Separation and purification of elements from alkaline and carbonate nuclear waste solutions

Machine-Learning-Enabled Ligand Screening for Cs/Sr Crystallizing Separation

“…The further development of nuclear energy is a viable means by which growing energy demands may be met in accordance with environmental aims, such as those outlined in the United Nation’s seventh sustainable development goals (SDG-7) and concepts such as “carbon peaking” and “carbon neutrality”. , However, with the expansion of nuclear energy, the stock of spent nuclear fuel (SNF) continues to increase globally. Accordingly, improving the efficiency of SNF reprocessing is crucial for realizing closed fuel cycles in the nuclear energy industry, necessitating more efficient treatment and management of high-level liquid waste (HLLW). , Various methods, including solvent extraction, chemical precipitation, and ion-exchange, have been developed to efficiently separate and capture nuclides from SNF in recent decades. − Among the separation strategies available, crystallizing separation has some clear advantages in terms of atom economy and decreasing the production of sludge and secondary organic waste, and it is generally compatible with current infrastructures . To effectively realize crystallizing separation, the design and synthesis of suitable ligands with high selectivities and recovery rates for targeted nuclides (Cs or Sr) are of great significance.…”