CAREER: Multiscale Mechanics of Carbon Nanotube-Polymer Composites

这项学院早期职业发展（Career）基金将支持对碳纳米管填充聚合物复合材料断裂机制的理解和力学性能的预测研究。这些材料有潜力在美国的繁荣、安全和全球竞争力中发挥越来越大的作用，并推动航空航天、制造业、生物医学和民用基础设施等主要工业部门的经济表现。聚合物复合材料是一种可调材料，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，理解这些材料的加工-结构-性能关系和失效机制是复杂的，因为它们具有广泛的成分、现象和跨时间、长度、复杂性和不确定性尺度的相互作用。本研究旨在揭示这些关系和机制，并通过高效、机器学习辅助、实验信息、多尺度计算方法取代传统的聚合物复合材料设计的试错方法，从而加速发现具有更高可制造性、可靠性和性能的新型聚合物复合材料，最终使经济和社会受益。该项目的教育和推广部分将有助于提高STEM多学科教育的多样性，包括开发先进材料课程，并在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关系。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，人们对这些材料的变形和破坏以及填料-基质界面上的载荷传递知之甚少。该项目将进一步阐明这些材料在纳米和微观尺度上的机械响应背后的现象和机制，并通过开发一个概率框架，包括实验室测试、微观表征、图像处理、多尺度建模和模拟以及机器学习，来量化它们的加工-结构-性质关系。所涉及的不确定性将被量化，并将开发一个概率多尺度建模和仿真层次来研究高保真聚合物复合材料模型。机器学习将用于执行敏感性分析和开发聚合物复合材料性能的概率预测模型。研究结果将为异质、高性能、多功能复合材料的设计提供一条新的途径。该奖项反映了美国国家科学基金会的法定使命，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。

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.