ArticleJournal of applied crystallography2022
Towards real-time analysis of liquid jet alignment in serial femtosecond crystallography.
Article in Journal of applied crystallography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Real-time analysis of liquid jet sample delivery stability for an X-ray free-electron laser using machine vision.Journal of applied crystallography · 2024Article
- Characterization of Biological Samples Using Ultra-Short and Ultra-Bright XFEL Pulses.Advances in experimental medicine and biology · 2024Article
- A phenomenological model of the X-ray pulse statistics of a high-repetition-rate X-ray free-electron laser.IUCrJ · 2023Article
- Form factor determination of biological molecules with X-ray free electron laser small-angle scattering (XFEL-SAS).Communications biology · 2023Article
- Microfluidic rotating-target device capable of three-degrees-of-freedom motion for efficient in situ serial synchrotron crystallography.Journal of synchrotron radiation · 2023Article
- An experimental study of liquid micro-jets produced with a gas dynamic virtual nozzle under the influence of an electric field.Frontiers in molecular biosciences · 2023Article
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Authors and funding
13 authors.
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Abstract
Liquid sample delivery systems are used extensively for serial femtosecond crystallography at X-ray free-electron lasers (XFELs). However, misalignment of the liquid jet and the XFEL beam leads to the X-rays either partially or completely missing the sample, resulting in sample wastage and a loss of experiment time. Implemented here is an algorithm to analyse optical images using machine vision to determine whether there is overlap of the X-ray beam and liquid jet. The long-term goal is to use the output from this algorithm to implement an automated feedback mechanism to maintain constant alignment of the X-ray beam and liquid jet. The key elements of this jet alignment algorithm are discussed and its performance is characterized by comparing the results with a manual analysis of the optical image data. The success rate of the algorithm for correctly identifying hits is quantified via a similarity metric, the Dice coefficient. In total four different nozzle designs were used in this study, yielding an overall Dice coefficient of 0.98.
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