Modeling the Mechanics and Functionality of Soft Polymer Composites

Abstract

Soft multifunctional polymer composites are a class of composite material that can exhibit low elastic modulus (<10MPa) while also have excellent thermal, electrical conduction, and energy storage properties. These functional properties are essential for the composite to be integrated into various devices such as soft thermal, dielectric actuators, tactile and strain sensors. Most existing soft polymers such as elastomers and hydrogels are highly deformable but are not necessarily ideal for device integration. Despite that, early pioneering studies had managed to use commercial elastomers such as Sylgard 184 and Ecoflex 00-30 to create pneumatic soft robots or flexible circuit boards for rigid electronics. However, these pristine polymers are electrically and thermally insulative which limits their potential in creating advanced devices. To overcome this, soft polymers such as elastomers are embedded with distinct types of conductive metallic, non-metallic solids, and more recently non-toxic liquid metals (LM) such as eutectic gallium-indium (EGaIn). EGaIn polymer composite possesses attractive features for wearable applications because the combined fluidic and metallic nature of liquid metals enable the synthesized composite to retain high compliance while enhancing their final thermal, electrical conductivity, and permittivity. Despite that, high volume fraction (50%) of EGaIn inclusions is needed to achieve approximately ten times improvement to the conductive properties of the polymer composite. In addition, the native oxide shell formed around the EGaIn droplets highly influences the elasticity of the EGaIn polymer composite and their ability to form conductive pathways under mechanical loading. To understand the role of gallium oxide shell, two main modeling frameworks are introduced to correlate the relative size of this oxide shell with the bulk mechanical behaviors of EGaIn polymer composite and to predict the rupture stress in the oxide shell when external mechanical loading is applied. Alternately, solid conductive fillers can improve the functional properties such as the electrical, thermal conductivity, and permittivity of a soft composite by tenths to even hundredth times at low volume fraction. One of the promising nanomaterials capable of achieving such enhancement in a polymer composite is the recently found two dimensional (2D) metallic nanosheets known as MXenes. Titanium carbide (Ti3C2) is a common type of MXenes which can have an impressive lateral length of several microns with one nanometer thickness. In addition, Ti¬3C2 sheets are also an attractive nanomaterial because of their large intrinsic electrical conductivity and lower elastic modulus than other 2D nanomaterials, notably graphene. When these large 2D fillers are dispersed in polymers, these nanosheets can form percolation network at very low concentration (<1%) which can tremendously enhance the effective conductivity of the composite. However, the concentration of this 2D solid filler needs to be carefully optimized to ensure minimal stiffening effects of the overall soft matter composite while high enough for the composite to achieve large conductivity. To resolve this issue, the following dissertation presents a theoretical model that correlates the microstructure arrangement of MXene polymer composite with the resulting tradeoffs between their elastic modulus and their thermal or dielectric properties. Viscoelasticity is a dominant mechanical behavior found in most soft matter composites, particularly those with hydrogel matrix. This property causes the composite to relax when constant strain is applied (stress relaxation) and to gradually increase in strain (Creep) when constant load is applied. This time-dependent mechanical behavior created a unique difficulty when conducting dynamic mechanical testing of layered-cell-in-gel composite as maintaining a constant mean stress/strain on the overall hydrogel composite is challenging. However, regulating this stress condition is vital to study the mechano-response of the embedded cells. Hence, to optimize the loading parameter applied on this hydrogel composite, the current dissertation presents an elegant predictive model based on micromechanics formulation which can help rapidly predict the stress-time behavior of the composite and to guide experiment executions.