Entanglement Relaxation Time in Polyethylene: Simulation versus Experimental Data

Ramos, Javier; Vega, Juan Francisco; Theodorou, Doros N.; Martínez‐Salazar, Javier

doi:10.1021/ma702445e

Cited by 53 publications

(66 citation statements)

References 36 publications

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“…MSL 50 CH 2 (BC: 20/1000 C) was the critical length to form lamellae crystal in precisely branched polyethylene crystallization. This critical MSL was confirmed that 1 or 2 full folds and 3 edge folds formed in the linear PE chain simulation of 192 CH 2 . When BC above 30/1000 C, there was no folds at all for one MSL, neither full nor edge fold.…”

Section: Resultssupporting

confidence: 72%

Effects of Branch Content and Branch Length on Polyethylene Crystallization: Molecular Dynamics Simulation

Gao

Shao

et al. 2016

Macro Theory & Simulations

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Influences of branch content (BC) and branch length (BL) on isothermal crystallization of precisely branched polyethylene are studied by molecular dynamics simulation. Branch acts as a defect both in nucleation and crystal growth process. BC affects not only crystallization kinetics but also final morphologies. Crystallization rate and crystallinity decrease as BC increases. Morphology Regimes change from lamellae crystal to bundle crystal at critical BC (20/1000 C) because of different folding pattern. 50 CH2 is the critical methyl sequence length to form lamellae crystal. Lamellae thicknesses of final morphologies decrease in gradient corresponding to Morphologies Regimes. BL has no influence on the crystallization kinetics, and only affects the final morphologies when more branches inclusion happens with BL increasing. Trans‐rich phenomenon in pre‐crystalline state is observed. Crystallization process begins at the end of induction stage when trans state population reaches a critical value, and this value is independent of BC and BL.

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Section: Resultssupporting

confidence: 72%

Effects of Branch Content and Branch Length on Polyethylene Crystallization: Molecular Dynamics Simulation

Gao

Shao

et al. 2016

Macro Theory & Simulations

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“…This is discussed later. Other methods of determining entanglements include: characterization by neutron spin‐echo, NMR relaxation methods, and Monte Carlo simulations . However, these methods are still not an alternative to the rheological research.…”

Section: Characterization Of Entanglementsmentioning

confidence: 99%

The Entanglements of Macromolecules and Their Influence on the Properties of Polymers

Pawlak

2019

Macro Chemistry & Physics

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Macromolecules form entanglements, the presence of which determines many of the polymer's properties; in solution, melt, and solid state. In this review, how the properties of polymers (rheological, thermal, and mechanical), depend on the concentration of the entanglements, is discussed. Although a typical polymer is characterized by the equilibrium density of entanglements, there are methods that can be applied for the reduction of this density. The methods used most often include dissolving a polymer and rapid freezing, crystallization during polymerization, and crystallization from a cooled dilute solution. The properties of disentangled polymers are significantly different from the properties of fully entangled polymers. These properties are discussed in relation to their dependence on the density of the entanglements. The disentangled material can be re‐entangled by the annealing of the polymer; however, reaching the equilibrium density of the entanglements is usually a slow process. It means that a polymer may be processed without losing its disentanglements. The properties of the re‐entangled polymer are usually close to the properties of the initial polymer.

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“…Thus, without the enhancement of α, the above multiscale computational framework cannot capture the correct relaxation behavior of these polydisperse chains. Since the PDI values of HPB TM polymersare rather small ( 1.1) [334], while they are as large as 1.8 to 2.9 for PEs [335], a large α = 0.7 value has been chosen for HPBs and a small one, α = 0.3, for PEs when t > τ d . With the enhancement of α, the predicted G(t) is in agreement with experimental results, which further confirms the fractional diffusive behavior of polydisperse chains.…”

Section: (A) (B)mentioning

confidence: 99%

Challenges in Multiscale Modeling of Polymer Dynamics

et al. 2013

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The mechanical and physical properties of polymeric materials originate from the interplay of phenomena at different spatial and temporal scales. As such, it is necessary to adopt multiscale techniques when modeling polymeric materials in order to account for all important mechanisms. Over the past two decades, a number of different multiscale computational techniques have been developed that can be divided into three categories: (i) coarse-graining methods for generic polymers; (ii) systematic coarse-graining methods and (iii) multiple-scale-bridging methods. In this work, we discuss and compare eleven different multiscale computational techniques falling under these categories and assess them critically according to their ability to provide a rigorous link between polymer chemistry and rheological material properties. For each technique, the fundamental ideas and equations are introduced, and the most important results or predictions are shown and discussed. On the one hand, this review provides a comprehensive tutorial on multiscale computational techniques, which will be of interest to readers newly entering this field; on the other, it presents a critical discussion of the future opportunities and key challenges in the multiscale geometric mapping matrix between all-atomistic and coarse-grained models N number of monomers per chain N e entanglement length, which is the number of monomers between two entanglements n v number of polymer chains per unit volume n ij (n 0 (r ij )) entanglement number (at equilibrium) between particle pairs i and j p r , P R configurational probability distributions in all-atomistic and coarse-grained models, respectively R ee end-to-end distance of polymer chain R G radius of gyration of polymer chain s segment index/contour length variable along primitive chain S(q) single chain coherent dynamic scattering function Z number of entanglements per chain, defined as Z = N/N e α exponent in standard Mittag-Leffler function σ

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Entanglement Relaxation Time in Polyethylene: Simulation versus Experimental Data

Cited by 53 publications

References 36 publications

Effects of Branch Content and Branch Length on Polyethylene Crystallization: Molecular Dynamics Simulation

Effects of Branch Content and Branch Length on Polyethylene Crystallization: Molecular Dynamics Simulation

The Entanglements of Macromolecules and Their Influence on the Properties of Polymers

Challenges in Multiscale Modeling of Polymer Dynamics

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