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:
  • 金额:
    $ 76万
  • 依托单位:
  • 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

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、电子产品和电网储能最受欢迎的储能设备之一。为下一代可充电电池开发新型电解质需要更多地了解传输特性,微观结构以及微观结构对传输特性的影响。在这个项目中,研究人员将系统地改变电解质的成分和浓度,以确定先进的可充电电池的最佳解决方案。拟议研究的成功将提供高通量实验/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。研究成果将纳入研究人员的课程,并用于培养跨学科研究领域的本科生和研究生。新的教育推广计划包括每年秋季为当地高中学生和教师举办电解质储能研讨会,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决定了本体电解质的溶剂化结构,动力学和电池性能。了解这些复杂的相互作用及其与电解液性能的相关性,对于探索其工作机理,实现电池电解液的合理设计具有重要意义。该提案的新奇在于使用先进的高通量表征,并借助MD模拟和机器学习来确定分子相互作用与电池电解质宏观性质之间的联系。该提案旨在(1)通过多峰表征方法拉曼和X射线获得对溶剂化结构的良好理解,以进行高通量实验/表征。高通量X射线散射技术(APS的USAXS/SAXS/WAXS)将用于表征溶液组织作为离子组成,离子浓度和温度的函数;(2)通过高通量计算筛选研究研究传输特性来关联结构-性能关系。AIMD和MD将开发一个计算平台来筛选结构/性能关系;(3)将创建基于机器学习的数据分析平台,通过分析高通量的结构和模拟数据来预测和识别电池性能,本项目由材料研究部和化学,生物,环境工程和运输系统。该奖项反映了NSF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。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Unveiling the Liquid Electrolyte Solvation Structure by Small Angle X-ray Scattering
  • DOI:
    10.1021/acs.chemmater.3c01648
  • 发表时间:
    2023-12
  • 期刊:
  • 影响因子:
    8.6
  • 作者:
    Xinyi Liu;Lingzhe Fang;Xingyi Lyu;R. Winans;Tao Li
  • 通讯作者:
    Xinyi Liu;Lingzhe Fang;Xingyi Lyu;R. Winans;Tao Li
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Tao Li其他文献

Evaluating liver function and the impact of immune checkpoint inhibitors in the prognosis of hepatocellular carcinoma patients: A systemic review and meta-analysis
评估肝功能和免疫检查点抑制剂对肝细胞癌患者预后的影响:系统评价和荟萃分析
  • DOI:
    10.1016/j.intimp.2022.109519
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    5.6
  • 作者:
    Bao-Wen Tian;Lun-Jie Yan;Zi-Niu Ding;Hui Liu;Cheng-Long Han;Guang-Xiao Meng;Jun-Shuai Xue;Zhao-Ru Dong;Yu-Chuan Yan;Jian-Guo Hong;Zhi-Qiang Chen;Dong-Xu Wang;Tao Li
  • 通讯作者:
    Tao Li
Phosphorylation of GluN2B subunits of N-methyl-d-aspartate receptors in the frontal association cortex involved in morphine-induced conditioned place preference in mice
额叶联合皮层 N-甲基-d-天冬氨酸受体 GluN2B 亚基的磷酸化参与吗啡诱导的小鼠条件性位置偏好
  • DOI:
    10.1016/j.neulet.2020.135470
  • 发表时间:
    2020-11
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Gang Chen;Wei Han;Axiang Li;Jing Wang;Jing Xiao;Xin Huang;Khosa Asif Nazir;Qing Shang;Hongyan Qian;Chuchu Qiao;Xinshe Liu;Tao Li
  • 通讯作者:
    Tao Li
A novel negative selection algorithm based on subspace clustering
一种基于子空间聚类的负选择算法
Normal Dispersion Fiber-based Nonlinear Pulse Compressor for Generating 2-μm Watt-scale, ~100-MHz, Few-cycle Laser Pulse
基于正色散光纤的非线性脉冲压缩器,用于生成 2μm 瓦特级、~100MHz、少周期激光脉冲
Author-topic evolution analysis using three-way non-negative Paratucker
使用三向非负 Paratucker 进行作者主题演化分析
  • DOI:
    10.1145/1390334.1390521
  • 发表时间:
    2008
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Wei Peng;Tao Li
  • 通讯作者:
    Tao Li

Tao Li的其他文献

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{{ truncateString('Tao Li', 18)}}的其他基金

CRII: SaTC: Securing Smart Devices with AI-Powered mmWave Radar in New-Generation Wireless Networks
CRII:SaTC:在新一代无线网络中使用人工智能驱动的毫米波雷达保护智能设备
  • 批准号:
    2422863
  • 财政年份:
    2024
  • 资助金额:
    $ 76万
  • 项目类别:
    Standard Grant
CRII: SaTC: Securing Smart Devices with AI-Powered mmWave Radar in New-Generation Wireless Networks
CRII:SaTC:在新一代无线网络中使用人工智能驱动的毫米波雷达保护智能设备
  • 批准号:
    2245760
  • 财政年份:
    2023
  • 资助金额:
    $ 76万
  • 项目类别:
    Standard Grant
Collaborative Research: FuSe: Spin Gapless Semiconductors and Effective Spin Injection Design for Spin-Orbit Logic
合作研究:FuSe:自旋无间隙半导体和自旋轨道逻辑的有效自旋注入设计
  • 批准号:
    2328828
  • 财政年份:
    2023
  • 资助金额:
    $ 76万
  • 项目类别:
    Standard Grant
Collaborative Research: Rational design of Ni/Ga intermetallic compounds for efficient light alkanes conversion through ammonia reforming
合作研究:合理设计Ni/Ga金属间化合物,通过氨重整实现轻质烷烃的高效转化
  • 批准号:
    2210868
  • 财政年份:
    2022
  • 资助金额:
    $ 76万
  • 项目类别:
    Standard Grant
Collaborative Research: Understanding the Reversible Formation of Sodium Hydrosulfide in Hybrid Electrolytes for High-Energy Density Storage
合作研究:了解用于高能量密度存储的混合电解质中硫氢化钠的可逆形成
  • 批准号:
    2208972
  • 财政年份:
    2022
  • 资助金额:
    $ 76万
  • 项目类别:
    Standard Grant
Collaborative Research: Characterization of Transport Properties and Microstructures of Battery Electrolytes via In Situ Spectroscopy
合作研究:通过原位光谱表征电池电解质的传输特性和微观结构
  • 批准号:
    2120559
  • 财政年份:
    2021
  • 资助金额:
    $ 76万
  • 项目类别:
    Standard Grant
Collaborative Research: Design of a Novel Photo-Thermo-Catalyst for Enhanced Activity and Stability of Dry Reforming of Methane
合作研究:设计新型光热催化剂以增强甲烷干重整的活性和稳定性
  • 批准号:
    1924574
  • 财政年份:
    2019
  • 资助金额:
    $ 76万
  • 项目类别:
    Standard Grant
SHF: Medium: Collaborative Research: Enhancing Mobile VR/AR User Experience: An Integrated Architecture-System Approach
SHF:媒介:协作研究:增强移动 VR/AR 用户体验:集成架构系统方法
  • 批准号:
    1900713
  • 财政年份:
    2019
  • 资助金额:
    $ 76万
  • 项目类别:
    Continuing Grant
Heegaard Splitting and Topology of 3-Manifolds
三流形的 Heegaard 分裂和拓扑
  • 批准号:
    1906235
  • 财政年份:
    2019
  • 资助金额:
    $ 76万
  • 项目类别:
    Continuing Grant
EAGER: Improving Lifetime and Data Reconstruction Time of Flash-Based RAID by Orchestrating Control between RAID and SSD Controllers
EAGER:通过协调 RAID 和 SSD 控制器之间的控制来提高基于闪存的 RAID 的使用寿命和数据重建时间
  • 批准号:
    1822459
  • 财政年份:
    2018
  • 资助金额:
    $ 76万
  • 项目类别:
    Standard Grant

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合作研究:DMREF:采用自适应网络进行极限力学的聚合物闭环设计
  • 批准号:
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    2024
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    $ 76万
  • 项目类别:
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Collaborative Research: DMREF: AI-enabled Automated design of ultrastrong and ultraelastic metallic alloys
合作研究:DMREF:基于人工智能的超强和超弹性金属合金的自动化设计
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