Chain Packing in Ethylene−Butene Copolymers

The single chain limit of structural relaxation in a polyolefin blend

The influence of composition on component dynamics and relevant static properties in a miscible polymer blend is investigated using molecular dynamics simulation. Emphasis is placed on dynamics in the single chain dilution limit, as this limit isolates the role of inherent component mobility in the polymer's dynamic behavior when placed in a blend. For our systems, a biased local concentration affecting dynamics must arise primarily from chain connectivity, which is quantified by the self-concentration, because concentration fluctuations are minimized due to restraints on chain lengths arising from simulation considerations. The polyolefins simulated [poly(ethylene-propylene) (PEP) and poly(ethylene-butene) (PEB)] have similar structures and glass transition temperatures, and all interactions are dispersive in nature. We find that the dependence of dynamics upon composition differs between the two materials. Specifically, PEB (slower component) is more influenced by the environment than PEP. This is linked to a smaller self-concentration for PEB than PEP. We examine the accuracy of the Lodge-McLeish model (which is based on chain connectivity acting over the Kuhn segment length) in predicting simulation results for effective concentration. The model predicts the simulation results with high accuracy when the model's single parameter, the self-concentration, is calculated from simulation data. However, when utilizing the theoretical prediction of the self-concentration the model is not quantitatively accurate. The ability of the model to link the simulated self-concentration with biased local compositions at the Kuhn segment length provides strong support for the claim that chain connectivity is the leading cause of distinct mobility in polymer blends. Additionally, the direct link between the willingness of a polymer to be influenced by the environment and the value of the self-concentration emphasizes the importance of the chain connectivity. Furthermore, these findings are evidence that the Kuhn segment length is the relevant length scale controlling segmental dynamics.

Coarse-grained description of polymer blends as interacting soft-colloidal particles

We present a theoretical approach which maps polymer blends onto mixtures of soft-colloidal particles. The analytical mesoscale pair correlation functions reproduce well data from united atom molecular dynamics simulations of polyolefin mixtures without fitting parameters. The theory exactly recovers the analytical expressions for density and concentration fluctuation structure factors of soft-colloidal mixtures (liquid alloys).

Spatial regimes in the dynamics of polyolefins: Self-motion

Molecular dynamics simulations are used to investigate the spatial dependence of dynamics in a series of polyolefins. The dynamic indicator used is the self-intermediate scattering function, which parallels the observable in an incoherent quasielastic neutron scattering experiment such as time of flight or backscattering. As with neutron time of flight experiments, two processes are evident. The fast process is a single exponential, and has relaxation times that scale as q(-2), where q is the momentum transfer. The slow process is the stretched exponential decay usually associated with the motion underlying the glass transition. The stretching exponent is a function of spatial scale, with the minimum values occurring near the spatial scale of interchain packing. Relaxation times for the slow process scale as q(-2/beta) for all materials investigated. The relative contribution of the two processes is a function of spatial scale, with the crossover from fast to slow dynamics at the location of closest possible interchain contacts, which is approximately three times the cage size. These observations apply equally well to the four materials considered. We consider the relative ordering of relaxation times of the series in light of their local chain architecture. This ordering varies depending on the observable calculated..