01a3 Informatics-driven discovery and design of deep eutectic solvents (DESs)for the recycling of high-value metals from Li-ion batteries

The exponential increase in the production of lithium-ion batteries and their relatively short lifetimes (3-10 years) will inevitably lead to the generation of huge numbers of spent batteries. Current world’s recycling capacity can only process about 5% of the total end-of-life lithium-ion batteries, through costly, high-temperature, and energy intensive pyrometallurgical processes. The proposing team has developed a novel hydrometallurgical lithium-ion battery recycling technology based on the use of a new type of ‘green solvents’ called deep eutectic solvents. These types of solvents are made from a eutectic mixture of hydrogen-bond donor and hydrogen-bond acceptor components, which are easily synthesized from non-toxic, biodegradable, and inexpensive raw materials. A great advantage of deep eutectic solvents, over traditional inorganic acids, is that they can act both as an effective leaching and reducing agent, thus eliminating the need for supporting chemicals and processes to achieve metal extraction at low temperatures. The selection of the hydrogen-bond acceptor and hydrogen-bond donor components ultimately determines the physical and chemical properties of the deep eutectic solvent molecules. The large number of compounds that, when combined, can form deep eutectic solvent mixtures offers tremendous potential for fine tuning these molecules to desired task-specific applications. However, traditional trial-and-error approaches are inadequate to survey this combinatorial space in a meaningful way. In Phase I, the project will create a rapid-discovery, rational-design, and inverse-engineering framework for the accelerated development of novel deep eutectic solvent optimized for the recovery of high value metals from spent lithium-ion batteries. This computational approach is based on a data-driven pipeline that tightly couples physics-based molecular modeling, high-throughput in silico screening, machine learning, and materials informatics. In addition, we will synthesize the top deep eutectic solvent candidates, identified by this methodology, and will evaluate their metal extracting performance in our laboratory. Further development of the proposed technology into Phase II, Phase III and beyond, can help establish the foundation of a scalable, cost effective, and environmentally friendly lithium-ion battery recycling industry in the US that can drive the recovery of a significant amount of the total end-of-life batteries that currently end up being incinerated or disposed in landfills.