Three reversible states controlled on a gold monoatomic contact by the electrochemical potential

Conductance of an Au mono atomic contact was investigated under the electrochemical potential control. The Au contact showed three different behaviors depending on the potential: 1 $G_{0}$ ($G_{0}$ = $2e^{2}/h$), 0.5 $G_{0}$ and not-well defined values below 1 $G_{0}$ were shown when the potential of the contact was kept at -0.6 V (double layer potential), -1.0 V (hydrogen evolution potential), and 0.8 V (oxide formation potential) versus Ag/AgCl in 0.1 M Na$_{2}$SO$_{4}$ solution, respectively. These three reversible states and their respective conductances could be fully controlled by the electrochemical potential. These changes in the conductance values are discussed based on the proposed structure models of hydrogen adsorbed and oxygen incorporated on an Au mono atomic contact.Comment: 8 pages, 4 figures, to be appeared in Physical Review

Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning

Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development

Electrical Properties of Monocrystalline Thin Film Si for Solar Cells Fabricated By Rapid Vapor Deposition with Nano-Surface Controlling Double Layer Porous Si in H2

International audienceIntroduction To reduce the Si thickness with maintaining the high quality is a promising approach to reduce the cost of monocrystalline Si solar cell. A major method to fabricate monocrystalline thin Si is epitaxy by Chemical Vapor Deposition (CVD) and Layer Transfer Process (LTP) as shown in Fig. 1. A seed layer and a sacrificial layer such as double layer porous Si (DLPS) which consist of a Low Porous Layer (LPL) and a High Porous Layer (HPL) are fabricated on the surface of a monocrystalline Si wafer, and then Si is epitaxially deposited on the seed layer. This wafer can then be reused in LTP, thus further reducing the material cost of these Si cells. There remain two challenging issues: (ⅰ) crystal defect introduced during epitaxy caused by the roughness of the seed layer 1) and (ⅱ) low deposition rate and yield of epitaxy by CVD. To solve problem (ⅰ), we proposed a Zone Heating Recrystallization (ZHR) method 2) to smoothen the DLPS surface as shown in Fig.2. The structure of the DLPS surface can be modified by using an upper lamp heater to scan the surface in one direction and a bottom heater to pre-heat Si substrate. To solve problem (ⅱ), we proposed a Rapid Vapor Deposition (RVD) method 3) as shown in Fig.3. By depositing Si under a high vapor pressure by heating the source Si to over 2000℃, the deposition rate of over 10 μm/min with a higher yield is achieved. By applying both the ZHR and RVD methods, we successfully reduced the roughness of a DLPS surface and obtained monocrystalline Si with Si wafer level. The critical effect of lowering the roughness of a DLPS surface to R ms < 0.3 nm wa