The Kremer–Grest (KG) polymer model is a standard model for studying generic polymer properties in molecular dynamics simulations. It owes its popularity to its simplicity and computational efficiency, rather than its ability to represent specific polymers species and conditions. Here we show that by tuning the chain stiffness it is possible to adapt the KG model to model melts of real polymers. In particular, we provide mapping relations from KG to SI units for a wide range of commodity polymers. The connection between the experimental and the KG melts is made at the Kuhn scale, i.e., at the crossover from the chemistry-specific small scale to the universal large scale behavior. We expect Kuhn scale-mapped KG models to faithfully represent universal properties dominated by the large scale conformational statistics and dynamics of flexible polymers. In particular, we observe very good agreement between entanglement moduli of our KG models and the experimental moduli of the target polymers.
We present an effective and simple multiscale method for equilibrating Kremer Grest model polymer melts of varying stiffness. In our approach, we progressively equilibrate the melt structure above the tube scale, inside the tube and finally at the monomeric scale. We make use of models designed to be computationally effective at each scale. Density fluctuations in the melt structure above the tube scale are minimized through a Monte Carlo simulated annealing of a lattice polymer model. Subsequently the melt structure below the tube scale is equilibrated via the Rouse dynamics of a force-capped Kremer-Grest model that allows chains to partially interpenetrate. Finally the Kremer-Grest force field is introduced to freeze the topological state and enforce correct monomer packing. We generate 15 melts of 500 chains of 10.000 beads for varying chain stiffness as well as a number of melts with 1.000 chains of 15.000 monomers. To validate the equilibration process we study the time evolution of bulk, collective, and single-chain observables at the monomeric, mesoscopic, and macroscopic length scales. Extension of the present method to longer, branched, or polydisperse chains, and/or larger system sizes is straightforward.
The surface characteristics of graphenes and carbon-based nanofillers are analyzed regarding activity, porosity, and roughness in comparison with mechanical and dielectric properties of polymer nanocomposites based on graphene in a nitrile–butadiene rubber (NBR) matrix. From adsorption isotherms of the fillers, information about surface area, roughness, and surface heterogeneities is evaluated. In addition, different models are used to evaluate a pore-size distribution. The composites are prepared in a conventional way by melt mixing. These graphene-based elastomer nanocomposites are mechanically and electrically characterized and compared with a standard carbon black. It is demonstrated that graphene nanocomposites show a considerably different behavior compared with carbon black, although the properties strongly depend on the graphene type. Promising results for industrial applications are found in the case of graphene/NBR composites containing dioctyl-phtalate (DOP) as a softener.
We study elastomeric networks using dissipative-particle-dynamics simulations. This soft-core method gives access to mesoscopic time and length scales and is potentially capable to study complex systems such as network defects and gels, but the unmodified method underestimates topological interactions and can only model phantom networks. In this work, we study the capability of slip springs to recover topological effects of network strands. We show that slip springs with a restricted mobility restore the topological contributions of trapped entanglements. Uniaxial strain experiments give access to the cross-link and entanglement contribution to the shear modulus of a slip-spring model network. We find these contributions to coincide with those reported for comparable hard-core Kremer–Grest networks (Gula et al. Macromolecules 2020, 53, 6907–6927). For network strands longer than the chains’ entanglement length, the contribution of slip springs to the shear modulus equals the plateau modulus of the un-cross-linked precursor melt. However, a constant number of slip springs overestimates the shear modulus for high cross-link densities. To probe their applicability, we successfully compare our simulations with experimental polyisoprene rubbers: a network obtained by parameter-free cross-linking of a simulated polyisoprene melt reproduces the viscoelastic moduli of experimental rubbers.
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