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

Dallin, Bradley; Kelkar, Atharva; Lehn, Reid C. Van

doi:10.1039/d2sc02856e

Cited by 5 publications

(14 citation statements)

References 76 publications

(196 reference statements)

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“…In Figure a, we observe a pronounced peak in PC3 at θ ≈ 50° that cannot be directly attributed to icosahedral or tetrahedral structures. Previous work has suggested that features at θ ≈ 50° stem primarily from a small population of five-coordinate water molecules. , These authors demonstrate higher P 3 b (50°) in five-coordinate waters than in four-coordinate waters. Additionally, PC3 presents a diffuse, positive amplitude for θ > 120°.…”

Section: Resultsmentioning

confidence: 69%

“…As discussed in Section , we attribute PC3 primarily to the effect of a small population of five-coordinate waters. Furthermore, we note that Dallin et al found the population of five-coordinate waters to be predictive of solvation energetics, namely, the hydration free energy at self-assembled monolayer surfaces (SAMs) . The resulting projection of a given system’s differential three-body angle distribution onto PC3 quantifies the portion of three-body angles corresponding to a five-coordinate water molecule.…”

Section: Resultsmentioning

confidence: 87%

“…To quantify the relationship between a collection of water structural metrics and observable properties, here we apply statistical learning algorithms to systematically select the metrics that best describe and predict changes in equilibrium dynamics (diffusivity) and solvation thermodynamics (hard sphere excess chemical potential). In statistical learning applications, researchers frequently seek to reduce redundant or uninformative features from models to improve the computational efficiency and improve the robustness of predictions. ,, Due to the potentially large numbers of putative features (possibly thousands), these efforts tend to leverage low intervention dimensionality-reduction strategies. − Hence, dimensionality-reduction algorithms such as principal component analysis ,, and feature selection strategies are often applied.…”

Section: Resultsmentioning

confidence: 99%

“…While the explicit calculation of hydrophobic hydration typically requires detailed and expensive free-energy calculations, ,,,, here we choose a simpler surrogate probe and estimate the excess chemical potential, μ HS ex , of solvating a hard sphere with radius 3.3 Å (ideal small hydrophobic molecule). We calculate μ HS ex via Widom insertion in which hard sphere insertions are attempted at 10 000 random positions for 100 independent simulation snapshots and by computing the negative log probability of overlapping with zero atomic coordinates, μ HS ex = − k B T ln P ( N = 0).…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, detailed structural analyses have become important tools for the rational design of materials. ,− In one recent example, Dallin et al sought to leverage computationally inexpensive water metrics to predict the hydration free energies at a variety of chemically and spatially heterogeneous self-assembled monolayer (SAM) surfaces. They found that by characterizing only five structural metrics–pertaining to water’s orientational order, density, and hydrogen bondingthe hydration free energies could be quite accurately predicted (<4 k B T ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Resultsmentioning

confidence: 69%

Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Protein adsorption on blood-contacting surfaces: A thermodynamic perspective to guide the design of antithrombogenic polymer coatings

Crago,

Lee,

Hoang

et al. 2024

Acta Biomaterialia

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

Jiao,

Robinson Brown,

Shell

2023

Langmuir

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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-ofconcept, 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 surfacedecorated 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.

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

Abstract: 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...

Cited by 5 publications

References 76 publications

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

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

Protein adsorption on blood-contacting surfaces: A thermodynamic perspective to guide the design of antithrombogenic polymer coatings

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

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