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Sökning: WFRF:(Küpper Jochen) > (2022)

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1.
  • Lee, Jason W.l., et al. (författare)
  • The Kinetic Energy of PAH Dication and Trication Dissociation Determined by Recoil-Frame Covariance Map Imaging
  • 2022
  • Ingår i: Physical Chemistry Chemical Physics. - 1463-9084. ; 24:38, s. 23096-23105
  • Tidskriftsartikel (refereegranskat)abstract
    • We investigated the dissociation of dications and trications of three polycyclic aromatic hydrocarbons (PAHs), fluorene, phenanthrene, and pyrene. PAHs are a family of molecules ubiquitous in space and involved in much of the chemistry of the interstellar medium. In our experiments, ions are formed by interaction with 30.3 nm extreme ultraviolet (XUV) photons, and their velocity map images are recorded using a PImMS2 multi-mass imaging sensor. Application of recoil-frame covariance analysis allows the total kinetic energy release (TKER) associated with multiple fragmentation channels to be determined to high precision, ranging 1.94-2.60 eV and 2.95-5.29 eV for the dications and trications, respectively. Experimental measurements are supported by Born-Oppenheimer molecular dynamics (BOMD) simulations.
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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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  • Resultat 1-2 av 2

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