The directional motion of micro- and nanoparticles can be induced not only directly due to the effect of forces with a nonzero average value, which set the direction of the motion, but also, in the absence of such forces in systems with broken mirror symmetry, under the effect of nonequilibrium fluctuations of various natures (the motor or ratchet effect). Unlike other reviews on nanoparticle transport, we focus on the principles of nanotransport control by means of the ratchet effect, which has numerous practical applications and, in particular, is a promising mechanism for targeted delivery of drugs in living organisms. We explain in detail various techniques to arrange directional motion in asymmetric media by means of rectification of the nonequilibrium fluctuations that supply energy to the system and feature a zero average value of applied forces, whether actual or generalized. We consider in depth the properties and characteristics of ratchet systems, their dependences on temperature, load forces, and features of the periodic potential profile in which nanoparticles move, such as the frequency of fluctuations of this profile and its spatial and time asymmetry. A systematic description of factors that determine the direction of motion of ratchet systems is presented.
Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.
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