The dependence of the individual mean square displacement of rare gases in binary mixtures is studied by a combined experimental and theoretical approach. We show that the diffusion constant can be varied in a considerable range by changing the molar fractions of the mixtures. On the experimental side, NMR diffusion measurements are done on hyperpolarized 3He and 129Xe, mixed with several inert buffer gases, in the presence of a magnetic field gradient. The results are compared to diffusion coefficients obtained from atomistic molecular dynamics simulations based on Lennard-Jones type potentials of the corresponding gas mixtures, and to appropriate analytical expressions, yielding very good mutual agreement. This study is the first quantitative validation of the effects of the mutual interactions between gas particles on the individual diffusion properties. It is shown that the dependency of gas phase diffusion properties on the local chemical environment may not be neglected, e.g. in diffusion-controlled chemical reactions.
Trapped entanglements, cross-linker functionality, and elastically effective chains are the sources of elasticity of polymer networks and gels. However, despite more than 80 years of theoretical and experimental research in this field, still little is known about their relative contribution to network elasticity. In this work, we use double quantum nuclear magnetic resonance (DQ NMR) experiments to characterize the elasticity of model polymer networks prepared with cross-linkers of mixed functionality and control of structural defects. An order parameter that condensates the elastic response within the theoretical framework of the entangled phantom theory for rubber elasticity was identified. Standard lore dictates that low molecular weight precursors for the elastically active chains leads to a negligible contribution of trapped entanglements. Here we show that the contribution of trapped entanglements may equal the contribution coming from elastically active material and that it is independent of network topology.
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