DPD Parameters Estimation for Simultaneously Simulating Water–Oil Interfaces and Aqueous Nonionic Surfactants

Many-body dissipative particle dynamics simulations of micellization of sodium alkyl sulfates

We present a study of micelle formation in alkyl sulfate surfactants using the simulation method of many-body dissipative particle dynamics (MDPD). We parametrise our model by tuning the intermolecular interactions in order to reproduce experimental values for the chemical potential and density at room temperature. Using this approach, we find that our model shows good agreement with experimental values for the critical micelle concentration (CMC). Furthermore, we show that our model can accurately predict CMC trends, which result from varying properties such as surfactant tail length and the salt concentration. We apply our model to investigate the effect of aggregation number on various micellar properties, such as the shape of individual micelles and the fraction of bound counterions. We show that micelles become aspherical at large aggregation numbers, in line with experimental predictions, and that longer tail surfactants are generally more spherical at all aggregation numbers compared to those which are shorter. We find excellent agreement between our simulations and experimental values for the degree of counterion binding, a factor that is crucial to accurately studying micellar shape, but one that is typically overlooked in the existing literature.

Review of the Interfacial Structure and Properties of Surfactants in Petroleum Production and Geological Storage Systems from a Molecular Scale Perspective

Surfactants play a crucial role in tertiary oil recovery by reducing the interfacial tension between immiscible phases, altering surface wettability, and improving foam film stability. Oil reservoirs have high temperatures and high pressures, making it difficult and hazardous to conduct lab experiments. In this context, molecular dynamics (MD) simulation is a valuable tool for complementing experiments. It can effectively study the microscopic behaviors (such as diffusion, adsorption, and aggregation) of the surfactant molecules in the pore fluids and predict the thermodynamics and kinetics of these systems with a high degree of accuracy. MD simulation also overcomes the limitations of traditional experiments, which often lack the necessary temporal-spatial resolution. Comparing simulated results with experimental data can provide a comprehensive explanation from a microscopic standpoint. This article reviews the state-of-the-art MD simulations of surfactant adsorption and resulting interfacial properties at gas/oil-water interfaces. Initially, the article discusses interfacial properties and methods for evaluating surfactant-formed monolayers, considering variations in interfacial concentration, molecular structure of the surfactants, and synergistic effect of surfactant mixtures. Then, it covers methods for characterizing microstructure at various interfaces and the evolution process of the monolayers' packing state as a function of interfacial concentration and the surfactants' molecular structure. Next, it examines the interactions between surfactants and the aqueous phase, focusing on headgroup solvation and counterion condensation. Finally, it analyzes the influence of hydrophobic phase molecular composition on interactions between surfactants and the hydrophobic phase. This review deepened our understanding of the micro-level mechanisms of oil displacement by surfactants and is beneficial for screening and designing surfactants for oil field applications.

Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

This paper presents a novel approach to predicting critical micelle concentrations (CMCs) by using graph neural networks (GNNs) augmented with Gaussian processes (GPs). The proposed model uses learned latent space representations of molecules to predict CMCs and estimate uncertainties. The performance of the model on a data set containing nonionic, cationic, anionic, and zwitterionic molecules is compared against a linear model that works with extended connectivity fingerprints (ECFPs). The GNN-based model performs slightly better than the linear ECFP model when there is enough well-balanced training data and achieves predictive accuracy that is comparable to published models that were evaluated on a smaller range of surfactant chemistries. We illustrate the applicability domain of our model using a molecular cartogram to visualize the latent space, which helps to identify molecules for which predictions are likely to be erroneous. In addition to accurately predicting CMCs for some surfactant classes, the proposed approach can provide valuable insights into the molecular properties that influence CMCs.

Dioctyl sodium sulfosuccinate surfactant self-assembly dependency of solvent hydrophilicity: a modelling study

The self-assembly of dioctyl sodium sulfosuccinate (AOT) model surfactant in solvent environments of differing polarity is examined by means of dissipative particle dynamics (DPD) bead model parametrized against Hildebrand solubility parameters from atomistic molecular dynamics (MD) simulations. The model predicts that in hydrophobic solvents (e.g. dodecane) the surfactant forms small (Nagg ∼ 8) reverse micellar aggregates, while in a solvent corresponding to water lamellar assembly takes place, in good agreement with literature structural parameters. Interestingly, solvents of intermediate polarity lead to formation of large, internally structured aggregates. In these, the surfactant headgroups cluster within the aggregate, surrounded by a continuous phase formed by the hydrocarbon tails. We show that the partitioning of the headgroups between the aggregate surface layer and the inner clustered phase depends primarily on solvent polarity, and can be controlled by the solvent, but also system composition. Finally, we compare the DPD assembly response to simplified effective interaction potentials derived at dilute concentration limit for the interactions. The comparison reveals that the simplified effective potential descriptions provide good level of insight on the assembly morphologies, despite drastic, isotropic interactions simplification involved.

Microscopic insights on clathrate hydrate growth from non-equilibrium molecular dynamics simulations

Clathrate hydrates form and grow at interfaces. Understanding the relevant molecular processes is crucial for developing hydrate-based technologies. Many computational studies focus on hydrate growth within the aqueous phase using the 'direct coexistence method', which is limited in its ability to investigate hydrate film growth at hydrocarbon-water interfaces. To overcome this shortcoming, a new simulation setup is presented here, which allows us to study the growth of a methane hydrate nucleus in a system where oil-water, hydrate-water, and hydrate-oil interfaces are all simultaneously present, thereby mimicking experimental setups. Using this setup, hydrate growth is studied here under the influence of two additives, a polyvinylcaprolactam oligomer and sodium dodecyl sulfate, at varying concentrations. Our results confirm that hydrate films grow along the oil-water interface, in general agreement with visual experimental observations; growth, albeit slower, also occurs at the hydrate-water interface, the interface most often interrogated via simulations. The results obtained demonstrate that the additives present within curved interfaces control the solubility of methane in the aqueous phase, which correlates with hydrate growth rate. Building on our simulation insights, we suggest that by combining data for the potential of mean force profile for methane transport across the oil-water interface and for the average free energy required to perturb a flat interface, it is possible to predict the performance of additives used to control hydrate growth. These insights could be helpful to achieve optimal methane storage in hydrates, one of many applications which are attracting significant fundamental and applied interests.

A many-body dissipative particle dynamics parametrisation scheme to study behaviour at air-water interfaces

In this article, we present a general parametrisation scheme for many-body dissipative particle dynamics (MDPD). The scheme is based on matching model components to experimental surface tensions and chemical potentials. This allows us to obtain the correct surface and mixing behaviours of complex, multicomponent systems. The methodology is tested by modelling the behaviour of nonionic polyoxyethylene alkyl ether surfactants at an air/water interface. In particular, the influence of the number of ethylene oxide units in the surfactant head group is investigated. We find good agreement with many experimentally obtained parameters, such as minimum surface area per molecule; and a decrease in the surface tension with increasing surfactant surface density. Moreover, we observe an orientational transition, from surfactants lying directly on the water surface at low surface coverage, to surfactants lying parallel or tilted with respect to the surface normal at high surface coverage. The parametrisation scheme is also extended to cover the zwitterionic surfactant lauryldimethylamine oxide (LDAO), where we provide good predictions for the surface tension at maximum surface coverage. Here, if we exceed this coverage, we are able to demonstrate the spontaneous production of micelles from the surface surfactant layer.

Comprehensive review of the interfacial behavior of water/oil/surfactant systems using dissipative particle dynamics simulation

A comprehensive understanding of interfacial behavior in water/oil/surfactant systems is critical to evaluating the performance of emulsions in various industries, specifically in the oil and gas industry. To gain fundamental knowledge regarding this interfacial behavior, atomistic methods, e.g., molecular dynamics (MD) simulation, can be employed; however, MD simulation cannot handle phenomena that require more than a million atoms. The coarse-grained mesoscale methods were introduced to resolve this issue. One of the most effective mesoscale coarse-grained approaches for simulating colloidal systems is dissipative particle dynamics (DPD), which bridges the gap between macroscopic time and length scales and molecular-scale simulation. This work reviews the fundamentals of DPD simulation and its progress on colloids and interface systems, especially surfactant/water/oil mixtures. The effects of temperature, salt content, a water/oil ratio, a shear rate, and a type of surfactant on the interfacial behavior in water/oil/surfactant systems using DPD simulation are evaluated. In addition, the obtained results are also investigated through the lens of the chemistry of surfactants and emulsions. The outcome of this comprehensive review demonstrates the importance of DPD simulation in various processes with a focus on the colloidal and interfacial behavior of surfactants at water-oil interfaces.

An Efficient Random Number Generation Method for Molecular Simulation

We propose a new random number generation method, which is the fastest and the simplest of its kind, for use with molecular simulation. We also discuss the possibility of using this method with various other numerical calculations. To demonstrate the significant increases in calculation speeds that can be gained by using our method, we present a comparison with prior methods for dissipative particle dynamics (DPD) simulations. The DPD method uses random numbers to reproduce thermal fluctuations of molecules. As such, an efficient method to generate random numbers in parallel computing environments has been widely sought after. Several random number generation methods have been developed that use encryption. In this study, we establish for the first time that random numbers with desirable properties exist in the particle coordinates used in DPD calculations. We propose a method for generating random numbers without encryption that utilizes this source of randomness. This is an innovative method with minimal computational cost, since it is not dependent on a complicated random number generation algorithm or an encryption process. Furthermore, our method may lead to faster random number generation for many other physical and chemical simulations.

Surface morphology effects on clathrate hydrate wettability

HYPOTHESIS

Clathrate hydrates preferentially form at interfaces; hence, wetting properties play an important role in their formation, growth, and agglomeration. Experimental evidence suggests that the hydrate preparation process can strongly affect contact angle measurements, leading to the different results reported in the literature. These differences hamper technological progress. We hypothesize that changes in hydrate surface morphologies are responsible for the wide variation of contact angles reported in the literature.

EXPERIMENTS

Experimental testing of our hypothesis is problematic due to the preparation history of hydrates on their surface properties, and the difficulties in advanced surface characterization. Thus, we employ molecular dynamics simulations, which allow us to systematically change the interfacial features and the system composition. Implementing advanced algorithms, we quantify fundamental thermodynamic properties to validate our observations.

FINDINGS

We achieve excellent agreement with experimental observations for both atomically smooth and rough hydrate surfaces. Our results suggest that contact line pinning forces, enhanced by surface heterogeneity, are accountable for altering water contact angles, thus explaining the differences among reported experimental data. Our analysis and molecular level insights help interpret adhesion force measurements and yield a better understanding of the agglomeration between hydrate particles, providing a microscopic tool for advancing flow assurance applications.

Dissipative particle dynamics simulations in colloid and Interface science: a review

Dissipative particle dynamics (DPD) is one of the most efficient mesoscale coarse-grained methodologies for modeling soft matter systems. Here, we comprehensively review the progress in theoretical formulations, parametrization strategies, and applications of DPD over the last two decades. DPD bridges the gap between the microscopic atomistic and macroscopic continuum length and time scales. Numerous efforts have been performed to improve the computational efficiency and to develop advanced versions and modifications of the original DPD framework. The progress in the parametrization techniques that can reproduce the engineering properties of experimental systems attracted a lot of interest from the industrial community longing to use DPD to characterize, help design and optimize the practical products. While there are still areas for improvements, DPD has been efficiently applied to numerous colloidal and interfacial phenomena involving phase separations, self-assembly, and transport in polymeric, surfactant, nanoparticle, and biomolecules systems.

Self-assembly of mono- and poly-dispersed nanoparticles on emulsion droplets: antagonistic vs. synergistic effects as a function of particle size

In this work, using Dissipative Particle Dynamics simulations, we provide fundamental insights into the self-assembly of nanoparticles (NPs) on droplet surfaces in an oil-in-water emulsion. We highlight the effect of particle size on the arrangement of NPs for different interparticle interactions. NPs of two different sizes were considered. In general, when the NP-NP interaction is changed from repulsive to attractive, a transition in the NP arrangement occurs from weekly-connected networks to clusters of NPs separated by particle-free domains. When NP-NP interactions are strongly attractive, NPs yield small 3D aggregates on the droplet surface. These arrangements seem to agree with experimental observations reported in the literature. In addition, our simulations suggest that small NPs are able to diffuse more easily on the droplet surface, which leads to prompt self-organisation, while large NPs are more likely to form metastable structures, perhaps because of slow mobility and strong adsorption to the interface. Our analysis suggests that thermal fluctuations could provide the activation energy for the small NPs to escape local minima in the free energy landscape. The results obtained for systems containing NPs of two sizes provide evidence of size segregation on the droplet surface, which could be useful when NP self-assemblies are used, for example, to template supra-molecular materials. However, analysis of the simulated trajectories suggests that the results depend strongly on the initial configuration, as the larger NPs seem to impose barriers for the small NPs to adsorb and diffuse on the droplet surface.

A biointerface effect on the self-assembly of ribonucleic acids: a possible mechanism of RNA polymerisation in the self-replication cycle

Despite decades of intensive research, many questions remain on the formation and growth of the first cells on Earth. Here, we used computer simulation to compare the self-assembly process of ribonucleic acids in two environments: enclosed in a vesicle-cell membrane and in the bulk. The self-assembly was found to be more favoured in the former environment, and the origin of such a biointerface effect was identified. These results will contribute to a better understanding of the origin of life on the primitive Earth.

Quantification of Ostwald Ripening in Emulsions via Coarse-Grained Simulations

Ostwald ripening is a diffusional mass transfer process that occurs in polydisperse emulsions, often with the result of threatening the emulsion stability. In this work, we design a simulation protocol that is capable of quantifying the process of Ostwald ripening at the molecular level. To achieve experimentally relevant time scales, the dissipative particle dynamics (DPD) simulation protocol is implemented. The simulation parameters are tuned to represent two benzene droplets dispersed in water. The coalescence between the two droplets is prevented via the introduction of membranes, which allow diffusion of benzene from one droplet to the other. The simulation results are quantified in terms of the changes in the droplet volume as a function of time. The results are in qualitative agreement with experiments. The agreement with the Lifshitz-Slyozov-Wagner theory becomes quantitative when the simulated solubility and diffusion coefficient of benzene-in-water are considered. The effect of two different surfactants was also investigated. In agreement with both experimental observations and theory, the addition of surfactants at moderate concentrations decreased the Ostwald ripening rate because of the reduction in the interfacial tension between benzene and water; as the surfactant film becomes dense, other phenomena are likely to further delay the Ostwald ripening. In fact, the results suggest that the surfactant that yields higher density at the benzene-water interface delayed more effectively Ostwald ripening. The formation of micelles can also affect the ripening rate, in qualitative agreement with experiments, although our simulations are not conclusive on such effects. Our simulations show that the coarse-grained DPD formalism is able to capture the molecular phenomena related to Ostwald ripening and reveal molecular level features that could help to understand experimental observations. The results could be useful for predicting and eventually controlling the long-term stability of emulsions.