Date of Award
Doctor of Philosophy (PhD)
Christopher N. Bowman
Jason P. Killgore
Frank W. DelRio
Polymer particle systems have been experienced tremendous research efforts in recent years owing to their rapidly expanding applications. Improvements in synthesis methodologies have resulted in unprecedented control over the structure and chemistry of particles. In contrast to these advancements, knowledge of the experimental studies of the mechanical properties of particles remains scarce. Smart particles, specimens capable of changing shape in response to external stimuli, have specifically begun to attract attention. In this thesis two different smart particle systems capable of conforming to an infinite spectrum of user-controlled geometries are investigated: shape memory and shape reconfiguring particles.
Shape memory polymers have the unique ability to memorize and recover their permanent shapes after being programmed to hold high strain levels up to a few hundred percent. By heating the material above its glass transition temperature (Tg), applying a deformation, then cooling the material back below Tg, entropically unfavorable network topologies can be fixed and later recovered. Utilizing nanoimprint lithography, this thesis first investigates the ability of micro- and nano-scale shape memory particles to fix and then recover from user-controlled, highly strained geometries. It then examines how elastic and surface energies influence the shape memory cycle at different length scales.
Shape reconfiguration in polymeric materials is a more recent phenomenon. Covalent adaptive networks (CANs) that can reversibly break and reform covalent bonds through their molecular network have been shown to successfully relax internal stresses upon stiumuls by either heat or light. In contrast to shape memory polymers, where higher energy geometries are temporarily fixed, plasticity in CANs results in new, permanent, thermally stable geometries. In this thesis we present the first instance of micro-particles synthesized with a CAN and explore the ability of the particle system to permanently fix new network topologies.
Cox, Lewis Michael, "Shape Memorization and Reconfiguration of Polymer Particles" (2016). Mechanical Engineering Graduate Theses & Dissertations. 128.