Author: Bradley Dallin

Publisher:

Published: 2021

Total Pages: 0

ISBN-13:

Understanding how interfaces interact with interfacial water molecules is vital to design novel bio- and nanomaterials that can rationally tune hydrophobic interactions (i.e., water-mediated interactions that drive the association of nonpolar materials in an aqueous environment). Materials with fine-tuned hydrophobicity would be of great importance in various biological (e.g., lipid membrane self-assembly) and industrial applications (e.g., separations). However, accurately predicting hydrophobic interactions at the nanoscale poses a significant challenge due to collective interactions between interfacial water molecules and the specific physical or chemical properties on the surface. Enormous research effort using experiments, simulations, and theory has been applied to relate surface properties, interfacial water structure, and hydrophobicity. The general goal of this dissertation is to apply simulation experiments and data-centric analysis to build upon the existing literature to fundamentally understand how spatially varying physical and chemical surface properties influence hydrophobic interactions. In Chapters 2 and 3, we examine how spatially varying physical properties influence hydrophobic interactions. We developed an experimentally validated simulation methodology to directly measure hydrophobic interactions between two uniformly nonpolar, planar self-assembled monolayers (SAMs). In addition, we quantified interfacial water structure by measuring a variety of water order parameters to determine how physical properties perturb the interfacial water hydrogen bond network. Our observations showed that increased SAM order correlated with decreased interfacial water structure. These observations predicted differences in the solvation entropy of the SAM-water interface, which we confirmed using simulation and experimental measurements of hydrophobic force as a function of temperature. In Chapter 4, we explore how specific end group chemistry and surface patterns influence hydrophobicity. We deployed a more efficient method to measure relative differences in SAM hydrophobicity to evaluate many more chemistries and patterns. We quantified interfacial water structure for each of the SAMs and trained a data-centric regression model to predict SAM hydrophobicity. Strikingly, this regression model required only five water structural features to accurately predict hydrophobicity. We further examined these features to understand fundamentally how chemistry and pattern tune interfacial water structure to alter hydrophobicity. In Chapter 5, we apply the insights gained from Chapter 3 which found that changes in the local water structural properties on the surface related to macroscopic differences in hydrophobicity. Using 2D and 3D data representations of the spatially varying interfacial water properties, we developed a machine learning model that uses convolutional neural networks to rapidly predict SAM hydrophobicity. This model provides a valuable tool to virtually screen many different SAM surface properties. In Chapter 6, we combine our newly established understanding of how physical properties (Chapters 2 and 3) and chemical properties (Chapter 4) impact hydrophobicity to predict howsurface properties of a SAM-protected gold nanoparticle (GNP) alter hydrophobicity. We find that physical properties, such as GNP curvature and ligand backbone, affect hydrophobicity by altering the GNP surface topology. We also find that changing the end group chemistry has a pronounced effect on GNP hydrophobicity. We use these insights to map hydrophobicity on GNPs which serve as an accurate prediction of small molecule binding to the GNP surface. The new insights gained in these studies about the relationship between surface properties, interfacial water structure, and hydrophobicity provide a strong basis of understanding for how physically and chemically heterogeneous materials modulate hydrophobic interactions. This new understanding could be directly applied as a framework for guided bio- and nanomaterials design in the many applications involving water-mediated interactions.