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Rapid and Scalable Manufacturing of Graphene Electrodes for Next Generation Lithium-ion Batteries

Rapid and Scalable Manufacturing of Graphene Electrodes for Next Generation Lithium-ion Batteries
快速、可扩展地制造下一代锂离子电池的石墨烯电极
批准号:
1435783
负责人:
Nikhil Koratkar
金额:
$30.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-08-01 至 2017-07-31

项目摘要

项目成果

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中文摘要
翻译
用于全电动汽车和便携式电子设备的下一代锂离子电池将需要在能量和功率密度方面取得突破性进展。为了从根本上实现这种改进,必须以可扩展和具有成本效益的方式开发和制造阳极和阴极的新材料概念。一种这样的材料概念是基于石墨烯的电极材料。然而,由于缺乏工艺可扩展性和大规模生产,传统的石墨烯电极制造工艺并不可行。该项目的目标是开发新的方法来克服这些挑战,并使可扩展和成本效益高的石墨烯电极材料的制造成为可能。这可能导致新的电池技术,除了便携式电子产品,如手机、笔记本电脑和平板电脑,还可以在下一代无线通信设备、固定存储电池、微芯片以及下一代混合动力和全电动汽车中发挥核心作用。为了解决石墨烯电极制造过程的大规模可扩展性,将探索两种新的石墨烯氧化物沉积方法。其中包括一种称为电镀的电场驱动过程和一种基于流体流动的过程,即超声波喷涂。此外,使用光热还原过程,将形成高度多孔性的石墨烯电极。为了展示工艺的可伸缩性,将开发一种用于石墨烯纸制造的基于网络的连续(卷到卷)沉积工艺。这将包括设计、原型制作和测试实验试验台。一旦投入使用,试验台将用于探索工艺变量和条件对各种关键性能指标(如产量和生产能力)的影响。通过提出的可扩展制造工艺生产的石墨烯电极的结构、性能和性能将得到深入表征,以确认它在初步实验室测试中观察到的能量和功率密度方面取得了突破性的改善。
英文摘要
The next generation of Lithium-ion batteries for all-electric vehicles as well as portable electronics devices will require breakthrough improvements in both energy and power density. In order to achieve such improvements radically new materials concepts for the anode and cathode will have to be developed and manufactured in a scalable and cost-effective manner. One such material concept is that of graphene-based electrode materials. However traditional manufacturing processes for graphene electrodes are not viable due to lack of process scalability and mass manufacture. The objective of this project is to develop novel approaches to overcome these challenges and enable the scalable and cost-effective manufacturing of graphene-based electrode materials. This can lead to new battery technologies which in addition to portable electronics such as cell phones, laptops and tablet computers could also play a central role in next generation wireless communication devices, stationary storage batteries, microchips and in next generation hybrid and all-electric vehicles.To address mass scalability of the graphene electrode manufacturing process, two new graphene oxide deposition approaches will be explored. These include an electric-field driven process called electroplating and one fluid flow-based process, namely ultrasonic spraying. Furthermore, using a photo-thermal reduction process, a highly porous graphene electrode will be formed. To demonstrate process scalability, a web-based continuous (roll-to-roll) deposition process for graphene paper manufacturing will be developed. This will involve designing, prototyping, and testing an experimental test-bed. Once operational, the test-bed will be used to explore the effect of process variables and conditions on various key performance metrics such as yield and throughput. The structure, properties and performance of the graphene electrodes produced by the proposed scalable manufacturing process will be characterized in-depth to confirm that it provides the breakthrough improvements in energy and power density that were observed in preliminary lab-scale testing.
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Collaborative Research: Fundamental Study of Niobium Tungsten Oxide Anodes for High-Performance Aqueous Batteries
  • 批准号:
    2126178
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.84万
  • 财政年份:
    2021
  • 负责人:
    Nikhil Koratkar
  • 依托单位:
Fundamental Study of Interaction of Ions Present in Water with Graphene Coatings for Energy Harvesting
  • 批准号:
    2002742
  • 项目类别:
    Standard Grant
  • 资助金额:
    $31.73万
  • 财政年份:
    2020
  • 负责人:
    Nikhil Koratkar
  • 依托单位:
Collaborative Research: Fundamental Study of Environmentally Stable and Lead-Free Chalcogenide Perovskites for Optoelectronic Device Engineering
  • 批准号:
    2013640
  • 项目类别:
    Standard Grant
  • 资助金额:
    $37.05万
  • 财政年份:
    2020
  • 负责人:
    Nikhil Koratkar
  • 依托单位:
Fundamental Study of Fatigue Life Enhancement in Hierarchical Carbon-Fiber/Epoxy/Nanoparticle Composites
  • 批准号:
    2015750
  • 项目类别:
    Standard Grant
  • 资助金额:
    $39.75万
  • 财政年份:
    2020
  • 负责人:
    Nikhil Koratkar
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis