CAREER: Multiscale Mechanics of Carbon Nanotube-Polymer Composites

职业：碳纳米管-聚合物复合材料的多尺度力学

基本信息

批准号：
2046332
负责人：
Hessam Yazdani
金额：
$ 56.26万
依托单位：
Howard University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046332&HistoricalAwards=false
关键词：
CAREER Multiscale Mechanics Carbon Nanotube

项目摘要

This Faculty Early Career Development (CAREER) grant will support research on understanding the fracture mechanisms and predicting the mechanical properties of carbon nanotube-filled polymer composites. These materials have the potential to play a growing role in the prosperity, security, and global competitiveness of the United States and propelling the economic performance of major industrial sectors such as aerospace, manufacturing, biomedical, and civil infrastructure. Polymer composites are tunable materials whereby changes to their constituents, processing conditions, and microstructure one can achieve products with distinct functions. Understanding the processing-structure-property relations and failure mechanisms of these materials, however, is complicated because they feature a wide range of compositions, phenomena, and interactions across several scales of time, length, complexity, and uncertainty. This research aims to unravel these relations and mechanisms and in turn supplant the traditional trial-and-error approach to the design of polymer composites by an efficient, machine learning-assisted, experiment-informed, multiscale computational approach that will accelerate the discovery of novel polymer composites with improved manufacturability, reliability, and performance, ultimately benefiting the economy and society. The educational and outreach components of this project will contribute to enhancing diversity in STEM multidisciplinary education and include developing courses in advanced materials and forming sustainable collaborations between the PI’s research group and industry partners and professional organizations. Among the scientific and technological challenges remaining in the field of carbon nanotube-filled polymer composites, one of the least-understood areas is the deformation and failure of these materials and a poor understanding of load transfer in them at the filler-matrix interface. This project will further elucidate the phenomena and mechanisms that underlie the mechanical response of these materials at the nano- and microscales and quantify their processing-structure-property relationships by developing a probabilistic framework comprising laboratory tests, microscopic characterizations, image processing, multiscale modeling and simulations, and machine learning. The uncertainties involved will be quantified, and a probabilistic multiscale modeling and simulation hierarchy will be developed to study high-fidelity models of polymer composites. Machine learning will be used to perform sensitivity analyses and develop probabilistic predictive models for the properties of polymer composites. The study outcome will offer a new route to design heterogeneous, high-performance, and multifunctional composite materials.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.

该学院的早期职业发展（CAREER）资助将支持了解断裂机制和预测碳纳米管填充聚合物复合材料的机械性能的研究，这些材料有潜力在繁荣、安全和全球竞争力中发挥越来越大的作用。聚合物复合材料是可调节材料，通过改变其成分、加工条件和微观结构，可以获得具有不同功能的产品。然而，这些材料的加工-结构-性能关系和失效机制很复杂，因为它们具有广泛的成分、现象和跨时间、长度、复杂性和不确定性的相互作用。关系和机制，进而通过高效的、机器学习辅助的、基于实验的、多尺度计算方法取代传统的聚合物复合材料设计试错方法，这将加速发现具有改进的可制造性的新型聚合物复合材料，可靠性，该项目的教育和推广部分将有助于增强 STEM 多学科教育的多样性，包括开发先进材料课程以及在 PI 的研究小组与行业合作伙伴和专业组织之间建立可持续的合作。在碳纳米管填充聚合物复合材料领域仍然存在的科学和技术挑战中，最不了解的领域之一是这些材料的变形和失效，以及对它们在填料-基体界面处的载荷传递了解甚少。将进一步阐明这些材料在纳米和微米尺度上的机械响应的现象和机制，并通过开发一个由实验室测试、微观表征、图像处理、多尺度建模和模拟组成的概率框架来量化其加工-结构-性能关系，所涉及的不确定性将被量化，并且将开发概率多尺度建模和模拟层次结构来研究聚合物复合材料的高保真模型，机器学习将用于执行敏感性分析和开发。该研究成果将为设计反映异质、高性能和多功能复合材料提供一条新途径。该奖项是 NSF 的法定使命，并通过使用基金会的知识进行评估，被认为值得支持。优点和更广泛的影响审查标准。