Desorption of hydrocarbon chains by association with ionic and nonionic surfactants under flow as a mechanism for enhanced oil recovery

The need to extract oil from wells where it is embedded on the surfaces of rocks has led to the development of new and improved enhanced oil recovery techniques. One of those is the injection of surfactants with water vapor, which promotes desorption of oil that can then be extracted using pumps, as the surfactants encapsulate the oil in foams. However, the mechanisms that lead to the optimal desorption of oil and the best type of surfactants to carry out desorption are not well known yet, which warrants the need to carry out basic research on this topic. In this work, we report non equilibrium dissipative particle dynamics simulations of model surfactants and oil molecules adsorbed on surfaces, with the purpose of studying the efficiency of the surfactants to desorb hydrocarbon chains, that are found adsorbed over flat surfaces. The model surfactants studied correspond to nonionic and cationic surfactants, and the hydrocarbon desorption is studied as a function of surfactant concentration under increasing Poiseuille flow. We obtain various hydrocarbon desorption isotherms for every model of surfactant proposed, under flow. Nonionic surfactants are found to be the most effective to desorb oil and the mechanisms that lead to this phenomenon are presented and discussed.Comment: 10 figures; to appear in Scientific Report

Structure of laponite-styrene precursor dispersions for production of advanced polymer-clay nanocomposites

One method for production of polymer-clay nanocomposites involves dispersal of surface-modified clay in a polymerisable monomeric solvent, followed by fast in situ polymerisation. In order to tailor the properties of the final material we aim to control the dispersion state of the clay in the precursor solvent. Here, we study dispersions of surface-modified Laponite, a synthetic clay, in styrene via large-scale Monte-Carlo simulations and experimentally, using small angle X-ray and static light scattering. By tuning the effective interaction between simulated laponite particles we are able to reproduce the experimental scattering intensity patterns for this system, with good accuracy over a wide range of length scales. However, this agreement could only be obtained by introducing a permanent electrostatic dipole moment into the plane of each Laponite particle, which we explain in terms of the distribution of substituted metal atoms within each Laponite particle. This suggests that Laponite dispersions, and perhaps other clay suspensions, should display some of the structural characteristics of dipolar fluids. Our simulated structures show aggregation regimes ranging from networks of long chains to dense clusters of Laponite particles, and we also obtain some intriguing ‘globular’ clusters, similar to capsids. We see no indication of any ‘house-of-cards’ structures. The simulation that most closely matches experimental results indicates that gel-like networks are obtained in Laponite dispersions, which however appear optically clear and non-sedimenting over extended periods of time. This suggests it could be difficult to obtain truly isotropic equilibrium dispersion as a starting point for synthesis of advanced polymer-clay nanocomposites with controlled structures

Boosting Bayesian Parameter Inference of Nonlinear Stochastic Differential Equation Models by Hamiltonian Scale Separation

Parameter inference is a fundamental problem in data-driven modeling. Given observed data that is believed to be a realization of some parameterized model, the aim is to find parameter values that are able to explain the observed data. In many situations, the dominant sources of uncertainty must be included into the model, for making reliable predictions. This naturally leads to stochastic models. Stochastic models render parameter inference much harder, as the aim then is to find a distribution of likely parameter values. In Bayesian statistics, which is a consistent framework for data-driven learning, this so-called posterior distribution can be used to make probabilistic predictions. We propose a novel, exact and very efficient approach for generating posterior parameter distributions, for stochastic differential equation models calibrated to measured time-series. The algorithm is inspired by re-interpreting the posterior distribution as a statistical mechanics partition function of an object akin to a polymer, where the measurements are mapped on heavier beads compared to those of the simulated data. To arrive at distribution samples, we employ a Hamiltonian Monte Carlo approach combined with a multiple time-scale integration. A separation of time scales naturally arises if either the number of measurement points or the number of simulation points becomes large. Furthermore, at least for 1D problems, we can decouple the harmonic modes between measurement points and solve the fastest part of their dynamics analytically. Our approach is applicable to a wide range of inference problems and is highly parallelizable.Comment: 15 pages, 8 figure

Simple flexible polymers in a spherical cage

We report the results of Monte Carlo simulations investigating the effect of a spherical confinement within a simple model for a flexible homopolymer. We use the parallel tempering method combined with multi-histogram reweighting analysis and multicanonical simulations to investigate thermodynamical observables over a broad range of temperatures, which enables us to describe the behavior of the polymer and to locate the freezing and collapse transitions. We find a strong effect of the spherical confinement on the location of the collapse transition, whereas the freezing transition is hardly effected.Comment: 7 pages, 4 figure

Supported liquid membranes: stabilization by gelation

A new method has been developed to increase the stability of supported liquid membranes. By applying a homogeneous gel network in the pores of the support both the mechanical stability (against liquid displacement) and the long term permeability increase substantially. The flux decreases only slightly because of the open structure of the gel network. A second technique, by which a thin dense gel layer is applied to the feed side of the membrane, results in a specific suppression of the formation of emulsion droplets. The stability of the membrane increases by this treatment to values which are very promising