In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256  256 pixels (high resolution: HR) from TEM measurements and 32  32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO4 nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

In this study, nano-scale copper powders were reduction treated in a hydrogen atmosphere at the relativelyhigh temperature of 350℃ in order to eliminate surface oxide layers, which are the main obstacles for fabricating anano/ultrafine grained bulk parts from the nano-scale powders. The changes in composition and microstructure beforeand after the hydrogen reduction treatment were evaluated by analyzing X-ray diffraction (XRD) line profile patternsusing the convolutional multiple whole profile (CMWP) procedure. In order to confirm the result from the XRD lineprofile analysis, transmitted electron microscope observations were performed on the specimen of the hydrogen reduc-tion treated powders fabricated using a focused ion beam process. A quasi-statically compacted specimen from the nano-scale powders was produced and Vickers micro-hardness was measured to verify the potential of the powders as thebasis for a bulk nano/ultrafine grained material. Although the bonding between particles and the growth in size of theparticles occurred, crystallites retained their nano-scale size evaluated using the XRD results. The hardness results dem-onstrate the usefulness of the powders for a nano/ultrafine grained material, once a good consolidation of powders isachieved.

We present a method of graphene synthesis with high thickness uniformity using the thermal chemical vapor deposition (TCVD) technique; we demonstrate its application to a grid supporting membrane using transmission electron microscope (TEM) observation, particularly for nanomaterials that have smaller dimensions than the pitch of commercial grid mesh. Graphene was synthesized on electron-beam-evaporated Ni catalytic thin films. Methane and hydrogen gases were used as carbon feedstock and dilution gas, respectively. The effects of synthesis temperature and flow rate of feedstock on graphene structures have been investigated. The most effective condition for large area growth synthesis and high thickness uniformity was found to be 1000˚C and 5 sccm of methane. Among the various applications of the synthesized graphenes, their use as a supporting membrane of a TEM grid has been demonstrated; such a grid is useful for high resolution TEM imaging of nanoscale materials because it preserves the same focal plane over the whole grid mesh. After the graphene synthesis, we were able successfully to transfer the graphenes from the Ni substrates to the TEM grid without a polymeric mediator, so that we were able to preserve the clean surface of the as-synthesized graphene. Then, a drop of carbon nanotube (CNT) suspension was deposited onto the graphene-covered TEM grid. Finally, we performed high resolution TEM observation and obtained clear image of the carbon nanotubes, which were deposited on the graphene supporting membrane.