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

In an effort to advance the development of hydrometallurgical reprocessing of used nuclear fuel across the globe, this work sets out to explore and identify an optimised, cost effective pathway to synthesise the ligand DEHiBA (N,N-di-(2-ethylhexyl)isobutyramide). Currently, very few chemical suppliers stock and distribute this specialist ligand, designed for selective uranium chelation and extraction from nuclear fuel. The current high cost of DEHiBA therefore restricts access to essential large-scale testing of this promising ligand designed to advance nuclear reprocessing. This work utilises an automated flow reactor platform for the efficient optimisation of four synthetic routes to DEHiBA. These optimisations focus on optimising cost, reagent efficiency, yield, and productivity target functions by exploiting the power of machine-learning algorithms for rapid process development. Ultimately, we have identified an efficient and cost-effective solvent-free route to DEHiBA from isobutyric anhydride and di-2-ethylhexylamine for 99%, at a purity of 76%, and a process mass intensity of 1.29 g g−1, whilst alternative conditions demonstrated productivities >75 kg L−1 h−1, all whilst maintaining a high level of process control with outlet temperatures not exceeding 35 °C.