Ultrasound Alignment of Carbon Nanotubes in a Polymer Medium for Additive Manufacturing of Nanocomposite Materials

用于纳米复合材料增材制造的聚合物介质中碳纳米管的超声排列

• 批准号: 1636208
• 负责人: Bart Raeymaekers
• 金额: $ 10万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636208&HistoricalAwards=false
• 关键词: Ultrasound; Alignment; Carbon; Nanotubes; Polymer

Polymer nanocomposite materials consist of a polymer matrix material reinforced with a nanoscale filler material. In particular, polymer nanocomposite materials reinforced with aligned carbon nanotubes are of interest due to their potentially higher strength-to-weight and stiffness-to-weight ratios in the direction of the carbon nanotube alignment, compared to traditional materials. Several methods exist to align carbon nanotubes in a polymer matrix material, including stretching, slicing, and techniques based on electric and magnetic fields. However, these methods are limited in terms of efficiency and scalability. This award supports fundamental research on using ultrasound waves to align carbon nanotubes in a polymer material in a scalable fashion. Research results can potentially enable integrating ultrasound directed self-assembly with a stereolithography additive manufacturing process to fabricate engineered polymer nanocomposite materials with aligned carbon nanotubes. These materials can exhibit mechanical performance that may rival or exceed that of state-of-the-art lightweight materials, and can be used in a wide range of applications, including aerospace, automobile, and infrastructure. The objective of this research is to establish the relationship between alignment of the carbon nanotubes in the photopolymer matrix and operating parameters of the ultrasound directed self-assembly technique. An experimental approach will be followed to achieve this objective. Carbon nanotubes will be dispersed in liquid photopolymer using surfactant and sonication, and ultrasound directed self-assembly will be used to create line patterns of aligned carbon nanotubes within the photopolymer. The photopolymer will subsequently be cured into a rectangular specimen using a stereolithography additive manufacturing process. The following operating parameters of the ultrasound directed self-assembly technique will be varied: ultrasound wave frequency from 0.19 to 2.00 MHz, ultrasound wave driving voltage amplitude from 10 to 40 Volt peak-to-peak, the photopolymer layer thickness from 100 to 500 micrometer, carbon nanotube length from 10 to 100 micrometer, and carbon nanotube weight fraction from 0.01 to 1 weight percent. The alignment of the carbon nanotubes in the cured photopolymer matrix will be determined using a suite of characterization tools that include scanning electron microscopy, electron computed tomography, and X-ray computed tomography. Furthermore, the alignment of the carbon nanotubes will be quantified using the Hermans' orientation factor and the full-width at half maximum techniques.

聚合物纳米复合材料由用纳米级填料材料增强的聚合物基质材料组成。特别地，与传统材料相比，用定向碳纳米管增强的聚合物纳米复合材料由于其在碳纳米管定向方向上的潜在更高的强度-重量比和刚度-重量比而受到关注。有几种方法可以在聚合物基质材料中排列碳纳米管，包括拉伸，切片和基于电场和磁场的技术。然而，这些方法在效率和可扩展性方面受到限制。该奖项支持使用超声波以可扩展的方式在聚合物材料中排列碳纳米管的基础研究。研究结果可能使超声引导自组装与立体光刻增材制造工艺相结合，以制造具有对齐碳纳米管的工程聚合物纳米复合材料。这些材料的机械性能可以与最先进的轻质材料相媲美或超过最先进的轻质材料，并且可以用于广泛的应用，包括航空航天、汽车和基础设施。 本研究的目的是建立在光聚合物基体中的碳纳米管的排列和超声引导自组装技术的操作参数之间的关系。为实现这一目标，将采取一种试验性的办法。碳纳米管将使用表面活性剂和超声处理分散在液体光聚合物中，并且超声引导的自组装将用于在光聚合物内产生对齐的碳纳米管的线图案。随后将使用立体光刻增材制造工艺将光聚合物固化成矩形样本。将改变超声引导自组装技术的以下操作参数：超声波频率为0.19至2.00 MHz，超声波驱动电压振幅为10至40 V峰-峰，光聚合物层厚度为100至500微米，碳纳米管长度为10至100微米，碳纳米管的重量分数为0.01 - 1wt%。固化的光聚合物基质中的碳纳米管的排列将使用一套表征工具来确定，所述表征工具包括扫描电子显微镜、电子计算机断层扫描和X射线计算机断层扫描。此外，碳纳米管的排列将使用赫尔曼取向因子和半高全宽技术进行量化。

项目成果

Quantifying macro- and microscale alignment of carbon microfibers in polymer-matrix composite materials fabricated using ultrasound directed self-assembly and 3D-printing

• DOI: 10.1016/j.compositesa.2019.105713
• 发表时间: 2020-02
• 期刊: Composites Part A-applied Science and Manufacturing
• 影响因子: 8.7
• 作者: K. Niendorf;B. Raeymaekers