Molecular Design and Analysis of Flow Battery Electrolytes based on Redox Deep Eutectic Solvents

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Redox flow batteries (RFBs) are large-scale, high capacity energy storage systems. RFBs work by flowing liquids through cells to insert (i.e. charging) or to extract energy (i.e. discharging) from the system and then storing the liquids in large reservoirs. These types of large-scale batteries enable the use of renewable energy technologies, such as solar and wind power, that may not always generate peak energy output coincident with demand. Significant improvements in energy storage capacity, power output and material costs are still needed to enable wide-scale deployment of RFB technology. This research project will utilize advanced electrochemical tools, robotics and computational methods to rapidly accelerate progress in developing the next generation of liquids as RFB energy storage materials. Students involved in the project will interface with another NSF-funded project on data science, which will allow them to take specialized courses in data science and machine learning that are focused on energy applications. This project also will engage students at all levels in research activities and help to train future engineers and scientists to tackle future problems in energy. This research program will use high-throughput electrochemical, spectroscopic and physicochemical techniques along with data-enabled discovery and bench-top RFB performance evaluation to discover new electrolyte materials with improved energy storage capacity. Specifically, the work focuses on redox-active deep eutectic solvents (RDES) produced from novel combinations of organic redox active molecules, hydrogen bond donors, and organic salts. Because of their organic nature, RDES electrolytes can be produced from abundant and inexpensive raw materials (e.g. dyes) while at the same time increasing the maximum attainable cell potentials. High-throughput analytical tools will be used to efficiently sample relevant properties over a large molecular design space and converge on viable RDES formulations. Experimental data sets will then be analyzed through the application of advanced data science algorithms to identify the chemical and formulation parameters that most effectively correlate to properties and to optimize electrolyte formulations. Lastly, lab-scale RFB tests will evaluate performance metrics to determine the viability of these RDES electrolytes in large-scale energy storage applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.