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.