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Ruthenium is recovered from used nuclear fuel in an oxidizing environment by depositing the volatile RuO4 species onto a polymeric substrate.
Real-time tracking and monitoring of radioactive/nuclear materials during transportation is a critical need to ensure safety and security. Current technologies rely on simple tagging, using sensors attached to transport containers, but they have limitations.
This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.