3D super-resolution coherent diffractive imaging of core-shell nanoparticles


[A. Pryor, Jr., A. Rana, R. Xu, J. A. Rodriguez, Y. Yang, M. Gallagher-Jones, H. Jiang, K. Kanhaiya, M. Nathanson, J. Park, S. Kim, S. Kim, D. Nam, Y. Yue, J. Fan, Z. Sun, B. Zhang, D. F. Gardner, C. S. B. Dias, Y. Joti, T. Hatsui, T. Kameshima, Y. Inubushi, K. Tono, J. Y. Lee, M. Yabashi, C. Song, T. Ishikawa, H. C. Kapteyn, M. M. Murnane, H. Heinz and J. Miao, "Single-shot 3D coherent difractive imaging of core-shell nanoparticles with elemental specifcity", Sci. Rep. 8:8284 (2018).]


Posted on May 29th, 2018


We report 3D coherent diffractive imaging (CDI) of Au/Pd core-shell nanoparticles with 6.1nm spatial resolution with elemental specificity. We measured single-shot diffraction patterns of the nanoparticles using intense x-ray free electron laser pulses. By exploiting the curvature of the Ewald sphere and the symmetry of the nanoparticle, we reconstructed the 3D electron density of 34 core-shell structures from these diffraction patterns. To extract 3D structural information beyond the diffraction signal, we implemented a super-resolution technique by taking advantage of CDI’s quantitative reconstruction capabilities. We used high-resolution model fitting to determine the Au core size and the Pd shell thickness to be 65.0 ± 1.0nm and 4.0 ± 0.5nm, respectively. We also identified the 3D elemental distribution inside the nanoparticles with an accuracy of 3%. To further examine the model fitting procedure, we simulated noisy diffraction patterns from an Au/Pd core-shell model and a solid Au model and confirmed the validity of the method. We anticipate this super-resolution CDI method can be generally used for quantitative 3D imaging of symmetrical nanostructures with elemental specificity.


With the advent of X-ray free electron lasers (XFELs) that produce extremely intense and short X-ray pulses, CDI has opened the door for high-resolution imaging of both physical and biological specimens based on the diffraction-before-destruction. However, because an intense XFEL pulse destroys a specimen after one exposure, it would be desirable to find a way to obtain 3D structure information from a single X-ray pulse. One method to achieve 3D structure determination from a single sample orientation is the use of the curvature of Ewald sphere and additional constraints such as symmetry and sparsity. Here, we implemented a super-resolution CDI technique to extract 3D structural information of core-shell nanoparticles beyond the diffraction signal. We reconstructed the 3D electron density of individual Au/Pd core-shell nanoparticles from single-shot diffraction patterns with 6.1 nm resolution. By exploiting CDI’s quantitative reconstruction, we used high-resolution model fitting to determine the size of the Au core and the thickness of the Pd shell to be 65.0 ± 1.0 nm and 4.0 ± 0.5 nm, respectively. We quantified the 3D elemental distribution inside the nanoparticle with an accuracy better than 3%. Finally, by implementing a semi-automated data analysis and 3D reconstruction pipeline, we established a general method for high-throughput, quantitative characterization of symmetrical nanoparticles.



Figure 1. Semi-automated data analysis and 3D reconstruction pipeline. a, A large number of diffraction patterns were experimentally collected consisting of no, partial, single, and multiple hits by XFEL pulses. High-quality single-hit diffraction patterns were selected from these patterns. The different colors in the pattern are due to the difference of the read-out noise of the detector segments. b, After background subtraction and center localization, each diffraction pattern was binned by 9×9 pixels to enhance the signal-to-noise ratio and the orientation of the pattern was determined. c, By taking advantage of the curvature of the Ewald sphere and symmetry intrinsic to the nanoparticle, a single-shot diffraction pattern was used to produce a 3D Cartesian grid of the Fourier magnitudes by a gridding method. d, The 3D phase retrieval was performed by the OSS algorithm. Among 1,000 independent reconstructions, the top 10% with the smallest R-factors were averaged to obtain a final 3D reconstruction for each single-shot diffraction pattern.



The 34 diffraction patterns were processed and reconstructed by using a semi-automated 3D data analysis pipeline, shown in Fig. 1. From each diffraction pattern, the background was subtracted based on the most recently available background exposure. An additional flat background subtraction was required, the value of which was determined by first smoothing and thresholding each pattern to determine the background region. The final subtracted value was determined by the average nonnegative pixel intensity in the background region multiplied by a single scaling factor, whose value was optimized based upon the quality of the resulting reconstructions. The center of each diffraction pattern was determined based on the centro-symmetry of the diffraction intensity at the low spatial frequency. Since the diffraction patterns have larger oversampling ratios, each pattern was binned by 9×9 pixels to enhance the signal-to-noise ratio.


Download the raw diffraction patterns here.

Download the background patterns used for background subtraction here.

Download the processed diffraction patterns here.

Download the source code here.


This document was prepared by AJ Pryor, Yongsoo Yang and John Miao in the Department of Physics & Astronomy and California NanoSystems Institute, University of California, Los Angeles, California, 90095, USA. Email: miao@physics.ucla.edu.