A robotic and high-throughput X-ray micro-computed tomography workflow

Area of Science:

• Materials Science
• Geoscience
• Data Science

Background:

• Growing demand for artificial intelligence (AI)-ready scientific datasets necessitates high-throughput experimental workflows.
• Synchrotron micro-computed tomography (micro-CT) is crucial for analyzing complex samples like soil cores.
• Current methods often lack the automation required for large-scale data generation.

Purpose of the Study:

• To demonstrate a fully automated data collection workflow for synchrotron micro-CT experiments on soil cores.
• To enable unsupervised operation of experimental beamlines, accelerating data acquisition.
• To lay the groundwork for future adaptive and intelligent synchrotron experiments.

Main Methods:

• Implementation of a fully automated workflow integrating sample exchange, multi-position movement, and data acquisition.
• Development of a self-contained robotic arm system for rapid and reliable sample exchange.
• Utilizing upgraded beamline sample stage stacks and an efficient macroscope-based imaging system for diverse sample types.

Main Results:

• Successful demonstration of unsupervised, automated data collection for synchrotron micro-CT on soil cores at beamline 7-BM, Advanced Photon Source (APS).
• The robotic system efficiently handles various sample types and facilitates deployment across multiple beamlines.
• The upgraded imaging system enables characterization of large and highly attenuating samples.

Conclusions:

• The developed automated workflow significantly enhances the efficiency and throughput of synchrotron micro-CT data collection.
• This automation is a critical step towards realizing AI-ready scientific datasets for large-scale research.
• The established foundation supports the future development of closed-loop, adaptive, and intelligent experimental systems.