Cross-sectional analysis of W-cored Ni nanoparticle via focused ion beam milling with impregnation

Tungsten and nickel bimetallic nanoparticle is synthesized by radio frequency thermal plasma process which belongs to the vapor phase condensation technology. The morphology and chemical composition of the synthesized particle were investigated using the conventional nanoparticle transmission electron microscopy (TEM) sample. A few part of them looked like core/shell structured particle, but ambiguities were caused by either TEM sample preparation or TEM analysis. In order to clarify whether a core/shell structure is developed for the particle, various methodologies were tried to prepare a cross-sectional TEM sample. Focused ion beam (FIB) milling was conducted for cold-compacted particles, dispersed particles on silicon wafer, and impregnated particles with epoxy which is compatible with electron beam. A sound cross-sectional sample was just obtained from cyanoacrylate impregnation and FIB milling procedure. A tungsten-cored nickel shell structure was precisely confirmed with aid of cross-sectional sample preparation method

Influence of combinatory effects of STEM setups on the sensitivity of differential phase contrast imaging

Differential phase-contrast (DPC) imaging in the scanning transmission electron microscopy (STEM) mode has been suggested as a new method to visualize the nanoscale electromagnetic features of materials. However, the quality of the DPC image is very sensitive to the electron-beam alignment, microscope setup, and specimen conditions. Unlike normal STEM imaging, the microscope setup variables in the DPC mode are not independent; rather, they are correlated factors decisive for field sensitivity. Here, we systematically investigated the independent and combinatory effects of microscope setups on the sensitivity of the DPC image in a hard magnet, Nd2Fe14B alloy. To improve sensitivity, a smaller overlap of the electron beam with annular detectors and a greater camera length were required. However, these factors cannot be controlled independently in the two-condenser-lens system. In this linked system, the effect of the camera length on the DPC sensitivity was slightly more predominant than the overlap. Furthermore, the DPC signal was noisy and scattered at a small overlap of less than 11%. The electron-beam current does not evidently affect the sensitivity. In addition, the DPC sensitivity was examined with respect to the sample thickness, and the optimum thickness for high sensitivity was approximately 65 nm for the hard magnetic material Nd2Fe14B. This practical approach to the STEM setup and sample thickness may provide experimental guidelines for further application of the DPC analysis method

Atomic and electronic reconstruction at the a-LAO/STO interface by e-beam induced crystallization

An abstract is not available for this content so a preview has been provided. As you have access to this article, a PDF of this content is available in through the ‘Save PDF’ action button.11Nsciescopu

An artificial neural tactile sensing system

Humans detect tactile stimuli through a combination of pressure and vibration signals using different types of cutaneous receptor. The development of artificial tactile perception systems is of interest in the development of robotics and prosthetics, and artificial receptors, nerves and skin have been created. However, constructing systems with human-like capabilities remains challenging. Here, we report an artificial neural tactile skin system that mimics the human tactile recognition process using particle-based polymer composite sensors and a signal-converting system. The sensors respond to pressure and vibration selectively, similarly to slow adaptive and fast adaptive mechanoreceptors in human skin, and can generate sensory neuron-like output signal patterns. We show in an ex vivo test that undistorted transmission of the output signals through an afferent tactile mouse nerve fibre is possible, and in an in vivo test that the signals can stimulate a rat motor nerve to induce the contraction of a hindlimb muscle. We use our tactile sensing system to develop an artificial finger that can learn to classify fine and complex textures by integrating the sensor signals with a deep learning technique. The approach can also be used to predict unknown textures on the basis of the trained model. A tactile sensing system that can learn to identify different types of surface can be created using sensors that mimic the fast and slow responses of mechanoreceptors found in human skin