Free Thiols Regulate the Interactions and Self-Assembly of Thiol-Passivated Metal Nanoparticles.

Thiol ligands bound to the metallic core of nanoparticles determine their interactions with the environment and self-assembly. Recent studies suggest that equilibrium between bound and free thiols alters the ligand coverage of the core. Here, X-ray scattering and MD simulations investigate water-supported monolayers of gold-core nanoparticles as a function of the core-ligand coverage that is varied in experiments by adjusting the concentration of total thiols (sum of free and bound thiols). Simulations demonstrate that the presence of free thiols produces a nearly symmetrical coating of ligands on the core. X-ray measurements show that above a critical value of core-ligand coverage the nanoparticle core rises above the water surface, the edge-to-edge distance between neighboring nanoparticles increases, and the nanoparticle coverage of the surface decreases. These results demonstrate the important role of free thiols: they regulate the organization of bound thiols on the core and the interactions of nanoparticles with their surroundings.

The influence of fractional surface coverage on the core-core separation in ordered monolayers of thiol-ligated Au nanoparticles.

We report the results of grazing incidence X-ray diffraction (GIXD) measurements from water supported Langmuir monolayers of gold nanoparticles ligated with dodecanethiol (12 carbons), tetradecanethiol (14 carbons), hexadecanethiol (16 carbons), and octadecanethiol (18 carbons). These monolayers are formed from solutions with varying concentrations of the respective thiols. We show that equilibrium between adsorbed thiol molecules and the thiols in the bulk solution implies fractional coverage of the Au nanoparticle core. We also show that the nanoparticle-nanoparticle separation and the correlation length of particles in these ordered films increases with thiol concentration in the parent solution, and that excess thiol can be found in the space between particles as well as in islands away from the particles. Using the equilibrium constant relating ligand solution concentration and nanoparticle surface coverage of the gold core by the ligand molecules, we interpret the way in which varying thiol concentration affects the nanoparticle-nanoparticle separation as a function of surface coverage of the gold core by the ligand molecules.

The role of ligands in the mechanical properties of Langmuir nanoparticle films.

Langmuir monolayers of ligand-capped inorganic nanoparticles exhibit rich morphologies under lateral compression such as wrinkling, folding, and multilayer nucleation. We demonstrate that the ligands play a crucial role in the mechanical properties of nanoparticle films by probing the morphology and anisotropic stress response during lateral compression of films with systematically varied ligand concentrations. Increasing the ligand concentration of the films past a threshold value inhibits monolayer wrinkling and folding in favor of multilayer formation, and sharply reduces the compressive and shear moduli. We attribute these drastic mechanical effects to modifications to the ligand interactions between adjacent particles as well as to two-dimensional crystalline structure changes occurring on the scale of tens of particles, as determined by transmission electron microscopy.

Accelerating the prediction of stable materials with machine learning.

Despite the rise in computing power, the large space of possible combinations of elements and crystal structure types makes large-scale high-throughput surveys of stable materials prohibitively expensive, especially for complex materials and materials subject to environmental conditions such as finite temperature. When physics-based computational methods and labor-intensive experiments are not feasible, machine learning (ML) methods can be a rapid and powerful alternative. Owing to a wealth of experimental and first-principles data as well as improved ML frameworks designed for materials modeling, ML is shown to be effective in predicting stability parameters and accelerating the discovery of new stable materials. In this Review, we summarize the most recent advancements in applying ML methodologies in predicting materials stability, focusing particularly on predictions of zero- and finite-temperature stability. We also highlight the need for more ML development in predictions of other thermodynamic knobs, such as pressure and surface/interfacial energy, which practically impact materials stability.