Collaborative Research: DMREF: High-Throughput Screening of Electrolytes for the Next Generation of Rechargeable Batteries

合作研究：DMREF：下一代可充电电池电解质的高通量筛选

基本信息

批准号：
2323117
负责人：
Tao Li
金额：
$ 76万
依托单位：
Northern Illinois University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-10-01 至 2027-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323117&HistoricalAwards=false
关键词：
Collaborative Research DMREF Throughput Screening

项目摘要

Rechargeable batteries have become one of the most popular energy storage devices for electric vehicles, electronics, and grid energy storage. Developing novel electrolytes for the next generation of rechargeable batteries require more understanding of transport properties, microstructures, and the impact of microstructure on transport property. In this project, the investigators will systematically vary the composition and concentration of the electrolytes to determine the optimum solution for advanced rechargeable batteries. The success of the proposed research will provide high throughput experimentation/characterization and machine learning platforms. Moreover, the integrated research and educational programs will broadly impact the university, secondary education, and the general public. The research results will be into the investigators' courses and be used to train undergraduate and graduate students in the interdisciplinary research areas. New educational outreach initiatives include having an Electrolyte for Energy Storage workshop for local high school students and teachers each fall to enhance the broader impact of this NSF project.The fundamental interactions in the electrolyte directly determine the solvation structures, kinetics, and battery performance of the bulk electrolytes. Understanding the complex interactions and their correlation with electrolyte performance is significant for exploring their working mechanisms and realizing the rational design of battery electrolytes. The novelty of this proposal lies in the use of advanced high-throughput characterization with the help of MD simulation and machine learning to determine the link between molecular interactions and the macroscopic properties of battery electrolytes. The proposal aims to (1) gain a good understanding of the solvation structure through multimodal characterization methods Raman and X-ray for high throughput experimentation/characterization. High-throughput X-ray scattering techniques (USAXS/SAXS/WAXS for APS) will be used to characterize solution organization as a function of ion composition, ion concentration, and temperature; (2) to correlate the structure-property relationship by studying transport properties through high-throughput computational screening studies. A computational platform will be developed to screen structure/property relationships by AIMD and MD; (3) A machine learning-based data analysis platform will be created to predict and identify battery properties by analyzing high-throughput structural and simulation data.This project is supported by the Division of Materials Research and the Chemical, Biological, Environmental Engineering and Transport Systems.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.

可充电电池已成为电动汽车，电子设备和电网储能的最流行的能源存储设备之一。为下一代可充电电池开发新型电解质需要对传输特性，微观结构以及微结构对运输特性的影响有更多的了解。在该项目中，研究人员将系统地改变电解质的组成和浓度，以确定高级可充电电池的最佳解决方案。拟议研究的成功将提供高吞吐量实验/表征和机器学习平台。此外，综合研究和教育计划将广泛影响大学，中学教育和公众。研究结果将是研究人员的课程，并用于跨学科研究领域的本科生和研究生培训。新的教育外展计划包括为当地高中生和老师提供用于储能研讨会的电解质，以增强该NSF项目的更广泛影响。电解质中的基本相互作用直接确定溶剂化结构，动力学和体积电解质的电池性能。了解复杂的相互作用及其与电解质性能的相关性对于探索其工作机制并意识到电池电解质的合理设计至关重要。该提案的新颖性在于在MD模拟和机器学习的帮助下使用先进的高通量表征，以确定分子相互作用与电池电解质的宏观特性之间的联系。该建议旨在通过多模式特征方法拉曼和X射线来良好地了解溶剂化结构，以进行高吞吐量实验/表征。高通量X射线散射技术（APS的USAXS/SAXS/WAXS）将用于将溶液组织表征为离子组成，离子浓度和温度的函数；（2）通过通过高通量计算筛选研究研究运输特性来关联结构特性关系。 AIMD和MD将开发一个计算平台以筛选结构/属性关系；（3）将创建一个基于机器的数据分析平台，以通过分析高通量结构和仿真数据来预测和识别电池性能。本项目得到材料研究和化学，生物学，环境工程和运输系统的部门的支持。该奖项反映了NSF的法定任务，并通过使用基础的智力效果和广泛的范围进行评估，并以评估值得评估。