Coarse-grained models of tethers for fast self-assembly simulations

Deep learning potential of mean force between polymer grafted nanoparticles

Grafting polymer chains on the surfaces of nanoparticles is a well-known route to control their self-assembly and distribution in a polymer matrix. A wide variety of self-assembled structures are achieved by changing the grafting patterns on the surface of an individual nanoparticle. However, an accurate estimation of the effective potential of mean force between a pair of grafted nanoparticles that determines their assembly and distribution in a polymer matrix is an outstanding challenge in nanoscience. We address this problem via deep learning. As a proof of concept, here we report a deep learning framework that learns the interaction between a pair of single-chain grafted spherical nanoparticles from their molecular dynamics trajectory. Subsequently, we carry out the deep learning potential of mean force-based molecular simulation that predicts the self-assembly of a large number of single-chain grafted nanoparticles into various anisotropic superstructures, including percolating networks and bilayers depending on the nanoparticle concentration in three-dimensions. The deep learning potential of mean force-predicted self-assembled superstructures are consistent with the actual superstructures of single-chain polymer grafted spherical nanoparticles. This deep learning framework is very generic and extensible to more complex systems including multiple-chain grafted nanoparticles. We expect that this deep learning approach will accelerate the characterization and prediction of the self-assembly and phase behaviour of polymer-grafted and unfunctionalized nanoparticles in free space or a polymer matrix.

Routes to the preparation of mixed monolayers of fluorinated and hydrogenated alkanethiolates grafted on the surface of gold nanoparticles

The use of binary blends of hydrogenated and fluorinated alkanethiolates represents an interesting approach to the construction of anisotropic hybrid organic-inorganic nanoparticles since the fluorinated and hydrogenated components are expected to self-sort on the nanoparticle surface because of their reciprocal phobicity. These mixed monolayers are therefore strongly non-ideal binary systems. The synthetic routes we explored to achieve mixed monolayer gold nanoparticles displaying hydrogenated and fluorinated ligands clearly show that the final monolayer composition is a non-linear function of the initial reaction mixture. Our data suggest that, under certain geometrical constraints, nucleation and growth of fluorinated domains could be the initial event in the formation of these mixed monolayers. The onset of domain formation depends on the structure of the fluorinated and hydrogenated species. The solubility of the mixed monolayer nanoparticles displayed a marked discontinuity as a function of the monolayer composition. When the fluorinated component content is small, the nanoparticle systems are fully soluble in chloroform, at intermediate content the nanoparticles become soluble in hexane and eventually they become soluble in fluorinated solvents only. The ranges of monolayer compositions in which the solubility transitions are observed depend on the nature of the thiols composing the monolayer.

Nonequilibrium structure in sequential assembly

The assembly of monomeric constituents into molecular superstructures through sequential-arrival processes has been simulated and theoretically characterized. When the energetic interactions allow for complete overlap of the particles, the model is equivalent to that of the sequential absorption of soft particles on a surface. In the present work, we consider more general cases by including arbitrary aggregating geometries and varying prescriptions of the connectivity network. The resulting theory accounts for the evolution and final-state configurations through a system of equations governing structural generation. We find that particle geometries differ significantly from those in equilibrium. In particular, variations of structural rigidity and morphology tune particle energetics and result in significant variation in the nonequilibrium distributions of the assembly in comparison to the corresponding equilibrium case.

Geometric curvature controls the chemical patchiness and self-assembly of nanoparticles

When organic molecules are tethered onto non-spherical nanoparticles, their chemical properties depend on the particles' local curvature and shape. Based on this observation, we show here that it is possible to engineer chemical patchiness across the surface of a non-spherical nanoparticle using a single chemical species. In particular, when acidic ligands are used, regions of the particle surface with different curvature become charged at different pH values of the surrounding solution. This interplay between particle shape and local electrostatics allows for fine control over nanoscale self-assembly leading to structures with varying degrees of complexity. These structures range from particle cross-stacks to open-lattice crystals, the latter with pore sizes on the order of tens of nanometres, that is, at the lower synthetic limits of metallic mesoporous materials.

Design of patchy particles using quaternary self-assembled monolayers

Binary and ternary self-assembled monolayers (SAMs) adsorbed on gold nanoparticles (NPs) have been previously studied for their propensity to form novel and unexpected patterns. The patterns found were shown to arise from a competition between immiscibilty of unlike surfactants and entropic gains due to length or other architectural differences between them. We investigate patterns self-assembled from quaternary monolayers on spherical nanoparticles. We perform simulations to study the effect of NP radius, degree of immiscibility between surfactants, length differences, and stoichiometry of the SAM on the formation of patterns. We report patterns analogous to binary and ternary cases, as well as some novel patterns specific to quaternary SAMs.

Facetted patchy particles through entropy-driven patterning of mixed ligand SAMS

We present a microscopic theory that describes the ordering of two distinct ligands on the surface of a facetted nanoparticle. The theory predicts that when one type of ligand is significantly bulkier than all others, the larger ligands preferentially align themselves along the edges and vertices of the nanoparticle. Monte Carlo simulations confirm these predictions. We show that the intrinsic conformational entropy of the ligands stabilizes this novel edge-aligned phase.

Striped nanowires and nanorods from mixed SAMS

We investigate the use of mixed self-assembled monolayers (SAMs) for creating nanoscale striped patterns on nanowires and nanorods. Our simulations predict that SAMs comprised of an equal composition of length-mismatched, thermodynamically incompatible surfactants adsorbed on nanowire surfaces self-organize into equilibrium stripes of alternating composition always perpendicular, rather than parallel, to the nanowire axis. We support the simulation results with preliminary experimental investigations of gold nanorods coated with binary mixtures of ligand molecules, which show stripes roughly perpendicular to the rod axis in all cases.