An experiment facility has been set up for the study of metal cluster compounds in our laboratory, which consists of a nano-electrospray ionization source, an ion transmission and focus system, and a reflectron time-o...An experiment facility has been set up for the study of metal cluster compounds in our laboratory, which consists of a nano-electrospray ionization source, an ion transmission and focus system, and a reflectron time-of-fight mass spectrometer. Taking advantage of the nano-electrospray ionization source, polyvalent ions are usually produced in the ＂ionization＂ process and the obtained mass resolution of the equipment is over 8000. The molecular ion peaks of metal cluster compounds [Au20（PPhpy2）10Cl2]（SbF6）4, where PPhpy2=bis（2- pyridyl）phenylphosphine, and [AuaAg2（C）L6]（BF4）4, where L=2-（diphenylphosphino）-5- methylpyridine, are distinguished in the respective mass spectrum, accompanied by some fragment ion peaks. In addition, the mass-to-charge ratios of the parent ions are determi- nated. Preliminary results suggest that the device is a powerful tool for the study of metal cluster compounds. It turns out that the information obtained by the instrumentation serves as an essential supplement to single crystal X-ray diffraction for structure characterization of metal cluster compounds.展开更多
Subject of the halo-chaos control in beam transport networks （channels） has become a key concerned issue for many important applications of high-current proton beam since 1990＇. In this paper, the magnetic field ad...Subject of the halo-chaos control in beam transport networks （channels） has become a key concerned issue for many important applications of high-current proton beam since 1990＇. In this paper, the magnetic field adaptive control based on the neural network with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.展开更多
文摘An experiment facility has been set up for the study of metal cluster compounds in our laboratory, which consists of a nano-electrospray ionization source, an ion transmission and focus system, and a reflectron time-of-fight mass spectrometer. Taking advantage of the nano-electrospray ionization source, polyvalent ions are usually produced in the ＂ionization＂ process and the obtained mass resolution of the equipment is over 8000. The molecular ion peaks of metal cluster compounds [Au20（PPhpy2）10Cl2]（SbF6）4, where PPhpy2=bis（2- pyridyl）phenylphosphine, and [AuaAg2（C）L6]（BF4）4, where L=2-（diphenylphosphino）-5- methylpyridine, are distinguished in the respective mass spectrum, accompanied by some fragment ion peaks. In addition, the mass-to-charge ratios of the parent ions are determi- nated. Preliminary results suggest that the device is a powerful tool for the study of metal cluster compounds. It turns out that the information obtained by the instrumentation serves as an essential supplement to single crystal X-ray diffraction for structure characterization of metal cluster compounds.
基金The project supported by the Key Projects of National Natural Science Foundation of China under Grant No. 70431002 and National Natural Science Foundation of China under Grants Nos. 70371068 and 10247005
文摘Subject of the halo-chaos control in beam transport networks （channels） has become a key concerned issue for many important applications of high-current proton beam since 1990＇. In this paper, the magnetic field adaptive control based on the neural network with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.
