Quantitative comparison of primitive chain network simulations with literature data of linear viscoelasticity for polymer melts

Masubuchi, Yuichi; Ianniruberto, Giovanni; Greco, Francesco; Marrucci, G.

doi:10.1016/j.jnnfm.2007.02.014

Cited by 65 publications

(100 citation statements)

References 22 publications

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“…The longest relaxation time was found to scale with the number of entanglements, Z, as Z 3.5±0.1 , while the self-diffusion coefficient was found to scale as D cm ∼ Z −2.4±0.2 ; both agree well with experimental results [144]. Later on, the PCN model was extended to study the relationship between entanglement length and plateau modulus [145][146][147][148][149]. It was also extended to study star and branched polymers [150], nonlinear rheology [151][152][153], phase separation in polymer blends [154,155], block copolymers [156] and the dynamics of confined polymers [157].…”

Section: Slip-link Modelsupporting

confidence: 67%

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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Section: Slip-link Modelsupporting

confidence: 67%

Challenges in Multiscale Modeling of Polymer Dynamics

et al. 2013

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“…The unit of molecular weight M 0 corresponds to the average molecular weight of subchain under equilibrium, and the value for polystyrene melt was set to 11k as determined in the previous study [19]. Owing to the fluctuation around network nodes in our model, the subchain molecular weight M 0 is similar to, but different from the entanglement molecular weight M e that is determined from the plateau modulus via the rubber theory.…”

Section: Model and Simulationmentioning

confidence: 99%

Primitive chain network simulations for elongational viscosity of bidisperse polystyrene melts

Takeda

Sukumaran

Sugimoto

et al. 2015

Adv. Model. and Simul. in Eng. Sci.

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“…Nevertheless, for the presented two cases, G (t ) obtained from experiments for entangled polymeric liquids is fairly reproduced. 5,[7][8][9][10][11] For the contribution of A s (t ) in S s (t ), it seems that the ratio of A s (t ) to S s (t ) for e = 0.5 is larger than that for e = 1.0. Figure 2 shows k (t ) obtained from S s (t ) and A s (t ) utilized in Fig.…”

Section: Correlation Functionsmentioning

confidence: 96%