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The surface properties of biologically active nanoparticles (NPs) are often dictated by synthetic ligands that are grafted to the NP core to form a protecting monolayer. Ligand selection is thus critical in determining NP surface properties and corresponding interactions at the nano-bio interface, which are relevant to numerous applications including drug delivery and biosensing. However, chemically specific structure–property relationships for rationally selecting ligands to achieve desired biointeractions are largely lacking. In this Focus Article, we review the challenges associated with relating ligand chemical properties to monolayer-protected NP surface properties due to the interplay of ligand–ligand, ligand–solvent, and ligand–biomolecule interactions that are difficult to anticipate. In particular, we highlight unexpected spatially varying properties that emerge even for uniformly functionalized NPs due to the fluctuations of ligands at the nanoscale. We further review the capability of physics-based molecular simulations to reveal these unexpected behaviors, providing powerful computational methods to predict NP properties. Finally, we discuss the opportunity for such simulations to be combined with machine-learning methods to guide the computational design of monolayer-protected NPs prior to synthesis. 

We utilize coarse-grained molecular dynamics simulations and enhanced sampling methods to reveal the effect of ligand branching on the thermodynamics of nanoparticle adsorption to lipid bilayers. 

The interactions of ligand-functionalized nanoparticles with the cell membrane affect cellular uptake, cytotoxicity, and related behaviors, but relating these interactions to ligand properties remains challenging. In this work, we perform coarse-grained molecular dynamics simulations to study how the adsorption of ligand-functionalized cationic gold nanoparticles (NPs) to a single-component lipid bilayer (as a model cell membrane) is influenced by ligand end group lipophilicity. A set of 2-nm diameter NPs, each coated with a monolayer of organic ligands that differ only in their end groups, was simulated to mimic NPs recently studied experimentally. Metadynamics calculations were performed to determine key features of the free energy landscape for adsorption as a function of the distance of the NP from the bilayer and the number of NP-lipid contacts. These simulations revealed that NP adsorption is thermodynamically favorable for all NPs due to the extraction of lipids from the bilayer and into the NP monolayer. To resolve ligand-dependent differences in adsorption behavior, string method calculations were performed to compute minimum free energy pathways for adsorption. These calculations revealed a surprising non-monotonic dependence of the free energy barrier for adsorption on ligand end group lipophilicity. Large free energy barriers are predicted for the least lipophilic end groups because favorable NP-lipid contacts are initiated only through the unfavorable protrusion of lipid tail groups out of the bilayer. The smallest free energy barriers are predicted for end groups of intermediate lipophilicity which promote NP-lipid contacts by intercalating within the bilayer. Unexpectedly, large free energy barriers are also predicted for the most lipophilic end groups which remain sequestered within the ligand monolayer rather than intercalating within the bilayer. These trends are broadly in agreement with past experimental measurements and reveal how subtle variations in ligand lipophilicity dictate adsorption mechanisms and associated kinetics by influencing the interplay of lipid-ligand interactions. 

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. 

Controlling the deposition of polymer-wrapped single-walled carbon nanotubes (s-CNTs) onto functionalized substrates can enable the fabrication of s-CNT arrays for semiconductor devices. In this work, we utilize classical atomistic molecular dynamics (MD) simulations to show that a simple descriptor of solvent structure near silica substrates functionalized by a wide variety of self-assembled monolayers (SAMs) can predict trends in the deposition of s-CNTs from toluene. Free energy calculations and experiments indicate that those SAMs that lead to maximum disruption of solvent structure promote deposition to the greatest extent. These findings are consistent with deposition being driven by solvent-mediated interactions that arise from SAM-solvent interactions, rather than direct s-CNT-SAM interactions, and will permit the rapid computational exploration of potential substrate designs for controlling s-CNT deposition and alignment.