Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning  

作　　者：Zeyuan Li Thomas Flynn Tongchao Liu Sizhan Liu Wah-Keat Lee Ming Tang Mingyuan Ge 

机构地区：[1]National Synchrotron Light Source II(NSLS-II),Brookhaven National Laboratory,Upton,NY11973,USA [2]Department of Materials Science and NanoEngineering,Rice University,Houston,TX 77005,USA [3]Computational Science Initiative,Brookhaven National Laboratory,Upton,NY 11973,USA [4]Chemical Sciences and Engineering Division,Argonne National Laboratory,Lemont,IL 60439,USA [5]Interdisciplinary Science Department,Brookhaven National Laboratory,Upton,NY 11973,USA

出　　处：《npj Computational Materials》2024年第1期1941-1949,共9页计算材料学(英文)

基　　金：supported by the LDRD project 24255 received from Brookhaven National Laboratory.Z.L;M.T.are supported by the Department of Energy,Basic Energy Sciences under project DE-SC0019111。

摘　　要：Improving the spatial and spectral resolution of 2D X-ray near-edge absorption structure(XANES)has been a decade-long pursuit to probe local chemical reactions at the nanoscale.However,the poor signal-to-noise ratio in the measured images poses significant challenges in quantitative analysis,especially when the element of interest is at a low concentration.In this work,we developed a postimaging processing method using deep neural network to reliably improve the signal-to-noise ratio in the XANES images.

关 键 词：ABSORPTION XANES HIGHLY 

分 类 号：O62[理学—有机化学]