(88h) High-Throughput Screening of Binary and Ternary Organic Redox Active Materials-Based Deep Eutectic Solvents
AIChE Annual Meeting
Monday, November 14, 2022 - 9:45am to 10:00am
Deep eutectic solvents (DESs) are a class of materials with varied applications including catalysis, synthesis, extraction processes, drug solubilization, and battery electrolytes. Their appeal stems from their broad electrochemical stability window, high electrical conductivity, low vapor-pressure, and low-flammability. These solvents possess a depression in the melting point at specific molar ratios of organic components that can result in a liquid solution near ambient temperatures. A breadth of candidates with varying concentrations can be used to form DESs, leading to a vast design space. To tackle such a challenge, we have demonstrated the use of high throughput experiments and data-driven design strategies for automated synthesis, rapid evaluation and screening of both their physical and electrochemical properties, for a total of 50 new combinations of DESs and DES analogous samples.1 The implemented workflow uses low-budget systems (< $10k), commercially available materials and follows open-science principles. The current project aims to further expand the chemical space investigated using the high-throughput protocol for the formulation of binary and ternary deep eutectic solvents containing organic redox active materials (ORAM) for use as electrolytes in redox flow batteries (RFB). The use of bio-derived materials aims to be an alternative to expensive and resource-limited transition metals, such as the current industry leading RFB system based on Vanadium. The targeted organic redox compounds are selected from quinone derivatives (e.g., benzo-, anthra-quinones), fluorenones, and TEMPO. Once again, the physical and electrochemical properties of all synthesized samples have been screened following the high-throughput protocol demonstrated previously. Finally, to adapt high-throughput principles to the analysis of the data collection, machine learning was leveraged for gaining better understanding on the relationship between properties and structure of the starting materials and those of the final DES and DES analogous samples produced. The combination of high-throughput experimentation and data analysis greatly accelerated the design and screening of candidate binary and ternary organic redox active materials-based DES. This overall workflow could be easily adapted to other design spaces and applications.