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.

氧化还原流量电池（RFB）是大型，高容量的存储系统。 RFB通过将液体流过细胞来插入（即充电）或从系统中提取能量（即放电），然后将液体存储在大型储层中。这些类型的大规模电池可以使用可再生能源技术（例如太阳能和风能），这些技术可能并不总是会产生与需求的峰值能量输出。仍然需要大量的RFB技术部署能量存储容量，功率输出和材料成本。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来迅速加速发展下一代液体作为RFB储能材料的进展。参与该项目的学生将与另一个由NSF资助的数据科学项目进行交互，这将使他们能够参加集中在能源应用上的数据科学和机器学习方面的专业课程。该项目还将吸引各个级别的学生参与研究活动，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。该研究计划将使用高通量电化学，光谱和物理化学技术以及启用数据的发现和台式RFB性能评估，以发现具有改善能量存储容量的新电解质材料。具体而言，这项工作的重点是由有机氧化还原活性分子，氢键供体和有机盐组合的新型组合产生的氧化还原活性深共晶溶剂（RDE）。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）产生，同时增加最大可达到的细胞电位。高通量分析工具将用于在大分子设计空间上有效采样相关特性，并收敛于可行的RDES公式。然后，将通过应用高级数据科学算法来分析实验数据集，以确定最有效地与性质相关并优化电解质制剂的化学和制剂参数。最后，实验室规模的RFB测试将评估性能指标，以确定这些RDE电解质在大规模储能应用中的可行性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。

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