Surfactants at fluid interfaces: molecular modeling and deep learning

Ham, Seok Gyun

Surfactants at fluid interfaces: molecular modeling and deep learning

dc.contributor.author	Ham, Seok Gyun	en
dc.contributor.committeechair	Qiao, Rui	en
dc.contributor.committeemember	Bai, Xianming	en
dc.contributor.committeemember	Tafti, Danesh K.	en
dc.contributor.committeemember	Lattimer, Brian Y.	en
dc.contributor.department	Mechanical Engineering	en
dc.date.accessioned	2024-12-17T09:00:12Z	en
dc.date.available	2024-12-17T09:00:12Z	en
dc.date.issued	2024-12-16	en
dc.description.abstract	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	en
dc.description.abstractgeneral	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.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:42032	en
dc.identifier.uri	https://hdl.handle.net/10919/123814	en
dc.language.iso	en	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Surfactant	en
dc.subject	Fluid-fluid interfaces	en
dc.subject	Molecular Modeling	en
dc.subject	Deep Learning	en
dc.title	Surfactants at fluid interfaces: molecular modeling and deep learning	en
dc.type	Dissertation	en
thesis.degree.discipline	Mechanical Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en