Well-crystallized bismuth tungstate (Bi2WO6) powders with different morphologies were successfully synthesized via a low-temperature molten salt method. The powders were characterized by X-ray diffraction, transmission electron microscopy, and UV-Vis, respectively. It was found that the variation of morphology of the obtained Bi2WO6 powder mainly depends on the different reaction temperatures and the weight ratio of LiNO3–NaNO3 salt to precursor. In addition, the UV-Visible absorption spectra showed that the synthesized powders had strong light absorption properties not only in the ultraviolet light but also in the visible light region. 相似文献
CeO2 particles confined within the pores of an SBA-15 mesoporous silica host were prepared by incipient wetness impregnation (IMP)
and deposition precipitation (DP) methods. The materials were characterized by XRD, N2-adsorption and temperature programmed reduction (TPR) to evaluate the structure, texture, and redox properties. The preparation
procedure had significant impact on the assembling mode of CeO2 inside the SBA-15 mesopores. A high dispersion of CeO2 particles was achieved via DP, whereas the dispersion of CeO2 prepared by IMP was found to be inhomogeneous and CeO2 partially blocked the pores. The CO conversion in the water-gas-shift reaction was enhanced over 1 wt% Pt supported on CeO2-modified SBA-15 obtained by DP. 相似文献
A new type of high temperature energy storage material was obtained through the melt infiltration method, using compounding
SiC ceramic foam as matrix and Na2SO4 as phase change material. The resulting composite material was measured by XRD, SEM, TG-DSC methods. The experimental results
indicate that the composite is composed of silicon carbide, sodium sulfate and square quartz, and no chemical reactions occurs
between Na2SO4 and SiC matrix. Na2SO4 has a good bonding with the SiC ceramic foam matrix. As the composite material is characterized by high thermal energy storage
density and high thermal conductivity, it is suit for energy storage under high temperature.
Funded by the “863” Hi-Tech Research and Development Program of China (2008AA05Z418) 相似文献
Neural Processing Letters - In this article, the finite time (FT) synchronization problem of fractional order quaternion valued neural networks with time delay is investigated. Without separating... 相似文献
Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
