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Zhao Linjie,Yang Mao,Xiao Chengjian,Gong Yu,Ran Guangming,Chen Xiaojun,Li Jiamao,Yue Lei,Chen Chao,Hou Jingwei,Wang Heyi,Long Xinggui,Peng Shuming 한국원자력학회 2024 Nuclear Engineering and Technology Vol.56 No.1
Understanding the tritium release and retention behavior of candidate tritium breeder materials is crucial for breeder blanket design. Recently, a melt spraying process was developed to prepare Li4SiO4 pebbles, which were subsequently subjected to the in-pile tritium production and extraction platform in China Mianyang Research Reactor (CMRR) to investigate their in-situ tritium release behavior and irradiation performance. The results demonstrate that HT is the main tritium release form, and adding hydrogen to the purge gas reduces tritium retention while increasing the HT percent in the purge gas. Post-irradiation experiments reveal that the irradiated pebbles darken in color and their grains swell, but the mechanical properties remain largely unchanged. It is concluded that the tritium residence time of Li4SiO4 made by melt spraying method at 467 ◦C is approximately 23.34 h. High-density Li4SiO4 pebbles exhibit tritium release at relatively low temperatures (<600 ◦C) that is mainly controlled by bulk diffusion. The diffusion coefficient at 525 ◦C and 550 ◦C is 1.19 × 10 11 cm2/s and 5.34 × 10 11 cm2/s, respectively, with corresponding tritium residence times of 21.3 hours and 4.7 hours.
3D CAD Model Classification with Deep Neural Networks
Feiwei Qin,Luye Li,Shuming Gao,Xiaoling Yang,Xiang Chen (사)한국CDE학회 2013 한국CAD/CAM학회 국제학술발표 논문집 Vol.2010 No.8
Model classification is essential to the management and reuse of 3D CAD models. Manual model classification is laborious and error prone. At the same time, the automatic classification methods are scarce due to the intrinsic complexity of 3D CAD models. In this paper, we propose an automatic 3D CAD model classification approach based on deep neural networks. According to prior knowledge of CAD domain, features are selected and extracted from 3D CAD models first, then preprocessed as high dimensional input vectors for category recognition. Furthermore, by anal ogy with the thinking process of engineers, a deep network classifier for 3D CAD models is constructed with the aid of deep learning techniques. To get an optimal solution, multiple strategies are appropriately chosen and applied in the training phase, which makes our classifier achieve better performance. We demonstrate the efficiency and effectiveness of our approach through experiments on 3D CAD model datasets.
Yuanping Sun,Yuanping Sun,조용훈,Hui Wang,Lili Wang,Shuming Zhang,Hui Yang 한국물리학회 2010 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.57 No.1
InN nanostructures with and without GaN capping layers were grown by using metal-organic chemical vapor deposition. Morphological, structural, and optical properties were systematically studied by using atomic force microscopy, X-ray diffraction (XRD) and temperature-dependent photoluminescence (PL). XRD results show that an InGaN structure is formed for the sample with a GaN capping layer, which will reduce the quality and the IR PL emission of the InN. The lower emission peak at 0.7 eV was theoretically fitted and assigned as the band edge emission of InN. Temperature-dependent PL shows a good quantum efficiency for the sample without a GaN capping layers; this corresponds to a lower density of dislocations and a small activation energy.
Tong Liu,Rong Huang,Fangsen Li,Zengli Huang,Jian Zhang,Jianping Liu,Liqun Zhang,Shuming Zhang,An Dingsun,Hui Yang 한국물리학회 2018 Current Applied Physics Vol.18 No.7
The accuracy and error propagation for determining the low specific contact resistance of Ohmic contacts on IIIV wide band-gap semiconductors based on the circular transmission line model have been analyzed and the validity of this method is discussed in detail. The accuracy is more susceptible to the factors including data fitting method, electrical measurement technique and contact area correction. By using the equations of the original circular transmission line model to extract the fitting parameters, the calculation accuracy is much improved and the inapplicability of the linear least-square fitting is prevented. To further improve the accuracy, a four-probe current-voltage measurement technique was adopted to reduce the parasitic series resistances and the uncertainty bound, especially for the Ohmic contact with low sheet resistance of the semiconductor. Moreover, we have studied the size effect of contact pads of patterns and demonstrated that contact area correction is necessary for the semiconductor with high sheet resistance. A comprehensive error analysis is also performed to fully understand all the impact factors on this advanced method of specific contact resistance measurement, which is benefit for device performance evaluation and failure analysis.
Optical properties of InN rods on sapphire grown by metal–organic chemical vapor deposition
Sun, Yuanping,Cho, Yong-Hoon,Dai, Zhenhong,Wang, Weitian,Wang, Hui,Wang, Lili,Zhang, Shuming,Yang, Hui Elsevier 2010 Physica E, Low-dimensional systems & nanostructure Vol.43 No.1
<P><B>Abstract</B></P><P>The InN rods were grown by metal–organic chemical vapor deposition with a density of 1.4×10<SUP>9</SUP>cm<SUP>−2</SUP>. Optical properties of InN rods have been systematically investigated by means of temperature dependent photoluminescence (PL) and power dependent PL. Four peaks appear in the PL spectra and the origination was analyzed. The lowest energy peak P1 (0.665eV) is attributed to transitions of conduction band electrons to the photo-holes captured by deep acceptor; P2 (0.717eV) is the direct band-to-band transition peak of InN; main peak P3 (0.759eV) results from the recombination of degenerate electrons with photo-holes near the top of the valence band (Burstein–Moss effects); the high energy shoulder P4 (0.787eV) was by the co-effect of quantum confinement and the Burstein–Moss effects due to the small size distribution of InN wetting layers.</P>