Doctor of Philosophy (PhD)
Mechanical and Industrial Engineering
With the increase in popularity of shape memory polymers (SMPs), especially in applications such as aerospace, textile, biomedical engineering, and even structures, the weight of the material and the devices made with it has always been a crucial factor. Using the shape memory polymer as a matrix to make a syntactic foam is one of the best and most affordable approaches to creating a lighter material that still has the shape memory effect. The addition of particles of different stiffness, strength, and size, with variable fractions, creates a composite that enables engineering the mechanical, as well as other physical and chemical, properties to tailor to the specific needs of each application. This ability further broadens the applicability of the SMPs in different industries depending on their particular demands. Notably, in applications such as aerospace and biomedical engineering, where the minimal volume is precious and replacing devices is very costly or even impossible, the proper combination of matrices and particles may expand the functionality of the obtained material to perform multiple tasks. In this dissertation, four connected studies are presented for improving the properties of SMPs by preparing SMP-based syntactic foams. These syntactic foams are made by mechanically dispersing different types of hollow glass microbubbles (HGMs) in two SMP matrices. Each chapter is a step toward advancing the functionality of the resulting material and exploring additional applications for shape memory polymers. First, the mechanical properties of a two-way SMP were improved to accommodate the next generation of actuators. Then, the same syntactic foam was used to create a hybrid open-cell foam to enable a multitude of additional applications. The possibility of an ultra-light SMP was examined next, where the syntactic foam is also capable of healing itself. Finally, the prospect of adding ferromagnetism and conductivity to the syntactic foam and the new opportunities it brings was investigated by metal-coated bubbles.
Sarrafan, Siavash, "Shape Memory Polymer-Based Multifunctional Syntactic Foams" (2023). LSU Doctoral Dissertations. 6136.
Available for download on Sunday, October 01, 2023
Engineering Science and Materials Commons, Mechanical Engineering Commons, Polymer and Organic Materials Commons