Equilibration of linear polyethylene melts with pre-defined molecular weight distributions employing united atom Monte Carlo simulations.

Possessing control over the molecular size (molecular weight/chain length/degree of polymerization) distribution of a polymeric material is extremely important in applications. This is manifested de facto by the extensive contemporary scientific literature on processes for controlling this distribution experimentally. Yet, the literature on computational techniques for achieving prescribed molecular size distributions in simulations and exploring their impact on properties is much less abundant than its experimental/technical counterpart. Here, we develop-on the basis of united atom melt simulations employing connectivity-altering Monte Carlo moves-a new Metropolis selection criterion that drives the multichain system to a prescribed but otherwise arbitrary distribution of molecular sizes. The new formulation is a generalization of that originally proposed [P. V. K. Pant and D. N. Theodorou, Macromolecules 28, 7224 (1995)], but simpler and more computationally efficient. It requires knowledge solely of the target distribution, which need not be normalized. We have implemented the new formulation on long-chain linear polyethylene melts, obtaining excellent results. The target molecular size distribution can be provided in tabulated form, allowing absolute freedom as to the types of chain size profiles that can be simulated. Distributions for which equilibration has been achieved here for linear polyethylene include a truncated most probable, a truncated Schulz-Zimm, an arbitrary one defined in tabulated form, a broad truncated Gaussian, and a bimodal Gaussian. The last two are comparable to those encountered in industrial applications. The impact of the molecular size distribution on the properties of the simulated melts, such as density, chain dimensions, and mixing thermodynamics, is explored.

A coarse-grained model for capturing the helical behavior of isotactic polypropylene.

Understanding the process-property relations of helical polymers using molecular simulations has been an attractive research field over the years. Specifically, isotactic polypropylene still remains a challenge for current computational experimentation, as it exhibits phenomena such as crystallization that emerge on large spatial and temporal scales. Coarse-graining is an efficient technique for approaching such phenomena, although previous coarse-grained models lack in preserving important atomistic and structural details. In this paper we develop a new coarse-grained model, based on the popular MARTINI force field, that is able to reproduce the helical behavior of isotactic polypropylene. To test the model, the predicted statistical and structural properties (characteristic ratio, density, entanglement molecular weight, solubility parameter in the melt) are compared with previous simulation results and available experimental data. For the development of the new coarse-grained force field, a single unperturbed chain Monte Carlo algorithm has been implemented: an efficient algorithm which samples conformations representative of a melt by simulating just a single chain.

Promising Route for the Development of a Computational Framework for Self-Assembly and Phase Behavior Prediction of Ionic Surfactants Using MARTINI.

Surfactants are amphiphilic molecules with multiple uses and industrial applications as detergents, wetting agents, emulsifiers, and so forth. They can be divided into three main categories: nonionic, ionic, and zwitterionic. The development of a universal computational framework able to predict key properties such as their critical micelle concentration (cmc) and the size of the micelles they form and to ultimately extract phase diagrams for their aqueous solutions, possibly in the presence of salts and oils, using their chemical constitution as input, would provide valuable information for the design and the production of these materials. In this work, we focus on ionic surfactants and investigate a possible route toward the development of such a framework based on coarse-grained simulations using the MARTINI forcefield in two versions: its implicit solvent version, called Dry MARTINI, and its explicit solvent version, called Wet MARTINI. The surfactants considered in our efforts are the anionic sodium dodecyl sulfate (SDS) and the three cationic cetyl, dodecyl, and octyl trimethyl ammonium bromide (CTAB, DTAB, and OTAB, respectively). First, we choose their mapping onto coarse-grained MARTINI beads. Next, we estimate their cmc's, their peak aggregation numbers, Nagg, and in the case of SDS, its small angle neutron scattering pattern at low concentrations, applying the Dry MARTINI forcefield. With a single modification to the Lennard-Jones energy parameter between hydrophobic beads and invoking Ewald summation with a physically meaningful dielectric constant for electrostatic interactions, our estimates are in very good agreement with experimental results. Furthermore, we predict the phase behavior of SDS/water and CTAB/water binary solutions using Wet MARTINI and find semiquantitative agreement with experimental phase diagrams. We conclude that the MARTINI forcefield, with careful treatment of electrostatic interactions and appropriate modification of parameters for some key functional groups, can be a powerful ally in the quest for a universal computational framework for the design of new surfactants with improved properties.

Thermodynamic Analysis of n-Hexane-Ethanol Binary Mixtures Using the Kirkwood-Buff Theory.

A complete thermodynamic analysis of mixtures consisting of molecules with complex chemical constitution can be rather demanding. The Kirkwood-Buff theory of solutions allows the estimation of thermodynamic properties, which cannot be directly extracted from atomistic simulations, such as the Gibbs energy of mixing (Δmix G). In this work, we perform molecular dynamics simulations of n-hexane-ethanol binary mixtures in the liquid state under two temperature-pressure conditions and at various mole fractions. On the basis of the recently published methodology of Galata [ Fluid Phase Equilib. 2018 , 470 , 25 - 37 ] , we first calculate the Kirkwood-Buff integrals in the isothermal-isobaric ( NpT) ensemble, identifying how system size affects their estimation. We then extract the activity coefficients, excess Gibbs energy, excess enthalpy, and excess entropy for the n-hexane-ethanol binary mixtures we simulate. We employ two approaches for quantifying composition fluctuations: one based on counting molecular centers of mass and a second one based on counting molecular segments. Results from the two approaches are practically indistinguishable. We compare our results against predictions of vapor-liquid equilibria obtained in a previous simulation work using the same force field, as well as with experimental data, and find very good agreement. In addition, we develop a simple methodology to identify the hydrogen bonds between ethanol molecules and analyze their effects on mixing properties.