A minimal predictive model for better formulations of solvent phases with low viscosity

Liquid-liquid extraction is the central technology in metal recycling. An important application is the recovery of major actinides uranium and plutonium. The viscosity increase of the organic phase when liquid-liquid extraction processes are intensified causes difficulties for hydrometallurgical processes on industrial scale. In this work, we have analyzed this problem for the example of N,N-dialkyl-amides in the presence of uranyl nitrate experimentally. Furthermore, we present a minimal model at nanoscale that allows rationalizing the experimental phenomena by connecting the molecular, mesoscopic and macroscopic scale and that allows predicting qualitative trends in viscosity. This model opens broad possibilities in optimizing constraints and is a further step towards knowledge-based formulation of extracting microemulsions formed by microstructures with low connectivity, even at high load with heavy metals