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Application of automated and robotically deployed in situ X‐ray fluorescence analysis for nuclear waste management

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Laboratory and synchrotron X‐ray fluorescence (XRF) analysis has both served as mainstay rapid and quantitative elemental analysis techniques for decades, attaining parts per million sensitivities for the majority of elements.… Click to show full abstract

Laboratory and synchrotron X‐ray fluorescence (XRF) analysis has both served as mainstay rapid and quantitative elemental analysis techniques for decades, attaining parts per million sensitivities for the majority of elements. Formerly, XRF was the reserve of large X‐ray generating systems and national facilities. More recently, developments in miniaturized X‐ray generators and detectors have allowed for this nondestructive technique to be utilized for portable and in situ elemental characterization of materials, away from the confines of the laboratory. When combined with a robotic manipulator, these usually handheld systems present a powerful method for autonomous assessments of material composition for a wide range of nuclear characterization and decommissioning scenarios. In this study, we present a proof‐of‐concept XRF system integrated with a robotic manipulator to autonomously identify a suite of nuclear relevant materials. Such remotely deployable noncontact tools are crucial for use within hazardous environments where it may not be possible, for physical and safety reasons, for a human operator to manually undertake characterization tasks. It is envisaged that this robotically deployed XRF system will comprise part of the wider autonomous characterization “toolkit”; capable of extensive large‐area mapping alongside targeted compositional “point analysis.” The system was demonstrated to rapidly and repeatably derive accurate and precise compositional information of different test materials, autonomously on both flat and complex, object‐rich surfaces.

Keywords: situ; ray fluorescence; ray; robotically deployed; analysis

Journal Title: Journal of Field Robotics
Year Published: 2022

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