Structured dark-field imaging for single nano-particles  

作　　者：陈健 高昆 王志立 云文兵 吴自玉 

机构地区：[1]National Synchrotron Radiation Laboratory, University of Science and Technology of China [2]5750 Imhoff Road, Concord,CA,94520, USA [3]Institute of High Energy Physics, Chinese Academy of Sciences

出　　处：《Chinese Physics B》2015年第8期497-500,共4页中国物理B（英文版）

基　　金：supported by the National Basic Research Program of China(Grant No.2012CB825800);the Science Fund for Creative Research Groups,the National Natural Science Foundation of China(Grant Nos.11475170,11205157,11305173,and 11321503);the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No.KJCX2-YW-N42)

摘　　要：In this work, we extensively describe and demonstrate the structured dark-field imaging(SDFI). SDFI is a newly proposed x-ray microscopy designed for revealing the fine features below Rayleigh resolution, in which different orders of scattered x-ray photons are collected by changing the numerical aperture of the condenser. Here, the samples of single particles are discussed to extend the scope of the SDFI technique reported in a previous work(Chen J, Gao K, Ge X, et al.2013 Opt. Lett. 38 2068). In addition, the details of the newly invented algorithm are explained, which is able to calculate the intensity of any pixel on the image plane rapidly and reliably.In this work, we extensively describe and demonstrate the structured dark-field imaging(SDFI). SDFI is a newly proposed x-ray microscopy designed for revealing the fine features below Rayleigh resolution, in which different orders of scattered x-ray photons are collected by changing the numerical aperture of the condenser. Here, the samples of single particles are discussed to extend the scope of the SDFI technique reported in a previous work(Chen J, Gao K, Ge X, et al.2013 Opt. Lett. 38 2068). In addition, the details of the newly invented algorithm are explained, which is able to calculate the intensity of any pixel on the image plane rapidly and reliably.

关 键 词：x-ray microscopy Rayleigh resolution fast algorithm 

分 类 号：TB383.1[一般工业技术—材料科学与工程]