A self-optimised approach to synthesising DEHiBA for advanced nuclear reprocessing, exploiting the power of machine-learning

Abstract

To aid the advancement of hydrometallurgical reprocessing of used nuclear fuel, this work has explored and optimised the synthesis of DEHiBA in continuous flow, to establish a scalable, cost-effective manufacture route.