摘要
Multimodal hard X-ray scanning probe microscopy has been extensively used to study functional materials providing multiple contrast mechanisms.For instance,combining ptychography with X-ray fluorescence(XRF)microscopy reveals structural and chemical properties simultaneously.While ptychography can achieve diffraction-limited spatial resolution,the resolution of XRF is limited by the X-ray probe size.Here,we develop a machine learning(ML)model to overcome this problem by decoupling the impact of the X-ray probe from the XRF signal.The enhanced spatial resolution was observed for both simulated and experimental XRF data,showing superior performance over the state-of-the-art scanning XRF method with different nano-sized X-ray probes.Enhanced spatial resolutions were also observed for the accompanying XRF tomography reconstructions.Using this probe profile deconvolution with the proposed ML solution to enhance the spatial resolution of XRF microscopy will be broadly applicable across both functional materials and biological imaging with XRF and other related application areas.
基金
This work uses the 3-ID Hard X-ray Nanoprobe(HXN)beamline of the National Synchrotron Light Source II(NSLS-II),which was supported by the U.S.Department of Energy(DOE).NSLS-II is an Office of Science user facility operated by Brookhaven National Laboratory under Contract No.DE-SC0012704.The work at UCL was supported by EPSRC
This work was partially carried out at the MERF facility at Argonne National Laboratory,which is supported within the core funding of the Applied Battery Research for Transportation Program.Argonne,a U.S.DOE,Office of Science laboratory,is operated under Contract No.DE-AC02-06CH11357.We acknowledge the support of the U.S.DOE,Office of Energy Efficiency and Renewable Energy,Vehicle Technologies Office,and in particular the support of Peter Faguy and Dave Howell.
