The time-dependent mechanisms of the glass transition can be exploited to program large recoverable shape changes in amorphous polymers. These shape memory polymers have enormous potential applications as morphing structures and deployable devices for aerospace and biomedical applications. The shape memory response in polymers is robust and repeatable, but the performance of current shape memory polymer devices are limited by the material's slow response time and small activation force. The shape memory performance is controlled by a combination of many factors, including thermomechanical properties and programming conditions. We developed a constitutive model to predict the shape memory behavior of amorphous networks that features the time-dependent mechanisms of the glass transition, including viscoelasticity, structural relaxation, and solvent effects. This presentation describes the model formulation, experimental characterization for parameter determination, and application of the model to predict the shape memory behavior under a variety of mechanical constraints and temperature conditions as well as the effects of physical aging and solvent absorption.
Thao D. Nguyen obtained her S.B. in Mechanical Engineering from MIT in 1998, and M.S. and Ph.D. in Mechanical Engineering from Stanford in 2004. Upon receiving her degree, she worked as a research scientist at Sandia National Laboratories in Livermore, CA then joined the Department of Mechanical Engineering at Johns Hopkins University as an Assistant Professor in 2007. Her research focuses on the biomechanics of soft tissues and the thermo-mechanics of polymers. Dr. Nguyen was awarded the 2008 Presidential Early Career Award for Scientists and Engineers (PECASE) for her work on constitutive modeling of shape memory polymers. In 2013, she received an NSF CAREER Award to investigate growth and remodeling of collagenous tissues; the inaugural UH Eshelby Mechanics Award for Young Faculty; and the ASME Sia Nemat-Nasser Early Career Award.