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CAREER: Scalable Processing of Novel Multi-scale Hybrid Composites

CAREER: Scalable Processing of Novel Multi-scale Hybrid Composites
职业:新型多尺度混合复合材料的可扩展加工
批准号:
1254540
负责人:
Erik Thostenson
金额:
$40.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-01-01 至 2019-09-30

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英文摘要
The primary research objective of the Faculty Early Career Development (CAREER) grant is to establish the processing science for an environmentally friendly and scalable processing approach toward integrating carbon nanotubes with conventional micron-sized advanced fiber reinforcement to create novel multi-scale hybrid micro/nanocomposites. The selective and intelligent integration of carbon nanotubes by hybridizing with micron-sized textile fibers enables the unique opportunity to form local multi-scale reinforcement architectures for the tailoring of both mechanical and physical properties. Preliminary research has established an efficient technique for producing extremely stable aqueous suspensions of highly dispersed carbon nanotubes in a single processing step. Through selective chemical functionalization of the nanotubes and controlled deposition onto carbon fibers it has been demonstrated that carbon nanotubes can fully penetrate fiber bundles and form chemical bonds with the fiber surface. Advanced composite materials produced from the multi-scale hybrids show remarkable improvements in shear strength and fracture toughness. The significance of the processing approach is that it allows precise control over the purity, structure and chemical functionality of the nanoscale precursor materials. The process is also carried-out under ambient conditions and is industrially scalable for future applications.If successful, the results of this research will expand the science-base for the manufacturing of nanostructured materials and understanding the basic interrelationship between manufacturing process parameters, the resulting micro- and nano-structure, and the material properties. Environmentally-friendly and cost-effective means for manufacturing carbon nanotube-based composites have the potential to dramatically enhance the large scale applications of structural and functional nanocomposites. Ultimately a fundamental knowledge of the processing-structure-property relationships will enable the nanoscale design of multifunctional nanostructured materials for new applications. The research will provide potential enabling capabilities towards the integration of adaptive, sensory, active and energy storage capabilities of nanostructures within structural materials. The educational plan involves a synergistic undergraduate research and design project to develop a lab-scale pilot facility for continuous deposition. A working pilot facility will then be constructed as a follow-on senior research project. As part of the CAREER educational plan the PI intends to extend a nanotechnology course module to reach-out to local high schools to help to build the pipeline of engineers interested in careers in materials and nanotechnology.
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PFT-TT: Using Textile-Based Wearable Sensors Coated with Nanocomposites for Virtual Health and Physical Rehabilitation
  • 批准号:
    2329838
  • 项目类别:
    Standard Grant
  • 资助金额:
    $55.0万
  • 财政年份:
    2023
  • 负责人:
    Erik Thostenson
  • 依托单位:
PFI-TT: Carbon Nanotube-Based Distributed Sensors for Structural Health Monitoring of Transportation and Oil/Gas Infrastructure
  • 批准号:
    1827729
  • 项目类别:
    Standard Grant
  • 资助金额:
    $20.0万
  • 财政年份:
    2018
  • 负责人:
    Erik Thostenson
  • 依托单位:
I-Corps: Exploring Commercialization of Nanostructured Composite Sensors
  • 批准号:
    1660189
  • 项目类别:
    Standard Grant
  • 资助金额:
    $5.0万
  • 财政年份:
    2016
  • 负责人:
    Erik Thostenson
  • 依托单位:
Carbon Nanotube-Based Sensing Composites: A Novel Distributed Sensing Approach for Structural Health Monitoring
  • 批准号:
    1234830
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2012
  • 负责人:
    Erik Thostenson
  • 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