Multiscale Modeling and Machine Learning Guided Characterization and Design of Chitosan-based Nanocomposites for Active Food Packaging

The goal of this project is to gain a fundamental understanding of the roles of nanofillers, interfaces, and emerging microstructures in the mechanical properties of chitosan-based polymer blend nanocomposites, in the form of nonwoven nanofiber mats through electrospinning, which has promising potential in active food packaging applications.In support of this goal, the objectives of the proposed project are the following:To elucidate how interfacial interactions, the surface features of nanofillers, and other factors influence the microstructure of biopolymer matrix using integrated atomistic and coarse-grained molecular dynamics simulations.To experimentally characterize the relationships between the microstructures of nanocomposites (nanofibers) and the properties of electrospun nonwoven nanofiber mats through judiciously chosen material systems guided by the computational insights.To integrate theory and data, produced from multiscale computational modeling and state-of-the-art experimentation, using a physics-guided machine learning approach to reveal the composition-structure-property relationship and enable the accelerated design of biopolymer nanocomposites and nonwoven nanofiber mats.