard real-time inference and control of human-robot walking.

Current and prospective trends in application of metallic nanomaterials have been studied. The study has been conducted within the Nanoroad SME European project – as the first step for a roadmap for industrial application of nanomaterials. The web page of the project is http://www.nanoroad.net/. The present report presents an analysis of patents, papers, national and European projects in the field of nano-metals, and also an analysis of the present state of research and expected trends in this domain. Based on the performed analysis a data base of nanomaterials has been developed as well as roadmaps with expected time to applications. It can be found under http://bourgogne.arist.tm.fr/nanoroadsme/home/. The roadmap is mainly addressed to SMEs to help them to decide about applications or production of nanomaterials.

Temperature conditions for initiation of thermal explosion (volume reaction) in multilayer Ti/Al nanofoils were explored both experimentally and by numerical simulation. It is shown that there exists a crit ical heating rate below which thermal explosion of foil is not observed. Upon heating at rates above the critical value, the initiation temperature of thermal explosion (T ig ) was found to decrease with increasing heating rate (V h ). A non monotonous dependence of T ig on V h was associated with formation of an intermetallic interlayer at the interface between Ti and Al layers in the solid state reaction thermally induced during heating. Numer ical simulation has shown that the interlayer gets thicker with decreasing heating rate, thus promoting an increase in T ig .

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