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

氧化还原液流电池(RFB)是一种大规模、大容量的能量存储系统。RFB的工作原理是让液体通过电池插入(即充电)或从系统中提取能量(即释放)，然后将液体存储在大型储液器中。这些类型的大型电池能够使用太阳能和风能等可再生能源技术，这些技术可能并不总是产生与需求相符的峰值能量输出。要实现RFB技术的广泛部署，仍需要在储能能力、电力输出和材料成本方面有重大改进。该研究项目将利用先进的电化学工具、机器人技术和计算方法，迅速加快开发下一代液体作为RFB储能材料的进展。参与该项目的学生将与NSF资助的另一个数据科学项目相联系，该项目将允许他们参加专注于能源应用的数据科学和机器学习方面的专业课程。该项目还将吸引各级学生参与研究活动，并帮助培训未来的工程师和科学家，以解决未来的能源问题。这项研究计划将使用高通量电化学、光谱和物理化学技术，以及数据启用的发现和台式RFB性能评估，以发现具有更高储能能力的新电解液材料。具体地说，工作重点是由有机氧化还原活性分子、氢键供体和有机盐的新型组合生产的具有氧化还原活性的深度共熔溶剂(RDES)。由于它们的有机性质，RDES电解液可以从丰富而廉价的原材料(如染料)中生产出来，同时还可以提高最大可达到的电池电位。高通量的分析工具将被用来在一个大的分子设计空间内有效地对相关性质进行采样，并汇聚到可行的RDES配方上。然后将通过应用先进的数据科学算法对实验数据集进行分析，以确定与性能最有效相关的化学和配方参数，并优化电解液配方。最后，实验室规模的RFB测试将评估性能指标，以确定这些RDES电解液在大规模储能应用中的可行性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.