The Evolution and Expansion of the Microjet Characterization Pipeline for X-Ray Laser Experiments from ASU to SLAC
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Gas Dynamic Virtual Nozzles (GDVNs) are critical for sample delivery in X-ray free-electron laser (XFEL) experiments, yet their comprehensive characterization has remained a challenge due to the complexity of data collection and analysis. To address this issue, we have developed a Python-based pipeline that automates GDVN characterization, originally designed at Arizona State University (ASU) and then expanded at SLAC National Accelerator Laboratory for use at the Linac Coherent Light Source (LCLS). The pipeline provides a fully automated workflow for injector characterization, integrating high-speed cameras, LED-enhanced imaging, and synchronized flow control. It enables real-time image processing to extract key jet properties, including speed, diameter, stability, and phase transitions between jetting and dripping modes. The system supports automated mapping of gas and liquid flow conditions, allowing rapid identification of stable jetting regimes with high precision. The SLAC adaptation enhances this framework by incorporating additional real-time data acquisition features, flexible scripting for flow scans, and expanded visualization capabilities, facilitating faster and reliable injector optimization. By reducing the time required for manual injector diagnostics and minimizing experimental failures, this pipeline improves efficiency in XFEL sample delivery. The next phase of development at LCLS will integrate multiple high-speed cameras to enable multi-angle jet imaging and characterization. Additionally, while currently optimized for microfluidic GDVN characterization, this pipeline has the potential for broader applications, including various liquid jets beyond GDVNs and new sample delivery techniques. Future expansions may also incorporate spectroscopic analysis, enabling real-time chemical and structural characterization of liquid jets under XFEL conditions. This comprehensive toolset not only provides a robust solution for GDVN characterization but also establishes a foundation for future injector developments at LCLS and other XFEL facilities around the world. By automating labor-intensive processes and expanding jet characterization capabilities, this pipeline represents a significant step toward optimizing sample delivery systems for cutting-edge ultrafast science and structural biology experiments.