Molecular Dynamics Simulations for the Description of Experimental Molecular Conformation, Melt Dynamics, and Phase Transitions in Polyethylene

• Published in: Macromolecules 2015-07, Vol.48 (14), p.5016-5027
• Main Authors: Ramos, Javier, Vega, Juan F, Martínez-Salazar, Javier
• Format: Article
• Language: English
• ISSN: 0024-9297; 1520-5835
• DOI: 10.1021/acs.macromol.5b00823

Long molecular dynamics simulations of the melt dynamics, glass transition and nonisothermal crystallization of a C192 polyethylene model have been carried out. In this model, the molecules are sufficiently long to form entanglements in the melt and folds in the crystalline state. On the other hand, the molecules are short enough to enable the use of atomistic simulations on a large scale of time. Two force fields, widely used for polyethylene, are taken into account comparing the simulation results with a broad set of literature experimental data. Although both force fields are able to capture the general physics of the system, TraPPe-UA is in a better quantitative agreement with the experimental data. According with the simulation results some fundamental aspects of polyethylene physical parameters are discussed such as the characteristic ratio (C n = 8.2 and 7.6 at 500 K, for TraPPe-UA and PYS force fields, respectively), the isothermal compressibility (α = 8.57 × 10–4 K–1), the static structure factor and the melt dynamics regimes corresponding to an entangled polymer. Furthermore, the simulated T g (187.0 K) obtained for linear PE is in a very good agreement with the extrapolated T g values (185–195 K) using the Gordon–Taylor equation. Finally, the simulation of the nonisothermal crystallization process supports the view of a mixed state of adjacent and nonadjacent re-entry model. The simulated two phase model reproduces very well the initial fold length expected for high supercoolings and the segregation of the system in ordered and disordered layers. The paper highlights the importance of combining simulation techniques with experimental data as a powerful means to explain the polymer physics.