ABSTRACT

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.

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