Monte Carlo simulations chain conformations

Zhang, L., Rafferty, J.L., Siepmann, J.I., Chen, B., and Schure, M.R., Chain conformation and solvent partitioning in reversed-phase liquid chromatography Monte Carlo simulations for various water/methanol concentrations, J. Chromatogr. A, 1126, 219, 2006. 

Chauve et al. [253] utilized the same technique to examine the reinforcing effects of cellulose whiskers in EVA copolymer nanocomposites. It was shown that larger energy is needed to separate polar EVA copolymers from cellulose than for the nonpolar ethylene homopolymer. The elastomeric properties in the presence of spherical nanoparticles were studied by Sen et al. [254] utilizing Monte Carlo simulations on polypropylene matrix. They found that the presence of the nanofillers, due to their effect on chain conformation, significantly affected the elastomeric properties of nanocomposites. 

Distribution functions for the end-to-end separation of polymeric sulfur and selenium are obtained from Monte-Carlo simulations which take into account the chains geometric characteristics and conformational preferences. Comparisons with the corresponding information on PE demonstrate the remarkable equilibrium flexibility or compactness of these two molecules. Use of the S and Se distribution functions in the three-chain model for rubberlike elasticity in the affine limit gives elastomeric properties very close to those of non-Gaussian networks, even though their distribution functions appear to be significantly non-Gaussian. 

Balazs and Lewandowski (1990) have performed simulations of the adsorption of triblock copolymers onto a planar surface, and examined the conformations of the adsorbed chains. Monte Carlo simulations were performed of the motion of hydrophilic-hydrophobic chains on a cubic lattice. These simulations revealed a complex structure in the interfacial region due to the self-assembly of chains, driven by the solvent-incompatible block, reducing adsorption onto the surface. The influence on the surface coverage of length of the hydrophilic segement, polymer concentration, interaction energy between hydrophilic block and the... 

Fig. 8. Average chain conformation for four different shear rates and (b) components of the mean squared radius of gyration, (Kgx) ( ), (R%y) (A), and (Rqz) ( ) as a function of the shear rate y. These results are from the Monte Carlo simulations of Miao et al. [67].

The dynamics of an entangled chain in an array of fixed obstacles can also be studied by Monte Carlo simulations. An initial unrestricted random walk conformation of a chain on a lattice (representing a chain in a melt) could be obtained using the method of section 9.6.2.2. The topological entanglement net of surrounding chains is represented by obstacles, sketched as solid circles in the middle of each elementary cell in Fig. 9.32. 

Many standard search methods have been used in side-chain conformation prediction, including Monte Carlo simulation [176-178], simulated annealing [179], self-consistent mean field calculations [154, 173, 180], and neural networks [170]. Self-consistent mean field calculations represent each side chain as a set of conformations, each with its own probability. Each rotamer of each side chain has a certain probability, p(n). The total energy is a weighted sum of the interactions with the backbone and interactions of side chains with each other ... 

Monte Carlo simulations generate a large number of conformations of the microscopic model under study that conform to the probability distribution dictated by macroscopic constrains imposed on the systems. For example, a Monte Carlo simulation of a melt at a given temperature T produces an ensemble of conformations in which conformation i with energy A. occurs with a probability proportional to exp (- Ej / kT). An advantage of the Monte Carlo method is that, by judicious choice of the elementary moves, one can circumvent the limitations of molecular dynamics techniques and effect rapid equilibration of multiple chain systems [65]. However, Monte Carlo... 

The RIS model can be combined with the Monte Carlo simulation approach to calculate a wider range of properties than is available from the simple matrix multiplication method. In the RIS Monte Carlo method the statistical weight matrices are used to generate chain conformations with a probability distribution that is implied in their statistical weights. 

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