β- PbO 2 nanobelts, with a rectangular cross section, a typical length of 10–200 μm, a width of 50–300 nm, and a width-to-thickness ratio of 5–10, have been successfully synthesized by simple elevated evaporation of commercial PbO powders at high temperature. The PbO2 nanobelts are enclosed by top surfaces ±(201) and side surfaces ±(101̄) and their growth direction is [010]. Each PbO2 nanobelt is found to have a large polyhedral Pb tip at one of its ends, suggesting the growth is dominated by a vapor–liquid–solid mechanism. Electron beam irradiation of the PbO2 nanobelts results in the phase transformation from PbO2 to PbO and finally to Pb.
Abstract The study presents the fundamental scientific understanding of electron transfer in contact electrification in solid–solid and liquid–solid cases and a newly revised model for the formation of electric double layer. The potential revolutionary impacts of triboelectric nanogenerators as energy sources and sensors are presented in the fields of health care, environmental science, wearable electronics, internet of things, human–machine interfacing, robotics, and artificial intelligence.
Abstract Dynamical theories are developed to calculate the diffraction intensities of double-inelastically scattered high-energy electrons (i.e. the electrons that have been inelastically scattered twice as the result of exciting two distinct crystal states) in crystals within the framework of quantum mechanics. These theories are needed to quantify the data of thermal diffusely scattered electron energy loss spectroscopy. The Bloch wave and Green's function approaches proposed here take into account the full dynamical diffraction effects of the electrons before and after each inelastic event. The Bloch wave theory gives a full three-dimensional description of double-inelastic scattering in crystals including higher-order Laue zone reflections, but it may not be convenient to calculate the intensity of high-angle thermal diffuse scattering of electrons because of the difficulty of including all of the possible multiphonon excitations. Green's function theory, as an alternative approach, is most suitable for integrating the contributions made by all possible multiphonon excitations. Details are given based on the first principle considerations of the inelastic processes. The diffraction intensity derived based on the Green function approach has been given in explicit analytical forms that are adequate for numerical calculations.
Neural networks have potential advantages such as real-time operation and robustness based on their parallel structure, self-organization, fuzziness, and particularly their adaptive learning ability. A single neural network is useful for identification of objects. To carry out identifying complex objects, however, it is necessary to consider hybrid architectures of two or more networks, which offer some degrees of improvement in performances. In this paper, neural learning techniques, the self-organizing feature mapping (SOFM), and learning vector quantization (LVQ2) have been applied to the automatic target recognition problem in the presence of a satellite object with high level noises. SOFM, unsupervised learning captures the homogeneity within-class characteristics; whereas LVQ2, supervised learning captures the heterogeneity of between-class.
