Analysis of Fibrous Assembly Orientations from XFEL Diffraction Data

Wojtas, D. H.; Seuring, C.; Ayyer, K.; Arnal, R. D.; Meents, A.; Mossou, E.; ̃Peña, G.; Paulraj, L. X.; Barthelmess, M.; Forsyth, V. T.; Barty, A.; Chapman, H. N.; Millane, R. P.

doi:10.1109/IVCNZ.2018.8634714

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Analysis of Fibrous Assembly Orientations from XFEL Diffraction Data

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Paulraj, L. X.
Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY);
International Max Planck Research School for Ultrafast Imaging & Structural Dynamics (IMPRS-UFAST), Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society;

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https://dx.doi.org/10.1109/IVCNZ.2018.8634714
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Citation

Wojtas, D. H., Seuring, C., Ayyer, K., Arnal, R. D., Meents, A., Mossou, E., et al. (2018). Analysis of Fibrous Assembly Orientations from XFEL Diffraction Data. In 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). New York, NY 10017 USA: IEEE. doi:10.1109/IVCNZ.2018.8634714.

Cite as: https://hdl.handle.net/21.11116/0000-0003-446A-A

Abstract

The application of a new generation of x-ray sources called X-ray Free Electron Lasers (XFELs) to diffractive imaging has allowed structural studies of specimens not previously accessible. Specimens of reduced crystallinity are of particular interest, including fibrous nano-crystals and single fibrous molecules. Diffractive imaging experiments using XFELs generate large datasets of diffraction frames from specimens with random, unknown orientations. The orientation of each diffraction frame needs to be determined from features in the pattern in order to register and merge the dataset for subsequent structural analysis. Certain sample delivery techniques simplify this process by limiting the range of orientations a specimen may take. In this paper we consider two sample delivery techniques: a liquid jet and a fixed target on a silicon wafer. Orientations determined from diffraction patterns from each delivery method are classified in order to investigate the type of orientation present. This information also helps to characterize the quality of sample preparations and provides feedback valuable for designing future experiments.