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Sökning: WFRF:(Fangohr Hans) > (2022)

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1.
  • Zhou Hagström, Nanna, 1993-, et al. (författare)
  • Megahertz-rate ultrafast X-ray scattering and holographic imaging at the European XFEL
  • 2022
  • Ingår i: Journal of Synchrotron Radiation. - : International Union of Crystallography (IUCr). - 0909-0495 .- 1600-5775. ; 29, s. 1454-1464
  • Tidskriftsartikel (refereegranskat)abstract
    • The advent of X-ray free-electron lasers (XFELs) has revolutionized fundamental science, from atomic to condensed matter physics, from chemistry to biology, giving researchers access to X-rays with unprecedented brightness, coherence and pulse duration. All XFEL facilities built until recently provided X-ray pulses at a relatively low repetition rate, with limited data statistics. Here, results from the first megahertz-repetition-rate X-ray scattering experiments at the Spectroscopy and Coherent Scattering (SCS) instrument of the European XFEL are presented. The experimental capabilities that the SCS instrument offers, resulting from the operation at megahertz repetition rates and the availability of the novel DSSC 2D imaging detector, are illustrated. Time-resolved magnetic X-ray scattering and holographic imaging experiments in solid state samples were chosen as representative, providing an ideal test-bed for operation at megahertz rates. Our results are relevant and applicable to any other non-destructive XFEL experiments in the soft X-ray range.
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2.
  • Zhuang, Yulong, et al. (författare)
  • Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging
  • 2022
  • Ingår i: IUCrJ. - : International Union of Crystallography (IUCr). - 2052-2525. ; 9, s. 204-214
  • Tidskriftsartikel (refereegranskat)abstract
    • One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand-maximizecompress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.
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