Affinity of small-molecule solutes to hydrophobic, hydrophilic, and chemically patterned interfaces in aqueous solution

Computation of Overhauser dynamic nuclear polarization processes reveals fundamental correlation between water dynamics, structure, and solvent restructuring entropy

Hydration water dynamics, structure, and thermodynamics are crucially important to understand and predict water-mediated properties at molecular interfaces. Yet experimentally and directly quantifying water behavior locally near interfaces at the sub-nanometer scale is challenging, especially at interfaces submerged in biological solutions. Overhauser dynamic nuclear polarization (ODNP) experiments measure equilibrium hydration water dynamics within 8-15 angstroms of a nitroxide spin probe on instantaneous timescales (10 picoseconds to nanoseconds), making ODNP a powerful tool for probing local water dynamics in the vicinity of the spin probe. As with other spectroscopic techniques, concurrent computational analysis is necessary to gain access to detailed molecular level information about the dynamic, structural, and thermodynamic properties of water from experimental ODNP data. We chose a model system that can systematically tune the dynamics of water, a water-glycerol mixture with compositions ranging from 0 to 0.3 mole fraction glycerol. We demonstrate the ability of molecular dynamics (MD) simulations to compute ODNP spectroscopic quantities, and show that translational, rotational, and hydrogen bonding dynamics of hydration water align strongly with spectroscopic ODNP parameters. Moreover, MD simulations show tight correlations between the dynamic properties of water that ODNP captures and the structural and thermodynamic behavior of water. Hence, experimental ODNP readouts of varying water dynamics suggest changes in local structural and thermodynamic hydration water properties.

Relationships between Water's Structure and Solute Affinity at Polypeptoid Brush Surfaces

Excellent antifouling surfaces are generally thought to create a tightly bound layer of water that resists solute adsorption, and highly hydrophilic surfaces such as those with zwitterionic functionalities are of significant current interest as antifoulant strategies. However, despite significant proofs-of-concept, we still lack a fundamental understanding of how the nanoscopic structure of this hydration layer translates to reduced fouling, how surface chemistry can be tuned to achieve antifouling through hydration water, and why, in particular, zwitterionic surfaces seem so promising. Here, we use molecular dynamics simulations and free energy calculations to investigate the molecular relationships among surface chemistry, hydration water structure, and surface-solute affinity across a variety of surface-decorated chemistries. Specifically, we consider polypeptoid-decorated surfaces that display well-known experimental antifouling capabilities and that can be synthesized sequence specifically, with precise backbone positioning of, e.g., charged groups. Through simulations, we calculate the affinities of a range of small solutes to polypeptoid brush surfaces of varied side-chain chemistries. We then demonstrate that measures of the structure of surface hydration water in response to a particular surface chemistry signal solute-surface affinity; specifically, we find that zwitterionic chemistries produce solute-surface repulsion through highly coordinated hydration water while suppressing tetrahedral structuring around the solute, in contrast to uncharged surfaces that show solute-surface affinity. Based on the relationship of this structural perturbation to the affinity of small-molecule solutes, we propose a molecular mechanism by which zwitterionic surface chemistries enhance solute repulsion, with broader implications for the design of antifouling surfaces.

Molecular Dynamics Study of the Antifouling Mechanism of Hydrophilic Polymer Brushes

We perform all-atom molecular dynamics simulations of the adsorption of amino acid side-chain analogues on polymer brushes. The analogues examined are nonpolar isobutane, polar propionamide, negatively charged propionate ion, and positively charged butylammonium ion. The polymer brushes consist of a sheet of graphene and strongly hydrophilic poly(carboxybetaine methacrylate) (PCBMA) or weakly hydrophilic poly(2-hydroxyethyl methacrylate) (PHEMA). The effective interactions between isobutane and polymer chains are repulsive for PCBMA and attractive for PHEMA. Gibbs energy decomposition analysis shows that this is due to the abundance of water in the PCBMA brush, which increases the steric repulsion and decreases the Lennard-Jones attraction. The affinity of the hydrophilic analogues is low for both PCBMA and PHEMA chains, but the balance between the components of the Gibbs energy is different for the two polymers. The simulations are performed at several θ, where θ is the degree of overlap of polymer chains. The antifouling performance against the neutral analogues is better for PCBMA than for PHEMA in the low and high θ regimes. However, in the middle θ regime, the antifouling performance of PHEMA is close to or better than that of PCBMA. This is attributed to the formation of a dense layer of PHEMA on the graphene surface that inhibits direct adsorption of analogue molecules on graphene. The charged analogues do not bind to either the PHEMA or PCBMA brush irrespective of θ.

Model-Dependent Solvation of the K-18 Domain of the Intrinsically Disordered Protein Tau

A known imbalance between intra-protein and protein-water interactions in many empirical force fields results in collapsed conformational ensembles of intrinsically disordered proteins in explicit solvent simulations that disagree with experiments. Multiple strategies have been introduced in the literature to modify protein-water interactions, which improve agreement between experiments and simulations. In this work, we combine simulations with standard and modified force fields with a spatially resolved analysis of solvation free energy contributions and compare the consequences of each strategy. We find that enhanced Lennard-Jones (LJ) interactions between protein atoms and water oxygens primarily improve the solvation of nonpolar functional groups of the protein. In contrast, modified electrostatics in the water model or strengthened LJ interactions between the protein and water hydrogens mainly affect the hydration of polar functional groups. Modified electrostatics further impact the average orientation of water molecules in the hydration shell. As a result, protein-water interactions with the first hydration layers are strengthened, while interactions with water molecules in higher hydration shells are weakened. Hence, distinct strategies to balance intra-protein and protein-water interactions in simulations have qualitatively different effects on protein solvation. These differences are not necessarily captured by comparisons to experiments that report on global parameters describing protein conformational ensembles, e.g., the radius of gyration, but will influence the tendency of a protein to form aggregates or phase-separated droplets.

Oxide- and Silicate-Water Interfaces and Their Roles in Technology and the Environment

Interfacial reactions drive all elemental cycling on Earth and play pivotal roles in human activities such as agriculture, water purification, energy production and storage, environmental contaminant remediation, and nuclear waste repository management. The onset of the 21st century marked the beginning of a more detailed understanding of mineral aqueous interfaces enabled by advances in techniques that use tunable high-flux focused ultrafast laser and X-ray sources to provide near-atomic measurement resolution, as well as by nanofabrication approaches that enable transmission electron microscopy in a liquid cell. This leap into atomic- and nanometer-scale measurements has uncovered scale-dependent phenomena whose reaction thermodynamics, kinetics, and pathways deviate from previous observations made on larger systems. A second key advance is new experimental evidence for what scientists hypothesized but could not test previously, namely, interfacial chemical reactions are frequently driven by "anomalies" or "non-idealities" such as defects, nanoconfinement, and other nontypical chemical structures. Third, progress in computational chemistry has yielded new insights that allow a move beyond simple schematics, leading to a molecular model of these complex interfaces. In combination with surface-sensitive measurements, we have gained knowledge of the interfacial structure and dynamics, including the underlying solid surface and the immediately adjacent water and aqueous ions, enabling a better definition of what constitutes the oxide- and silicate-water interfaces. This critical review discusses how science progresses from understanding ideal solid-water interfaces to more realistic systems, focusing on accomplishments in the last 20 years and identifying challenges and future opportunities for the community to address. We anticipate that the next 20 years will focus on understanding and predicting dynamic transient and reactive structures over greater spatial and temporal ranges as well as systems of greater structural and chemical complexity. Closer collaborations of theoretical and experimental experts across disciplines will continue to be critical to achieving this great aspiration.

Relationships between Molecular Structural Order Parameters and Equilibrium Water Dynamics in Aqueous Mixtures

Water's unique thermophysical properties and how it mediates aqueous interactions between solutes have long been interpreted in terms of its collective molecular structure. The seminal work of Errington and Debenedetti [Nature 2001, 409, 318-321] revealed a striking hierarchy of relationships among the thermodynamic, dynamic, and structural properties of water, motivating many efforts to understand (1) what measures of water structure are connected to different experimentally accessible macroscopic responses and (2) how many such structural metrics are adequate to describe the collective structural behavior of water. Diffusivity constitutes a particularly interesting experimentally accessible equilibrium property to investigate such relationships because advanced NMR techniques allow the measurement of bulk and local water dynamics in nanometer proximity to molecules and interfaces, suggesting the enticing possibility of measuring local diffusivities that report on water structure. Here, we apply statistical learning methods to discover persistent structure-dynamic correlations across a variety of simulated aqueous mixtures, from alcohol-water to polypeptoid-water systems. We investigate a variety of molecular water structure metrics and find that an unsupervised statistical learning algorithm (namely, sequential feature selection) identifies only two or three independent structural metrics that are sufficient to predict water self-diffusivity accurately. Surprisingly, the translational diffusivity of water across all mixed systems studied here is strongly correlated with a measure of tetrahedral order given by water's triplet angle distribution. We also identify a separate small number of structural metrics that well predict an important thermodynamic property, the excess chemical potential of an idealized methane-sized hydrophobe in water. Ultimately, we offer a Bayesian method of inferring water structure by using only structure-dynamics linear regression models with experimental Overhauser dynamic nuclear polarization (ODNP) measurements of water self-diffusivity. This study thus quantifies the relationships among several distinct structural order parameters in water and, through statistical learning, reveals the potential to leverage molecular structure to predict fundamental thermophysical properties. In turn, these findings suggest a framework for solving the inverse problem of inferring water's molecular structure using experimental measurements such as ODNP studies that probe local water properties.

Molecular Dynamics Investigation of Nanoscale Hydrophobicity of Polymer Surfaces: What Makes Water Wet?

The wettability of a polymer surface─related to its hydrophobicity or tendency to repel water─can be crucial for determining its utility, such as for a coating or a purification membrane. While wettability is commonly associated with the macroscopic measurement of a contact angle between surface, water, and air, the molecular physics that underlie these macroscopic observations are not fully known, and anticipating the relative behavior of different polymers is challenging. To address this gap in molecular-level understanding, we use molecular dynamics simulations to investigate and contrast interactions of water with six chemically distinct polymers: polytetrafluoroethylene, polyethylene, polyvinyl chloride, poly(methyl methacrylate), Nylon-66, and poly(vinyl alcohol). We show that several prospective quantitative metrics for hydrophobicity agree well with experimental contact angles. Moreover, the behavior of water in proximity to these polymer surfaces can be distinguished with analysis of interfacial water dynamics, extent of hydrogen bonding, and molecular orientation─even when macroscopic measures of hydrophobicity are similar. The predominant factor dictating wettability is found to be the extent of hydrogen bonding between polymer and water, but the precise manifestation of hydrogen bonding and its impact on surface water structure varies. In the absence of hydrogen bonding, other molecular interactions and polymer mechanics control hydrophobic ordering. These results provide new insights into how polymer chemistry specifically impacts water-polymer interactions and translates to surface hydrophobicity. Such factors may facilitate the design or processing of polymer surfaces to achieve targeted wetting behavior, and presented analyses can be useful in studying the interfacial physics of other systems.

Structural features of interfacial water predict the hydrophobicity of chemically heterogeneous surfaces

The hydrophobicity of an interface determines the magnitude of hydrophobic interactions that drive numerous biological and industrial processes. Chemically heterogeneous interfaces are abundant in these contexts; examples include the surfaces of proteins, functionalized nanomaterials, and polymeric materials. While the hydrophobicity of nonpolar solutes can be predicted and related to the structure of interfacial water molecules, predicting the hydrophobicity of chemically heterogeneous interfaces remains a challenge because of the complex, non-additive contributions to hydrophobicity that depend on the chemical identity and nanoscale spatial arrangements of polar and nonpolar groups. In this work, we utilize atomistic molecular dynamics simulations in conjunction with enhanced sampling and data-centric analysis techniques to quantitatively relate changes in interfacial water structure to the hydration free energy (a thermodynamically well-defined descriptor of hydrophobicity) of chemically heterogeneous interfaces. We analyze a large data set of 58 self-assembled monolayers (SAMs) composed of ligands with nonpolar and polar end groups of different chemical identity (amine, amide, and hydroxyl) in five mole fractions, two spatial patterns, and with scaled partial charges. We find that only five features of interfacial water structure are required to accurately predict hydration free energies. Examination of these features reveals mechanistic insights into the interfacial hydrogen bonding behaviors that distinguish different surface compositions and patterns. This analysis also identifies the probability of highly coordinated water structures as a unique signature of hydrophobicity. These insights provide a physical basis to understand the hydrophobicity of chemically heterogeneous interfaces and connect hydrophobicity to experimentally accessible perturbations of interfacial water structure.

A Molecular Dynamic Study of the Effects of Surface Partitioning on the OH Radical Interactions with Solutes in Multicomponent Aqueous Aerosols

The surface-bulk partitioning of small saccharide and amide molecules in aqueous droplets was investigated using molecular dynamics. The air-particle interface was modeled using a 80 Å cubic water box containing a series of organic molecules and surrounded by gaseous OH radicals. The properties of the organic solutes within the interface and the water bulk were examined at a molecular level using density profiles and radial pair distribution functions. Molecules containing only polar functional groups such as urea and glucose are found predominantly in the water bulk, forming an exclusion layer near the water surface. Substitution of a single polar group by an alkyl group in sugars and amides leads to the migration of the molecule toward the interface. Within the first 2 nm from the water surface, surface-active solutes lose their rotational freedom and adopt a preferred orientation with the alkyl group pointing toward the surface. The different packing within the interface leads to different solvation shell structures and enhanced interaction between the organic molecules and absorbed OH radicals. The simulations provide quantitative information about the dimension, composition, and organization of the air-water interface as well as about the nonreactive interaction of the OH radicals with the organic solutes. It suggests that increased concentrations, preferred orientations, and decreased solvation near the air-water surface may lead to differences in reactivities between surface-active and surface-inactive molecules. The results are important to explain how heterogeneous oxidation mechanisms and kinetics within interfaces may differ from those of the bulk.

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

Inverse Design of Pore Wall Chemistry To Control Solute Transport and Selectivity

Next-generation membranes for purification and reuse of highly contaminated water require materials with precisely tuned functionality to address key challenges, including the removal of small, charge-neutral solutes. Bioinspired multifunctional membrane surfaces enhance transport properties, but the combinatorically large chemical space is difficult to navigate through trial and error. Here, we demonstrate a computational inverse design approach to efficiently identify promising materials and elucidate design rules. We develop a combined evolutionary optimization, machine learning, and molecular simulation workflow to spatially design chemical functional group patterning in a model nanopore that enhances transport of water relative to solutes. The genetic optimization discovers nonintuitive functionalization strategies that hinder the transport of solutes through the pore, simply by patterning hydrophobic methyl and hydrophilic hydroxyl functional groups. Examining these patterns, we demonstrate that they exploit an unexpected diffusive solute hopping mechanism. This inverse design procedure and the identification of novel molecular mechanisms for pore chemical heterogeneity to impact solute selectivity demonstrate new routes to the design of membrane materials with novel functionalities. More broadly, this work illustrates how chemical design is a powerful strategy to modulate water-mediated surface-solute interactions in complex, soft material systems that are relevant to diverse technologies.

Learning the relationship between nanoscale chemical patterning and hydrophobicity

The hydrophobicity of proteins and similar surfaces, which display chemical heterogeneity at the nanoscale, drives countless aqueous interactions and assemblies. However, predicting how surface chemical patterning influences hydrophobicity remains a challenge. Here, we address this challenge by using molecular simulations and machine learning to characterize and model the hydrophobicity of a diverse library of patterned surfaces, spanning a wide range of sizes, shapes, and chemical compositions. We find that simple models, based only on polar content, are inaccurate, whereas complex neural network models are accurate but challenging to interpret. However, by systematically incorporating chemical correlations between surface groups into our models, we are able to construct a series of minimal models of hydrophobicity, which are both accurate and interpretable. Our models highlight that the number of proximal polar groups is a key determinant of hydrophobicity and that polar neighbors enhance hydrophobicity. Although our minimal models are trained on particular patch size and shape, their interpretability enables us to generalize them to rectangular patches of all shapes and sizes. We also demonstrate how our models can be used to predict hot-spot locations with the largest marginal contributions to hydrophobicity and to design chemical patterns that have a fixed polar content but vary widely in their hydrophobicity. Our data-driven models and the principles they furnish for modulating hydrophobicity could facilitate the design of novel materials and engineered proteins with stronger interactions or enhanced solubilities.

Molecular dynamics study of the interactions between a hydrophilic polymer brush on graphene and amino acid side chain analogues in water

We perform all-atom molecular dynamics simulations of poly(2-hydroxyethyl methacrylate) (PHEMA) brushes in aqueous solutions of isobutane, propionamide, and sodium propionate. These solutes are side chain analogues to leucine, glutamine, and glutamic acid, respectively. We compute the Gibbs energy profile of the solute's adsorption to the polymer brush and decompose it into the contributions from the steric repulsion, van der Waals interaction, and Coulomb interaction to reveal the energetic origin of repulsion or attraction of the solute by the polymer brush. The Henry adsorption constant is the amount of adsorption normalized by the concentration in aqueous solution. We examine the dependence of this quantity on the grafting density and chain length. Our results suggest that the concurrent primary and ternary adsorption mechanism may be more important than previously expected when the solute is hydrophobic.

How water wets and self-hydrophilizes nanopatterns of physisorbed hydrocarbons

HYPOTHESIS

Weakly bound, physisorbed hydrocarbons could in principle provide a similar water-repellency as obtained by chemisorption of strongly bound hydrophobic molecules at surfaces.

EXPERIMENTS

Here we present experiments and computer simulations on the wetting behaviour of water on molecularly thin, self-assembled alkane carpets of dotriacontane (n-C₃₂H₆₆ or C32) physisorbed on the hydrophilic native oxide layer of silicon surfaces during dip-coating from a binary alkane solution. By changing the dip-coating velocity we control the initial C32 surface coverage and achieve distinct film morphologies, encompassing homogeneous coatings with self-organised nanopatterns that range from dendritic nano-islands to stripes.

FINDINGS

These patterns exhibit a good water wettability even though the carpets are initially prepared with a high coverage of hydrophobic alkane molecules. Using in-liquid atomic force microscopy, along with molecular dynamics simulations, we trace this to a rearrangement of the alkane layers upon contact with water. This restructuring is correlated to the morphology of the C32 coatings, i.e. their fractal dimension. Water molecules displace to a large extent the first adsorbed alkane monolayer and thereby reduce the hydrophobic C32 surface coverage. Thus, our experiments evidence that water molecules can very effectively hydrophilize initially hydrophobic surfaces that consist of weakly bound hydrocarbon carpets.

Dissecting the Conformational Free Energy of a Small Peptide in Solution

The free energy surface of a small peptide was analyzed based on an unbiased microsecond molecular dynamics simulation. The peptide sampled disordered conformational ensembles of distinct compactness, and its free energy was decomposed into separate contributions from the intramolecular potential energy, conformational entropy, and solvation free energy. The latter was further broken down into enthalpic and entropic contributions due to peptide-water and water-water interactions. This decomposition was enabled by a generalized linear response relation between the peptide-water interaction energy and the solvation free energy, which was empirically parametrized by explicit solvation free energy calculations for representative peptide conformations. This full dissection of the peptide free energy identifies individual contributions that stabilize and destabilize compact and extended peptide conformational ensembles and reveals the origin of a free energy barrier associated with transitions between them.