Abstract
Traditionally, energy storage system characterization involves labor-intensive processes, from sample preparation to data acquisition and analysis. However, the integration of automation technologies streamlines this workflow, enhancing efficiency and throughput while minimizing human intervention. The 11-BM beamline, equipped with high-resolution X-ray diffraction capabilities, serves as a versatile platform for investigating the structural and chemical properties of materials crucial for energy storage applications.The key components of the automated workflow deployed at 11-BM, including robotic sample handling systems, real-time data acquisition algorithms, and advanced machine learning techniques for rapid data analysis. By interfacing with sophisticated automation system involving robotics like UR5e and KX2, researchers can remotely design experiments, execute protocols, and retrieve results with minimal manual intervention, thereby accelerating the pace of research and innovation in energy storage technology.
Published Version
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