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

基于氧化还原低共熔溶剂的液流电池电解质的分子设计与分析

基本信息

批准号：
1917340
负责人：
Lilo Pozzo
金额：
$ 48.59万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917340&HistoricalAwards=false
关键词：
Molecular Design Analysis Flow Battery

项目摘要

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.

氧化还原液流电池（RFB）是大规模、高容量的能量存储系统。 RFB通过使液体流过电池以插入（即充电）或从系统提取能量（即放电）并且然后将液体存储在大的储器中来工作。这些类型的大型电池使得可再生能源技术的使用成为可能，例如太阳能和风能，这些技术可能并不总是产生与需求一致的峰值能量输出。仍然需要在储能容量、功率输出和材料成本方面进行重大改进，以实现RFB技术的大规模部署。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，快速加速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NSF资助的数据科学项目进行对接，这将使他们能够参加专注于能源应用的数据科学和机器学习专业课程。该项目还将使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，以解决未来的能源问题。该研究计划将使用高通量电化学，光谱和物理化学技术，沿着数据支持的发现和台式RFB性能评估，以发现具有改进的储能容量的新电解质材料。具体而言，工作重点是氧化还原活性深共熔溶剂（RDES）的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。由于其有机性质，RDES电解质可以由丰富且廉价的原材料（例如染料）生产，同时提高可达到的最大电池电势。高通量分析工具将用于在大分子设计空间内有效地采样相关特性，并收敛于可行的RDES制剂。然后将通过应用先进的数据科学算法分析实验数据集，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NSF的法定使命，并被认为值得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来支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

PhasIR: An Instrumentation and Analysis Software for High-throughput Phase Transition Temperature Measurements

PhasIR：用于高通量相变温度测量的仪器和分析软件

DOI：
10.5334/joh.39
发表时间：
2021
期刊：
Journal of Open Hardware
影响因子：
0
作者：
Rodriguez, Jaime;Politi, Maria;Scheiwiller, Sage;Bonageri, Shrilakshmi;Adler, Stuart;Beck, David;Pozzo, Lilo D.
通讯作者：
Pozzo, Lilo D.

The laboratory of Babel: highlighting community needs for integrated materials data management

DOI：
10.1039/d3dd00022b
发表时间：
2023-06-12
期刊：
DIGITAL DISCOVERY
影响因子：
0
作者：
Pelkie，Brenden G.;Pozzo，Lilo D.
通讯作者：
Pozzo，Lilo D.

A high-throughput workflow for the synthesis of CdSe nanocrystals using a sonochemical materials acceleration platform

DOI：
10.1039/d3dd00033h
发表时间：
2023
期刊：
Digital Discovery
影响因子：
0
作者：
Mara Politi;Fábio Baum;K. Vaddi;Edwin Antonio;J. Vasquez;Brittany P. Bishop;Nadya Peek;V. Holmberg;L. Pozzo
通讯作者：
Mara Politi;Fábio Baum;K. Vaddi;Edwin Antonio;J. Vasquez;Brittany P. Bishop;Nadya Peek;V. Holmberg;L. Pozzo

HARDy: Handling Arbitrary Recognition of Data inPython

HARDy：在Python中处理数据的任意识别