Computer simulations in polymer physics

Edwards reptation model improves the dependence of the heights of the loss modulus peaks on the volume fractions (j)] and (ps of components. But this model lacks the higher frequency modes because it does not include tube length fluctuations. 

These tube length fluctuation modes (see Section 9.4.5) of the neighbouring chains affect the constraint release modes of a given chain. If entanglements between chains are assumed to be binary, there should be a duality between constraint release events and chain in a tube relaxation events. A release of an entanglement by reptation or tube length fluctuation of one chain in its tube leads to a release of the constraint on the second chain. If this duality is accepted, the distribution of constraint release rates can be determined self-consistently from the stress relaxation modulus of the tube model. 

The constraint release process in this self-consistent model is described by a Rouse model with random bead mobilities. The distribution of these mobilities is given by the constraint release rate distribution. The predictions of this self-consistent model are in good agreement with experiments on binary blends [see Fig. 9.27(b)]. 

The constraint release model represents the effects of surrounding 

Tlapid advances in computer technology are making computer simulations powerful tools to study polymer properties. Computer simulations occupy an important intermediate position between theory and experiments. They can provide valuable tests of assumptions and predictions of theoretical models as well as attempt to mimic experimental systems, such as polymer solutions, melts, and networks. 

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K. Kremer. Computer simulation of polymers. In M. P. Allen, D. J. Tildesley, eds. Computer Simulation in Chemical Physics. Amsterdam Kluwer, 1993, pp. 397 59. 

Kaols M H and P A Whitlock 1986. Monte Carlo Methods, Volume 1. Basics. New York, John Wiley Sons. Kermer K 1993 Computer Simulation of Polymers. In Allen M P and D J Tildesley (Editors) Computer Simulation in Chemical Physics. Dordrecht, Kluwer, NATO ASI Series 397.397-459 Rubmstein R Y 1981. Simulation and Monte Carlo Methods. New York, John Wiley Sons. 

K. Binder. General aspects of computer simulation techniques and their applications in polymer physics. In K. Binder, ed. Monte Carlo and Molecular Dynamics Simulations in Polymer Science. New York Oxford University Press, 1995, pp. 3 1. 

On the other hand, fundamental developments in polymer physics and polymer materials science in the recent past are now making it possible to consider broad ranges of their deformation and fracture from a mechanistic point of view at an appropriate molecular and morphological level. Moreover, insight gained from studies of corresponding responses of amorphous metals and semiconductors, reinforced by computational simulations and mechanistic modeling, has also broadened the perspective. 

Kremer K (2006) Polymer dynamics long time simulations and topological constraints. In Ferrario M, Ciccoti G, Binder K (eds) Computer simulations in condensed matiru systems from materials to chemical biology, vol 2. Lecture notes in physics, vol 704. Springer, Berlin, Heidelberg, p 341... 

Computer simulations of polymer-chain growth can be compared to experimental data in order to propose or support models of polymerization, as has been done for polylactide [126]. Physical properties, such as the dielectric constant of poly(N-vinyl carbazole), can often be correlated with structural features such as tacticity [127]. 

Alexei R. Khokhlov s main research interests are polymer science, statistical physics of macromolecules, physical chemistry of polyelectrolytes and ionomers, microphase separation in polymer systems, polymer liquid crystals, polyelectrolyte responsive gels, topological restrictions in polymer systems, dynamics of concentrated polymer solutions and melts, coil-globule transitions, associating polymers, computer simulation of polymer systems, biomimetic polymers, and proton-conducting polymer membranes. 

INTRODUCTION GENERAL ASPECTS OF COMPUTER SIMULATION TECHNIQUES AND THEIR APPLICATIONS IN POLYMER PHYSICS... 

Theodorou, D.N. (2006) Equilibration and coarse-graining methods for polymers, in Computer Simulations in Condensed Matter Systems From Materials to Chemical Biology, Volume 2, Lecture Notes in Physics, vol. 704, Springer, Berlin, pp. 419 48. 

Due to the complexity of macromolecular materials computer simulations become increasingly important in polymer science or, better, in what is now called soft matter physics. There are several reviews available which deal with a great variety of problems and techniques [1-7]. It is the purpose of the present introduction to give a very brief overview of the different approaches, mainly for dense systems, and a few apphcations. To do so we will confine ourselves to techniques describing polymers on a molecular level. By molecular level we mean both the microscopic and the mesoscopic level of description. In the case of the microscopic description (all)... 

The computer simulations are likely to be useful in two distinct situations— the first in which numerical data of a specified accuracy are required, possibly for some utilitarian purpose the second, perhaps more fundamental, in providing guidance to the theoretician s intuition, e.g., by comparing numerical results with those from approximate analytical approaches. As a consequence, the physical content of the model will depend upon the purpose of the calculation. Our attention here will be focused largely on the coarse-grained (lattice and off-lattice) models of polymers. Naturally, these models should reflect those generic properties of polymers that are the result of the chainlike structure of macromolecules. 

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