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RISS 인기검색어
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- 저자 : Dong Jinxiang (검색결과 5 건)
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Angang Dong,Wenqian Han,Yuchi Yang,Baixu Zhu,Biwei Wang,Jinxiang Zou 나노기술연구협의회 2019 Nano Convergence Vol.6 No.4
Recently, iron, nitrogen and sulfur codoped carbon-based materials have gained increasing attention for their synergistic effect towards superior electrocatalytic oxygen reduction performance. To gain insight into the contributions of the heteroatoms, we developed a facile and reproducible method for constructing Fe, N, S-codoped carbon frameworks derived from self-assembled Fe3O4 nanocrystal superlattices. The material constructed by the suggested method exhibited excellent ORR activity with more positive half-wave potential (∼ 0.869 V, vs RHE), higher diffusion-limiting current density (∼ 5.88 mA/cm2) and smaller Tafel slope (45 mV/dec) compared with Fe, N-codoped carbon frameworks and Pt/C. Notably, Fe3O4 nanocrystals served as both the building blocks for constructing carbon frameworks and the source of Fe residues leaving in the frameworks at the same time. By artificially tailoring the doping type and level as well as the homogeneousness of heteroatoms, the results discussed herein prove the importance of each kind of heteroatom in boosting ORR activity.
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Workload Characterization on a Cloud Platform: An Early Experience
Zujie Ren,Jinxiang Dong,Yongjian Ren,Renjie Zhou,Xindong You 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.6
Understanding the characteristics of cloud workloads is the key to making optimal configuration decisions and improving the system throughput. However workload characterization of cloud, especially in a large-scale production environment, has not been well studied yet. To gain insights on cloud workloads, we collected a one-week workload trace from a 100-node cloud cluster which hosts 1082 virtual machines. We characterized the workload at the granularity of virtual machines and physical nodes, respectively. We concluded with a set of meaningful observations. The results of workload characterization are representative and generally consistent with cloud cluster for public IaaS service providers, which can help other researchers and engineers understand the performance and VM characteristics of the cloud in their production environments.
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Jianjun, Yi,Yifeng, Guan,Baiyang, Ji,Bin, Yu,Jinxiang, Dong Korean Society for Precision Engineering 2007 International Journal of Precision Engineering and Vol.8 No.4
Gears, crucial components in modern precision machinery for power transmission mechanisms, are required to have low contacting noise with high torque transmission, which makes the use of gear-tooth profile modifications and gear-tooth surface crowning extremely efficient and valuable. Due to the shortcomings of current techniques, such as manual rectification, mechanical modification, and numerically controlled rectification, we propose a novel electrochemical gear-tooth profile modification method based on an artificial neural network control technique. The fundamentals of electrochemical tooth-profile modifications based on real-time control and a mathematical model of the process are discussed in detail. Due to the complex and uncertain relationships among the machining parameters of electrochemical tooth-profile modification processes, we used an artificial neural network to determine the required processing electric current as the tooth-profile modification requirements were supplied. The system was implemented and a practical example was used to demonstrate that this technology is feasible and has potential applications in the production of precision machinery.
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Yi Jianjun,Guan Yifeng,Ji Baiyang,Yu Bin,Dong Jinxiang 한국정밀공학회 2007 International Journal of Precision Engineering and Vol.8 No.4
Gears, crucial components in modern precision machinery for power transmission mechanisms, are required to have low contacting noise with high torque transmission, which makes the use of gear-tooth profile modifications and gear-tooth surface crowning extremely efficient and valuable. Due to the shortcomings of current techniques, such as manual rectification, mechanical modification, and numerically controlled rectification, we propose a novel electrochemical gear-tooth profile modification method based on an artificial neural network control technique. The fundamentals of electrochemical tooth-profile modifications based on real-time control and a mathematical model of the process are discussed in detail. Due to the complex and uncertain relationships among the machining parameters of electrochemical tooth-profile modification processes. we used an artificial neural network to determine the required processing electric current as the tooth-profile modification requirements were supplied. The system was implemented and a practical example was used to demonstrate that this technology is feasible and has potential applications in the production of precision machinery.
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Facile One-Pot Synthesis and Optical Properties of Quinary Wurtzite Cu3ZnInSnS6 Nanocrystals
Dehui Li,Chen Zhang,Yajie Zhang,Weichen Qi,Jinxiang Dong,Jing Yang,Zhidong Qiu 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2016 NANO Vol.11 No.11
Nearly monodisperse bullet-like Cu3ZnInSnS6 (CZITS) nanocrystals with wurtzite structure were successfully synthesized through optimized noninjection method. The structure, composition, morphology and optical properties of CZITS nanocrystals were characterized by X-ray diffraction, energy dispersive spectrometry, transmission electron microscopy and UV-Vis–NIR absorption. Their suitable bandgap and photoresponsive behavior indicate a high potential application in the field of solar cells. The growth mechanism of the as-synthesized CZITS nanocrystals was preliminarily discussed. It was found that the formation of CZITS could be separated into two steps: nucleation of Cu7S4 and growth of the CZITS main body.
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