Electronics, and nanoelectronics in particular, represent one of the most promising branches of technology. The search for novel and more efficient materials seems to be natural here. Thus far, silicon-based devices have been monopolizing this domain. Indeed, it is justified since it allows for significant miniaturization of electronic elements by their densification in integrated circuits. Nevertheless, silicon has some restrictions. Since this material is applied in the bulk form, the miniaturization limit seems to be already reached. Moreover, smaller silicon-based elements (mainly processors) need much more energy and generate significantly more heat than their larger counterparts. In our opinion, the future belongs to nanostructured materials where a proper structure is obtained by means of bottom-up nanotechnology. A great example of a material utilizing nanostructuring is mesoporous silica, which, due to its outstanding properties, can find numerous applications in electronic devices. This focused review is devoted to the application of porous silica-based materials in electronics. We guide the reader through the development and most crucial findings of porous silica from its first synthesis in 1992 to the present. The article describes constant struggle of researchers to find better solutions to supercapacitors, lower the k value or redox-active hybrids while maintaining robust mechanical properties. Finally, the last section refers to ultra-modern applications of silica such as molecular artificial neural networks or super-dense magnetic memory storage.

Silver and its nanoparticles (AgNPs) have different faces, providing different applications. In recent years, the number of positive nanosilver applications has increased substantially. It has been proven that AgNPs inhibit the growth and survival of bacteria, including human and animal pathogens, as well as fungi, protozoa and arthropods. Silver nanoparticles are known from their antiviral and anti-cancer properties; however, they are also very popular in medical and pharmaceutical nanoengineering as carriers for precise delivery of therapeutic compounds, in the diagnostics of different diseases and in optics and chemistry, where they act as sensors, conductors and substrates for various syntheses. The activity of AgNPs has not been fully discovered; therefore, we need interdisciplinary research to fulfil this knowledge. New forms of products with silver will certainly find application in the future treatment of many complicated and difficult to treat diseases. There is still a lack of appropriate and precise legal condition regarding the circulation of nanomaterials and the rules governing their safety use. The relatively low toxicity, relative biocompatibility and selectivity of nanoparticle interaction combined with the unusual biological properties allow their use in animal production as well as in bioengineering and medicine. Despite a quite big knowledge on this topic, there is still a need to organize the data on AgNPs in relation to specific microorganisms such as bacteria, viruses or fungi. We decided to put this knowledge together and try to show positive and negative effects on prokaryotic and eukaryotic cells.

In the present paper, the completely innovative architecture of artificial neural network based on Hopfield structure for solving a stereo-matching problem—hybrid neural network, consisting of the classical analog Hopfield neural network and the Maximum Neural Network—is described. The application of this kind of structure as a part of assistive device for visually impaired individuals is considered. The role of the analog Hopfield network is to find the attraction area of the global minimum, whereas Maximum Neural Network is finding accurate location of this minimum. The network presented here is characterized by an extremely high rate of work performance with the same accuracy as a classical Hopfield-like network, which makes it possible to use this kind of structure as a part of systems working in real time. The network considered here underwent experimental tests with the use of real stereo pictures as well as simulated stereo images. This enables error calculation and direct comparison with the classic analog Hopfield neural network as well as other networks proposed in the literature.