Phase Separation and Aggregation in Multiblock Chains

This article focuses on phase and aggregation behavior for linear chains composed of blocks of hydrophilic and hydrophobic segments. Phase and conformational transitions of patterned chains are relevant for understanding liquid-liquid separation of biomolecular condensates, which play a prominent role in cellular biophysics, but also for surfactant and polymer applications. Previous studies of simple models for multiblock chains have shown that, depending on the sequence pattern and chain length, such systems can fall into one of two categories: displaying either phase separation or aggregation into finite-size clusters. The key new result of the present study is that both formation of finite-size aggregates and phase separation can be observed for certain chain architectures at appropriate conditions of temperature and concentration. For such systems, a bulk dense liquid condenses from a dilute phase that already contains multi-chain finite-size aggregates. The computational approach involves several distinct steps using histogram-reweighting grand canonical Monte Carlo simulations, which are described here in some level of detail.Comment: 9 pages, 11 figure

Shear induced ordering in systems with competing interactions: A machine learning study

When short-range attractions are combined with long-range repulsions in colloidal particle systems, complex microphases can emerge. Here, we study a system of isotropic particles which can form lamellar structures or a disordered fluid phase when temperature is varied. We show that at equilibrium the lamellar structure crystallizes, while out of equilibrium the system forms a variety of structures at different shear rates and temperatures above melting. The shear-induced ordering is analyzed by means of principal component analysis and artificial neural networks, which are applied to data of reduced dimensionality. Our results reveal the possibility of inducing ordering by shear, potentially providing a feasible route to the fabrication of ordered lamellar structures from isotropic particles.Comment: The following article has been accepted by the Journal of Chemical Physics AIP. After it is published, it will be found at https://aip.scitation.org/journal/jc

The heat capacity of the restricted primitive model electrolyte

The constant-volume heat capacity, C_V(T, rho), of the restricted primitive model (RPM) electrolyte is considered in the vicinity of its critical point. It is demonstrated that, despite claims, recent simulations for finite systems do not convincingly indicate the absence of a divergence in C_V(T, rho)--which would point to non-Ising-type criticality. The strong qualitative difference between C_V for the RPM and for a Lennard-Jones fluid is shown to result from the low critical density of the former. If one considers the theoretically preferable configurational heat-capacity density, C_V/V, the finite-size results for the two systems display qualitatively similar behavior on near-critical isotherms.Comment: 5 Pages, including 5 EPS figures. Also available as PDF file at http://pallas.umd.edu/~luijten/erikpubs.htm

Phase Equilibria of Lattice Polymers from Histogram Reweighting Monte Carlo Simulations

Histogram-reweighting Monte Carlo simulations were used to obtain polymer / solvent phase diagrams for lattice homopolymers of chain lengths up to r=1000 monomers. The simulation technique was based on performing a series of grand canonical Monte Carlo calculations for a small number of state points and combining the results to obtain the phase behavior of a system over a range of temperatures and densities. Critical parameters were determined from mixed-field finite-size scaling concepts by matching the order parameter distribution near the critical point to the distribution for the three-dimensional Ising universality class. Calculations for the simple cubic lattice (coordination number z=6) and for a high coordination number version of the same lattice (z=26) were performed for chain lengths significantly longer than in previous simulation studies. The critical temperature was found to scale with chain length following the Flory-Huggins functional form. For the z=6 lattice, the extrapolated infinite chain length critical temperature is 3.70+-0.01, in excellent agreement with previous calculations of the temperature at which the osmotic second virial coefficient is zero and the mean end-to-end distance proportional to the number of bonds. This confirms that the three alternative definitions of the Theta-temperature are equivalent in the limit of long chains. The critical volume fraction scales with chain length with an exponent equal to 0.38+-0.01, in agreement with experimental data but in disagreement with polymer solution theories. The width of the coexistence curve prefactor was tentatively found to scale with chain length with an exponent of 0.20+-0.03 for z = 6 and 0.22+-0.03 for z = 26. These values are near the lower range of values obtained from experimental data.Comment: 23 pages, including 7 figure

Coarse Grained Computations for a Micellar System

We establish, through coarse-grained computation, a connection between traditional, continuum numerical algorithms (initial value problems as well as fixed point algorithms) and atomistic simulations of the Larson model of micelle formation. The procedure hinges on the (expected) evolution of a few slow, coarse-grained mesoscopic observables of the MC simulation, and on (computational) time scale separation between these and the remaining "slaved", fast variables. Short bursts of appropriately initialized atomistic simulation are used to estimate the (coarse-grained, deterministic) local dynamics of the evolution of the observables. These estimates are then in turn used to accelerate the evolution to computational stationarity through traditional continuum algorithms (forward Euler integration, Newton-Raphson fixed point computation). This "equation-free" framework, bypassing the derivation of explicit, closed equations for the observables (e.g. equations of state) may provide a computational bridge between direct atomistic / stochastic simulation and the analysis of its macroscopic, system-level consequences