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Transforming Critical Materials Separation using Precision Control

Project Details

Principal Investigator
Funding Source
Office of Basic Energy Sciences (BES)
Start Date
End Date

The rare earth elements (REE) are assessed as critical materials by the U.S. DOE because of their essential roles in renewable energy, high supply risk, and difficult substitutability. Research to diversify the supply of the REE must grapple with the complexity of source materials and recycle streams in which the REE are often very dilute and thus requires ligands with strong binding. Inspired by the extraordinarily selective separation systems found in nature in the face of the formidable challenges of separating the rare earths, we are led to ask the central question: How can we design new REE host molecules for separation that incorporate both extreme recognition and a mechanism to spring the captured ions from the tight grip of the host? We propose that ligands with preorganized structure have higher binding affinity and that weak interaction in the secondary binding sphere can be used to control selectivity. We also propose that redox process can be used to control the release of tightly bound metals. Using these guiding principles, the overarching goal of this proposal is to develop a fundamental understanding of the synergy between "strong" interactions at the ligand-metal binding site and "weak" interactions in the surrounding coordination sphere for the selective separations and stimuli-responsive release of lanthanides. In order to achieve this goal, we propose to organize the research around the following two specific aims. In Aim 1, we will focus on understanding the binding principles of lanthanides in the 1st-coordination sphere to design stimuli-responsive, multidentate, preorganized ligands for REE separations. In Aim 2, we will focus on understanding how the solvation environment can be used to control lanthanide selectivity and release. In pursuit of these aims, we propose a multidisciplinary research effort involving computational modeling and machine learning to design and synthesize new redox-active, preorganized receptors for separation of REE.