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

氧化还原液流电池是一种大规模、高容量的储能系统。rfb的工作原理是让液体流过电池插入（即充电）或从系统中提取能量（即放电），然后将液体储存在大型储层中。这些类型的大型电池能够使用可再生能源技术，如太阳能和风能，这些技术可能并不总是产生与需求一致的峰值能源输出。为了实现RFB技术的大规模部署，仍需要在储能容量、功率输出和材料成本方面取得重大进展。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另一个由美国国家科学基金会资助的数据科学项目对接，这将使他们能够学习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，并帮助培养未来的工程师和科学家来解决未来的能源问题。该研究项目将使用高通量电化学、光谱和物理化学技术，以及数据支持发现和台式RFB性能评估，以发现具有改进储能容量的新电解质材料。具体来说，这项工作的重点是由有机氧化还原活性分子、氢键供体和有机盐的新组合产生的氧化还原活性深共晶溶剂（RDES）。由于其有机性质，RDES电解质可以从丰富而廉价的原材料（例如染料）中生产，同时增加可达到的最大细胞电位。高通量分析工具将用于在大的分子设计空间内有效地取样相关性质，并收敛于可行的RDES配方。实验数据集将通过应用先进的数据科学算法进行分析，以确定与性能最有效相关的化学和配方参数，并优化电解质配方。最后，实验室规模的RFB测试将评估性能指标，以确定这些RDES电解质在大规模储能应用中的可行性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.