(186j) High-Throughput Electrochemical Screening of Deep Eutectic Solvents for Use in Redox Flow Batteries

Deep eutectic solvents (DESs) are a class of materials with varied applications including catalysis and 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 present a depression in the melting point at specific molar ratios of organic components that can result in a liquid solution at moderate temperatures. A breadth of candidates with varying concentrations can be used to form DESs, leading to a vast design space. High throughput experiments and data-driven design strategies are key to accelerate the optimization of materials based on their physicochemical and electrochemical properties as well as engineering criteria (e.g. cost, safety, toxicity) for candidate DESs. The implementation of high-throughput tools allows for a rapid evaluation and screening based on metrics such as the melting point, potential stability window and ionic conductivity. Several high-throughput protocols have been designed for identifying the design space of molecules under investigation, their formulation and material characterization. Using data science principles, the molecules composing our basis set were identified using scoring based on metrics such as cost, melting point, toxicity and molecular weight. The formulation of deep eutectic solvents was automated through the use of a pipetting robot, combinatorial techniques and well-plates. Next, the melting point of the proposed mixtures was detected using an IR camera and hot-plate set-up. To screen for their electrochemical properties, 96-well plates with screen printed electrodes in combination with measurement techniques such as Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) were implemented. Finally, to adapt high-throughput principles to the analysis of the data collection obtained through the aforementioned protocols, machine learning was leveraged for the data classification and data modeling. Furthermore, open-source Python-based packages have been developed and made available on GitHub. The combination of high-throughput experimentation and data analysis greatly accelerated the design and screening candidate DES systems. This overall workflow could be easily adapted to other design spaces and applications.