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Unpassivated diamond nanoparticles (bucky-diamonds) exhibit a unique surface reconstruction involving graphitization of certain crystal facets, giving rise to hybrid core-shell particles containing both aromatic and aliphatic carbon. Considerable effort is directed toward eliminating the aromatic shell, but persistent graphitization of subsequent subsurface-layers makes perdurable purification a challenge. In this study we use some simple statistical methods, in combination with electronic structure simulations, to predict the impact of different fractions of aromatic and aliphatic carbon on the charge transfer properties of the ensembles of bucky-diamonds. By predicting quality factors for a variety of cases, we find that perfect purification is not necessary to preserve selectivity, and there is a clear motivation for purifying samples to improve the sensitivity of charge transfer reactions. This may prove useful in designing drug delivery systems where the release of (selected) drugs needs to be sensitive to specific conditions at the point of delivery.

Nanodiamond is a promising material for biological and medical applications, owning to its relatively inexpensive and large-scale synthesis, unique structure, and superior optical properties. However, most biomedical applications, such as drug delivery and bio-imaging, are dependent upon the precise control of the surfaces, and can be significantly affected by the type, distribution and stability of chemical funtionalisations of the nanodiamond surface. In this paper, recent studies on nanodiamonds and their biomedical applications by conjugating with different chemicals are reviewed, while highlighting the critical importance of surface chemical states for various applications.

Carbon-based hydrogen storage materials are one of hottest research topics in materials science. Although the majority of studies focus on highly porous loosely bound systems, these systems have various limitations including use at elevated temperature. Here we propose, based on computer simulations, that diamond nanoparticles may provide a new promising high temperature candidate with a moderate storage capacity, but good potential for recyclability. The hydrogenation of nanodiamonds is found to be easily achieved, in agreement with experiments, though we find the stability of hydrogenation is dependent on the morphology of nanodiamonds and surrounding environment. Hydrogenation is thermodynamically favourable even at high temperature in pure hydrogen, ammonia, and methane gas reservoirs, whereas water vapour can help to reduce the energy barrier for desorption. The greatest challenge in using this material is the breaking of the strong covalent C-H bonds, and we have identified that the spontaneous release of atomic hydrogen may be achieved through charging of hydrogenated nanodiamonds. If the degree of induced charge is properly controlled, the integrity of the host nanodiamond is maintained, which indicates that an efficient and recyclable approach for hydrogen release may be possible., (This journal is © The Royal Society of Chemistry 2012)

Understanding nanodiamond functionalisation is of great importance for biological and medical applications. Here we examine the stabilities of oxygen, hydroxyl, and water functionalisation of the nanodiamonds using the self-consistent charge density functional tight-binding simulations. We find that the oxygen and hydroxyl termination are thermodynamically favourable and form strong C–O covalent bonds on the nanodiamond surface in an O2 and H2 gas reservoir, which confirms previous experiments. Yet, the thermodynamic stabilities of oxygen and hydroxyl functionalisation decrease dramatically in a water vapour reservoir. In contrast, H2O molecules are found to be physically adsorbed on the nanodiamond surface, and forced chemical adsorption results in decomposition of H2O. Moreover, the functionalisation efficiency is found to be facet dependent. The oxygen functionalisation prefers the {100} facets as opposed to alternative facets in an O2 and H2 gas reservoir. The hydroxyl functionalisation favors the {111} surfaces in an O2 and H2 reservoir and the {100} facets in a water vapour reservoir, respectively. This facet selectivity is found to be largely dependent upon the environmental temperature, chemical reservoir, and morphology of the nanodiamonds.

In this work, we have combined constant-pH molecular dynamics simulations and experiments to provide a quantitative analysis of pH dependent interactions between doxorubicin hydrochloride (DOX) cancer therapeutic and faceted nanodiamond (ND) nanoparticle carriers. Our study suggests that when a mixture of faceted ND and DOX is dissolved in a solvent, the pH of this solvent plays a controlling role in the adsorption of DOX molecules on the ND. We find that the binding of DOX molecules on ND occurs only at high pH and requires at least ∼10% of ND surface area to be fully titrated for binding to occur. As such, this study reveals important mechanistic insight underlying an ND-based pH-controlled therapeutic platform.

Machine learning can extract complex structure/property relationships but is often insufficient to explain how to control or tune the properties of materials, particularly when they are multi‐functional. This study demonstrates the value of combining multi‐target regression and multi‐target causal graphs to address the need to simultaneously control multiple properties of nanomaterials, and the need to translate these relationships into actionable insights. Using nanodiamonds as an exemplar, recursive feature elimination is first used to identify nine structural features that allow simultaneous prediction of their electron charge transfer properties and thermochemical stability to high accuracy by an interpretable random forest regressor. A multi‐target Bayesian network with domain knowledge incorporated via interactive learning using a hill‐climbing algorithm then determines how these important structural features of nanodiamonds relate to their functional properties, proposing causal paths that can be used to inform experimental design. [ABSTRACT FROM AUTHOR]

The functionality of many nanomaterials involves the collective properties of aggregates and self‐assembled superstructures. Using the mesostructure as a design parameter requires predictive capabilities, but this is challenging when nanoparticle samples present a diverse mixture of shapes and surface facets. Molecular dynamics with over 5000 nanoparticles is used here to simulate different mixtures of polyhedral nanodiamonds, with surfaces indicative of their characteristic multipolar surface electrostatic potential. It is found that polydispersed samples have different aggregation behavior than monodispersed samples, particularly in regard to the interparticle coordination and degree of long‐range order, but mixing nanoparticle shapes does not affect aggregate porosity. [ABSTRACT FROM AUTHOR]

While significant progress has been made toward production of monodispersed samples of a variety of nanoparticles, in cases such as diamond nanoparticles (nanodiamonds) a significant degree of polydispersivity persists, so scaling-up of laboratory applications to industrial levels has its challenges. In many cases, however, monodispersivity is not essential for reliable application, provided that the inevitable uncertainties are just as predictable as the functional properties. As computational methods of materials design are becoming more widespread, there is a growing need for robust methods for modeling ensembles of nanoparticles, that capture the structural complexity characteristic of real specimens. In this paper we present a simple statistical approach to modeling of ensembles of nanoparticles, and apply it to nanodiamond, based on sets of individual simulations that have been carefully selected to describe specific structural sources that are responsible for scattering of fundamental properties, and that are typically difficult to eliminate experimentally. For the purposes of demonstration we show how scattering in the Fermi energy and the electronic band gap are related to different structural variations (sources), and how these results can be combined strategically to yield statistically significant predictions of the properties of an entire ensemble of nanodiamonds, rather than merely one individual 'model' particle or a non-representative sub-set. [ABSTRACT FROM AUTHOR]