Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations.

Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3-5 nm diamond nanoparticles synthesized through detonation process, have attracted great interest for drug delivery due to their colloidal stability in water and their biocompatibility. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication, with their aggregate formation poorly understood. Here, we present a novel characterization method of combining machine learning with direct cryo-transmission electron microscopy imaging to characterize the unique colloidal behavior of DNDs. Together with small-angle X-ray scattering and mesoscale simulations we show and explain the clear differences in the aggregation behavior between positively and negatively charged DNDs. Our new method can be applied to other complex particle systems, which builds essential knowledge for the safe implementation of nanoparticles in drug delivery.

The pure and representative types of disordered platinum nanoparticles from machine learning.

The development of interpretable structure/property relationships is a cornerstone of nanoscience, but can be challenging when the structural diversity and complexity exceeds our ability to characterise it. This is often the case for imperfect, disordered and amorphous nanoparticles, where even the nomenclature can be unspecific. Disordered platinum nanoparticles have exhibited superior performance for some reactions, which makes a systematic way of describing them highly desirable. In this study we have used a diverse set of disorder platinum nanoparticles and machine learning to identify the pure and representative structures based on their similarity in 121 dimensions. We identify two prototypes that are representative of separable classes, and seven archetypes that are the pure structures on the convex hull with which all other possibilities can be described. Together these nine nanoparticles can explain all of the variance in the set, and can be described as either single crystal, twinned, spherical or branched; with or without roughened surfaces. This forms a robust sub-set of platinum nanoparticle upon which to base further work, and provides a theoretical basis for discussing structure/property relationships of platinum nanoparticles that are not geometrically ideal.

Simulating facet-dependent aggregation and assembly of distributions of polyhedral nanoparticles.

Coarse-grained molecular dynamics simulations of diamond nanoparticles were performed to investigate the effects of size polydispersity on three polyhedral shapes chosen to span a diverse space of surface interactions. It was found that the resulting self-assembly was size dependent as the simulations were quenched, with the largest nanoparticles providing a clustered scaffold for subsequent smaller nanoparticle assembly. Additionally, facet-facet interactions were dominated by the {111} surface and the resulting aggregate was dominated by meso-sized porosity for monodisperse systems, broadening to larger diameters for polydisperse systems.

Dynamic self-assembly of detonation nanodiamond in water.

Nanodiamonds are increasingly used in many areas of science and technology, yet, their colloidal properties remain poorly understood. Here we use direct imaging as well as light and X-ray scattering reveal that purified detonation nanodiamond (DND) particles in an aqueous environment exhibit a self-assembled lace-like network, even without additional surface modification. Such behaviour is previously unknown and contradicts the current consensus that DND exists as mono-dispersed single particles. With the aid of mesoscale simulations, we show that the lace network is likely the result of competition between a short-ranged electrostatic attraction between faceted particles and a longer-ranged repulsion arising from the interaction between the surface functional groups and the surrounding water molecules which prevents complete flocculation. Our findings have significant implications for applications of DND where control of the aggregation behaviour is critical to performance.

Correlating anisotropy and disorder with the surface structure of platinum nanoparticles.

Due to the competition between numerous physicochemical variables during formation and processing, platinum nanocatalysts typically contain a mixture of shapes, distributions of sizes, and a considerable degree of surface imperfection. Structural imperfection and sample polydispersivity are inevitable at scale, but accepting bulk and surface diversity as legitimate design features provides new opportunities for nanoparticle design. In recent years disorder and anisotropy have been embraced as useful design parameters but predicting the impact of uncontrollable imperfection a priori is challenging. In the present work we have created an ensemble of uniquely imperfect nanoparticles extracted from classical molecular dynamics trajectories and applied statistical filters to restrict the ensemble in ways that reflect common industrial design principles. We find that targeting different sizes and size distributions may be an effective way of promoting or suppressing internal disorder or crystallinity (as required), but the degree of anisotropy of the particle as a whole has a greater impact on the population of different types of surface ordering and active sites. These results indicate that tuning of disordered and anisotropic Pt nanoparticles is possible, but it is not as straightforward as geometrically ideal nanoparticles with a high degree of crystallinity. It is unlikely that a synthesis strategy could eliminate this diversity entirely, or ensure this type of structural complexity does not develop post-synthesis under operational conditions, but it may be possible to bias the formation of specific bulk structures and the surface anisotropy.

On reverse Monte Carlo constraints and model reproduction.

Reverse Monte Carlo (RMC) simulations were performed to investigate the effectiveness of any combination of five experimentally motivated constraints on the reproduction of a test case, a ternary ab initio model. It was found that low energy structures fitting a variety of constraints commonly used in the RMC methodology could still provide an incorrect description of the chemical structural unit populations in multi-elemental systems. It is shown that the use of an elemental bond type constraint is an effective way to avoid this. © 2017 Wiley Periodicals, Inc.

Predicting the role of seed morphology in the evolution of anisotropic nanocatalysts.

Controlling the structure of nanocrystals is an effective way to tune their properties and improve performance in a wide variety of applications. However, the atomic pathways for achieving this goal are difficult to identify and exercise, due to competing kinetic and thermodynamic influences during formation. In particular, an understanding of how symmetry, and symmetry breaking, determine nanocrystal morphology would significantly advance our ability to produce nanomaterials with prescribed functions. In this study we present results of a detailed computational study into the atomic structure of platinum nanoparticles at early growth stages of formation, as a function of temperature and atomic deposition rates. We investigate the impact of different types of crystalline seeds and characterize the emergent structures via simulated High Resolution Transmission Electron Microscopy (HRTEM) images. We find that the choice of initial seed is an important driver for symmetry breaking, due to a combination of atomic deposition and etching on different seed facets. A mix of low index facets causes the formation of important surface defects, in addition to the absorption/adsorption of single atoms, which can be correlated with different catalytic reactions as the process perpetuates. These findings provide new insights into nanocrystal shape-control mechanisms and suggest new opportunities for future design of this important class of nanomaterials.

Ab Initio Comparison of Bonding Environments and Threshold Behavior in Ge(x)As10Se(90-x) and Ge(x)Sb10Se(90-x) Glass Models.

Ab initio models of Ge(x)As10Se(90-x), and Ge(x)Sb10Se(90-x) glasses are constructed, and their bonding environments are characterized and compared against each other and to recent experimental studies of equivalent glasses at the same stoichiometry and density. A minimum in the linear refractive index is found to correlate with a maximum in the number of length-one, predominantly Se, atomic chains for both glass types. The threshold behavior difference between GeAsSe and GeSbSe is shown to be due to the appearance of As-As-Se2 structural units beyond the MCN = 2.67 threshold in the GeAsSe glasses.

Structural modeling of Ge6.25As32.5Se61.25 using a combination of reverse Monte Carlo and Ab initio molecular dynamics.

Ternary glass structures are notoriously difficult to model accurately, and yet prevalent in several modern endeavors. Here, a novel combination of Reverse Monte Carlo (RMC) modeling and ab initio molecular dynamics (MD) is presented, rendering these complicated structures computationally tractable. A case study (Ge6.25As32.5Se61.25 glass) illustrates the effects of ab initio MD quench rates and equilibration temperatures, and the combined approach's efficacy over standard RMC or random insertion methods. Submelting point MD quenches achieve the most stable, realistic models, agreeing with both experimental and fully ab initio results. The simple approach of RMC followed by ab initio geometry optimization provides similar quality to the RMC-MD combination, for far fewer resources.

Bonding trends within ternary isocoordinate chalcogenide glasses Ge(x)As(y)Se(1-x-y).

A structural study is presented of ab initio molecular dynamics simulations of Ge-As-Se chalcogenide glasses performed at the same mean coordination number but differing stoichiometry ranging between Se rich and Se poor glasses. Starting configurations are generated via Reverse Monte Carlo (RMC) simulations of Extended X-ray Absorption Fine Structure (EXAFS) measurements of experimental samples. Structural analysis is presented illustrating the bonding trends found with changing stoichiometry.

Modeling the crystallization of gold nanoclusters-the effect of the potential energy function.

The crystallization dynamics of 5083 atom gold nanoclusters, which were quenched from the melt, were studied by molecular dynamics (MD) using the EAM 'Glue' and 'Force-matched' potentials to compare and contrast how the crystallization dynamics is affected by these potential energy functions. MD simulations from each potential showed the formation of gold nanoclusters of icosahedral morphology during the quenching process, which is in good agreement with the experimental studies of gold nanoclusters formed under vacuum. The effect of the potential on the evolution of cluster (surface and interior) morphology during the crystallization process is discussed.