Surfactants at fluid-fluid interfaces play a critical role in numerous engineering applications, including enhanced oil recovery and fire suppression by foams. This dissertation explores surfactant-laden fluid-fluid interfaces in two applications using molecular dynamics (MD) simulations and develops deep learning models to predict the interfacial properties of sur factants. The first study investigates slippage modulation at brine–oil interfaces by surfactants, which is relevant to enhanced oil recovery operations. We identified a slip length of 1.2 nm at clean decane-brine interfaces. Introducing surfactants to the interface leads to an initial linear decrease in slippage, with nonylphenol being more effective than phenol. As surfactant concentration increases, the reduction in slip length slows, ultimately plateauing at 1.4 nm and 0.5 nm for nonylphenol and phenol, respectively. The mechanisms underlying these slip modulation behaviors and the effects of surfactant tail length on interfacial slippage are examined by analyzing the molecular structure and transport properties of the interfacial fluids and surfactants. The second study focuses on oil transport across surfactant-laden fluid-fluid interfaces, which is relevant to firefighting foam applications. Despite its importance, the molecular details of this transport are not fully understood. Through MD simulations, the potential of mean force (PMF) and local diffusivity profiles of heptane molecules across surfactant monolayers was computed to evaluate their transport resistance across the interface. It was discovered that a heptane molecule experiences significant resistance when crossing surfactant-covered water−vapor interfaces. This resistance, influenced by high PMF and low diffusion in the surfactant head group region, increases linearly with surfactant density and dramatically spikes as the monolayer reaches saturation, becoming equivalent to the resistance of a 5 nm thick layer of bulk water. These observations provide insights into the design of surfactants aimed at reducing oil transport through water−vapor interfaces. The final part of the dissertation explores the development of a quantitative structure-property relationship (QSPR) model for surfactants using a graph neural network (GNN) based approach. The model was trained on 92 surfactant data points and demonstrated high accuracy (R² = 0.86 on average) in predicting critical micelle concentration, limiting surface tension, and maximum surface excess for various surfactants. The performance of the model in capturing the relationship between molecular design parameters and surfactant properties was critically evaluated. The dataset, model development, and assessments contribute to advancing surfactant QSPR models and their rational design for diverse industrial applications / Doctor of Philosophy / Surfactants are substances that can reduce surface tension, and they play a vital role in many applications, such as recovering oil from petroleum reservoirs and making firefighting foams. This dissertation explores how surfactants behave at the interfaces between different fluids and develops models to predict surfactant properties using machine learning methods. First, how surfactants affect the movement of oil and water at their interface is studied due to their importance in oil recovery. It was discovered that adding surfactants to the interface between oil and brine reduces the "slippage" between them, with some surfactants being more effective than others. This slippage reduction eventually stabilizes as more surfactants are added. Next, the transport of fuel molecules across water-vapor interfaces coated with surfactants is studied due to its relevance to the design of surfactants for firefighting foams. It is revealed that a heptane molecule faces increasing resistance when passing through surfactant-covered interfaces, especially when the surfactant concentration is high. Lastly, a model for predicting surfactant properties from their molecular structure is developed using the artificial intelligence (AI) approach. Using a modest collection of surfactant data, a machine learning model was trained to predict three key properties of surfactants, and the model performance is encouraging. This approach can potentially facilitate the development of new surfactants for a wide range of applications.

The lattice Boltzmann (LB) method is a versatile way to model complex fluids with hydrodynamic interactions through solving the Navier-Stokes equations. It is well-known that the role of hydrodynamic interactions is ignorable in studying the Boltzmann equilibrium of colloidal (Brownian) particles. However, full hydrodynamic interactions play an important role in their dynamics. In the LB framework for moving colloids, the “bounce-back on links” method is used to calculate the hydrodynamic forces. In this thesis, three kinds of colloidal complex fluids with full hydrodynamic interactions are simulated by lattice Boltzmann methods: colloids in a binary fluid, magnetic colloids in a single fluid and magnetic colloids in a binary fluid. First, we have done extensive simulations of nanoparticles in a binary fluid, following up previous work[1] which predicted formation of a “bijel” (bicontinuous interfacially jammed emulsion gel) in symmetric fluid quenches. Our work in this thesis focuses on the analysis of the dynamics after nanoparticles become arrested on the fluid-fluid interfaces under conditions varying from a symmetric quench to a strongly asymmetric quench. Although these new simulations extend the time window studied by a factor of two, slow domain growth is still observed. Our new analyses address the mechanics of the slow residual dynamics which involves cooperative motion of the nanoparticles at the fluid-fluid interfaces. The second topic is the LB simulation of colloidal ferrofluids to see the effect of full hydrodynamic interactions among magnetic colloids. The main focus is on how the hydrodynamic interaction affects both the equilibrium dynamics of these dipolar systems and also their transient dynamics to form clusters. Numerically, magnetic colloids are implemented with the long-range dipolar interactions described by Ewald summation. To check the effect of full hydrodynamic interactions, Brownian dynamics without any hydrodynamic interaction has been done for comparison: Monte Carlo results are also reported. We confirm that our LB generates the Boltzmann distribution for static equilibrium properties, by comparison with these methods. However, the equilibrium dynamics is altered: hydrodynamic interactions make the structural relaxations slower in both the short-time and the long-time regime. This slow relaxation rate is also found for transient motions. The third topic addresses magnetic colloids in a binary fluid. In contrast with the preceding two systems which correspond directly to laboratory experiments, this last system is so far only predicted by the LB results in this thesis. To explore this hypothetical new material by the LB method, the basic structures are investigated in terms of both domain growth morphology and the arrangement of magnetic colloids. Under conditions varying from a symmetric quench to an asymmetric quench, a chainlike arrangement is observed for dipoles jammed on the surfaces, but the basic morphology of domains is still maintained regardless of the dipolar strength. In addition, applying external field affects the morphology of domains and the stability of domain structures.

Ce travail porte sur l'étude théorique des interfaces entre deux fluides visqueux, soumis à un écoulement de Couette plan. Dans cette situation hors d'équilibre, les fluctuations thermiques de l'interface sont modifiées en raison du couplage par le cisaillement entre les effets visqueux et les effets de tension. Comme c'est le cas pour d'autres systèmes de matière molle (par exemple, les phases lamellaires), le cisaillement peut alors amplifier ou amortir les déformations interfaciales. On s'intéresse tout d'abord à la dynamique des fluctuations interfaciales. On montre que ces dernières vérifient une équation stochastique non-linéaire, dont la solution est contrôlée par un paramètre sans dimension qui contient toute l'information sur le système. La résolution à faible taux de cisaillement révèle que le déplacement quadratique moyen des fluctuations thermiques diminue avec l'écoulement, conformément aux observations expérimentales et numériques. Ensuite, on étudie l'influence des effets inertiels sur la stabilité de l'écoulement, dans le régime des fortes viscosités et des faibles tensions. Ce régime des grands nombres capillaires n'a été que très peu étudié, mais trouve sa pertinence par exemple dans les mélanges biphasiques de colloïdes et de polymères. Des critères de stabilité simples sont mis en évidence. Finalement, on réalise une étude numérique des propriétés des fluctuations interfaciales à grand cisaillement. Bien que les effets visqueux soient dominants, il en ressort une phénoménologie similaire à certains modèles de turbulence. / In this contribution, we investigate theoretically an interface between two newtonian fluids in a stationnary shear flow. The statistical properties of the interface are driven out of equilibrium due to the coupling by the shear flow between viscous and tension effects. The shear flow may either enhance or suppress interfacial deformations, as it is the case in others soft matter systems (for example, lamellar phases). The dynamics of thermal fluctuations is first considered. It is shown that fluctuation modes follow a stochastic nonlinear equation. The solution is then controlled by a single dimensionless parameter, that contains all the information of the system. The mean square displacement is obtained in the limit of small shear rates: it is found to be smoothed out by the flow, in qualitative agreement with experiments and simulations. Then, a stability analysis of the flow is achieved when inertial contibutions are taken into account. We focus on the regime of small surface tension and large viscosity. This regime has experienced a renewed interest in the last few years, in the context of phase-separated colloid-polymer mixtures. Simple criteria for the stability or instability of the flow are outveiled. Finally, a numerical study of fluctuation properties is performed in the limit of large shear rate. Although viscous effects are predominant, the results share some similarities with some turbulence models.