Experimental, analytical and computational investigation of bimodal elastomer networks.

Abstract

Advances in the synthesis of macromolecular materials have led to the creation of special classes of elastomers called bimodal because of their bimodal distributions of linear starting oligomers. Numerous studies on these materials have documented anomalous increases in ultimate strength and toughness at certain mixture combinations of the constituents but have not yet identified a cause for this behavior. In addition, the ability to predict optimal mixtures still eludes polymer chemists. Constitutive models for the behavior of bimodal materials are also unable to <italic>predict</italic> material behavior, but instead tend to capture results using complicated curve fitting and iterative schemes. This thesis uncovers topological and micromechanical sources of these enhanced properties using periodic, topological simulations of chain-level network formation and develops a constitutive model of the aggregate bimodal network. Using a topological framework, in conjunction with the eight-chain averaging scheme of Arruda and Boyce, this work develops optical and mechanical constitutive models for bimodal elastomers whose results compare favorably with data in the literature. The resulting bimodal network theory is able to predict material response for a range of bimodal compositions using only two sets of data, a direct improvement over previous models. The micromechanics of elastomeric deformation and chain orientation as described by the eight-chain model are further validated by comparing optical and mechanical data generated during large deformation shear tests on unimodal materials with finite element simulations. In addition, a newly developed optical anisotropy model for the Raman tensor of polymeric materials, generated using an eight-chain unit cell model, is shown to compare favorably with tensile data in the literature. Results generated using NETSIM, a computer program developed in this thesis, have revealed naturally occurring, self-reinforcing topological features associated with experimentally observed increases in ultimate strength and toughness. The ability to predict increases in the populations of these topologies allows for the prediction of optimal bimodal mixtures and the definition of a metric of network optimality. The sol and gel fraction predictions from NETSIM also compare well with results obtained from experimental network synthesis and previous computational simulations. After formation, each molecular chain is assigned a modified entropic force-stretch law and the undeformed network is annealed, clearly illustrating how initial chain length distributions in bimodal materials deviate from the r.m.s. assumption. The results of computational annealing also highlight several structural features that have been observed experimentally in the literature. Results of the computational deformation of simulated, three dimensional networks show enhancements to strain hardening in networks with compositions similar to those which exhibited enhanced toughness in experiments. These enhanced, simulated networks also show increases in the orientation versus stretch response over compositionally similar networks. Orientation response results support previous experimental results. Increased occurrence of the doubled connection topology is found to enhance strain hardening in simulated networks and to be a positive factor in enhanced strain energy seen in experiments. The density of single cyclics, while having a positive correlation in the enhanced strain energy seen in experiments appears to negate the effect of increased populations of doubled connections in simulations.