Solution scattering at the Life Science X‐ray Scattering (LiX) beamline. (31st March 2020)

• Record Type: Journal Article
• Authors: Yang, Lin; Antonelli, Stephen; Chodankar, Shirish; Byrnes, James; Lazo, Edwin; Qian, Kun
• Is Part Of: Journal of synchrotron radiation. Volume 27:Part 3(2020)
• Page Start: 804
• Page End: 812
• Publication Date: 2020-03-31
• Subjects: SAXS -- WAXS -- solution scattering -- SEC‐SAXS -- automation; Synchrotron radiation -- Periodicals; Free electron lasers -- Periodicals; 539.73505
• DOI: 10.1107/S1600577520002362
• Languages: English
• ISSNs: 0909-0495

Abstract

Abstract : Instrumentation and data processing for solution scattering at the LiX beamline are presented. Abstract : This work reports the instrumentation and software implementation at the Life Science X‐ray Scattering (LiX) beamline at NSLS‐II in support of biomolecular solution scattering. For automated static measurements, samples are stored in PCR tubes and grouped in 18‐position sample holders. Unattended operations are enabled using a six‐axis robot that exchanges sample holders between a storage box and a sample handler, transporting samples from the PCR tubes to the X‐ray beam for scattering measurements. The storage box has a capacity of 20 sample holders. At full capacity, the measurements on all samples last for ∼9 h. For in‐line size‐exclusion chromatography, the beamline‐control software coordinates with a commercial high‐performance liquid chromatography (HPLC) system to measure multiple samples in batch mode. The beamline can switch between static and HPLC measurements instantaneously. In all measurements, the scattering data span a wide q ‐range of typically 0.006–3.2 Å −1 . Functionalities in the Python package py4xs have been developed to support automated data processing, including azimuthal averaging, merging data from multiple detectors, buffer scattering subtraction, data storage in HDF5 format and exporting the final data in a three‐column text format that is acceptable by most data analysis tools. These functionalities have been integrated into graphical user interfaces that run in Jupyter notebooks, with hooks for external data analysis software.