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Metal-organic frameworks (MOFs) are nanoporous materials with exceptional host-guest properties poised for groundbreaking innovations in gas separation applications according to high-throughput (HT) screening data. However, MOF structural libraries are nearly infinite in practice and so statistical and information technology will play a fundamental role in implementing and rationalizing MOF virtual screening. In this work, we apply k-means clustering and archetypal analysis (AA) to identify the truly significant nanoporous structures in a large library of ~82000 virtual MOFs. Quantitative structure-property relationship (QSPR) models of the theoretical CO2 and N2 uptake capacities were also developed using a calibration set of ~16000 hypothetical MOF structures derived from the prototypes and archetype frameworks. Since uptake capacities correlated poorly to the void fraction, surface area and pore size but these properties were used to build binary classifier predictors that successfully identify "high-performing" nanoporous materials in an external test set of ~65000 MOFs with accuracy higher than 94%. The accuracy of the classification decreased for MOFs with fluorine substituents. The classification models can serve as efficient filtering tools to detecting promising high-performing candidates at the early stage of virtual high-throughput screening of novel porous materials. [ABSTRACT FROM AUTHOR]

Thefabrication of silicon nanoparticles terminated with halogenspecies provides a convenient route to create readily functionalizablenanostructures; however, the relationship between formation conditionsand the thermodynamic ground-state morphology of the nanoparticlesthus formed remains poorly understood. In this work, we use densityfunctional theory calculations to compute surface energies of siliconsurfaces terminated with fluorine, chlorine, bromine, and iodine asa function of halogen chemical potential and hence we compute, viaa nanomorphology model, the thermodynamically optimal morphology of halogen-terminated silicon nanoparticles. We predict a variety of optimal nanoparticle shapes consisting primarily of Si(100), Si(113),and Si(111) facets with varying terminations, and we demonstrate howcontrol over morphology may be attained by controlling the chemicalenvironment. Implications for the fabrication of nanoparticles withfacet-selective reactivity are also discussed. [ABSTRACT FROM AUTHOR]

Self-assembledmonolayers (SAMs) are applied to gold nanoparticlesto guide growth, increase stability, and provide additional functionality.However, as with any surface coating, the changes to the energeticsof the surface can have unanticipated consequences, as well as introducingnew opportunities. Understanding the relationship between the efficiencyand type of SAMs and the underlying gold nanoparticles is challenging,but can be explored very efficiently using theory and computer simulation.In this paper, the relationship between the density of alkanethiolSAMs with different chain lengths and the shape of gold nanoparticlesis reported as a function of size, including the impact of highlyunder-coordinated sites such as edges and corners. The results showthat the most favorable sites for adsorption are on the {111} facetsadjacent to the {111}|{111} edges (followed by the corners), but itis the concentration of SAMs on facets themselves that drives variationsin the morphology. With the right choice of thiol and control of theSAM efficiency, it will be possible to create gold nanoparticles witha majority of either {111} or {100} facets, as required. [ABSTRACT FROM AUTHOR]

Under a given set of conditions, nanomaterials can crystallize into structures that are entirely inconsistent with the bulk material and may adopt a range of faceted morphologies that depend on the particle size. A size-dependent phase diagram, a graphical representation of the chemical equilibrium, offers a convenient way to describe this relationship among the size, morphology, and thermodynamic environment. Although creating such a diagram from conventional experiments is extremely challenging (and costly), theory and simulation allow us to use virtual experiments to control the temperature, pressure, size, structure and composition independently. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Metal-organic frameworks (MOFs) are nanoporous materials with exceptional host-guest properties poised for groundbreaking innovations in gas separation applications according to high-throughput (HT) screening data. However, MOF structural libraries are nearly infinite in practice and so statistical and information technology will play a fundamental role in implementing and rationalizing MOF virtual screening. In this work, we apply k-means clustering and archetypal analysis (AA) to identify the truly significant nanoporous structures in a large library of ∼82 000 virtual MOFs. Quantitative structure-property relationship (QSPR) models of the theoretical CO2 and N2 uptake capacities were also developed using a calibration set of ∼16 000 hypothetical MOF structures derived from the prototypes and archetype frameworks. Since uptake capacities correlated poorly to the void fraction, surface area and pore size but these properties were used to build binary classifier predictors that successfully identify "high-performing" nanoporous materials in an external test set of ∼65 000 MOFs with accuracy higher than 94%. The accuracy of the classification decreased for MOFs with fluorine substituents. The classification models can serve as efficient filtering tools to detecting promising high-performing candidates at the early stage of virtual high-throughput screening of novel porous materials.

The intrinsic relationships between nanoscale features and electronic properties of nanomaterials remain poorly investigated. In this work, electronic properties of 622 computationally optimized graphene structures were mapped to their structures using partial-least-squares regression and radial distributions function (RDF) scores. Quantitative structure-property relationship (QSPR) models were calibrated with 70% of a virtual data set of 622 passivated and nonpassivated graphenes, and we predicted the properties of the remaining 30% of the structures. The analysis of the optimum QSPR models revealed that the most relevant RDF scores appear at interatomic distances in the range of 2.0 to 10.0 Å for the energy of the Fermi level and the electron affinity, while the electronic band gap and the ionization potential correlate to RDF scores in a wider range from 3.0 to 30.0 Å. The predictions were more accurate for the energy of the Fermi level and the ionization potential, with more than 83% of explained data variance, while the electron affinity exhibits a value of ∼80% and the energy of the band gap a lower 70%. QSPR models have tremendous potential to rapidly identify hypothetical nanomaterials with desired electronic properties that could be experimentally prepared in the near future.

High-throughput (HT) computational characterization of nanomaterials is poised to accelerate novel material breakthroughs. The number of possible nanomaterials is increasing exponentially along with their complexity, and so statistical and information technology will play a fundamental role in rationalizing nanomaterials HT data. We demonstrate that multivariate statistical analysis of heterogeneous ensembles can identify the truly significant nanoparticles and their most relevant properties. Virtual samples of diamond nanoparticles and graphene nanoflakes are characterized using clustering and archetypal analysis, where we find that saturated particles are defined by their geometry, while nonsaturated nanoparticles are defined by their carbon chemistry. At the complex hull of the nanostructure spaces, a combination of complex archetypes can efficiency describe a large number of members of the ensembles, whereas the regular shapes that are typically assumed to be representative can only describe a small set of the most regular morphologies. This approach provides a route toward the characterization of computationally intractable virtual nanomaterial spaces, which can aid nanomaterials discovery in the foreseen big data scenario.

Innovations in computational nanoscience have traditionally come in conjunction with experimental innovations, but uncertainty often surrounds the trustworthiness of in silico studies. While the accuracy of simulations has been improving every year, considerably less attention has focused on dealing with increasing complexity, which may be the source of concern. Creating more realistic virtual experiments (without sacrificing theoretical and numerical accuracy) remains challenging, particularly when we are confronted with the polydispersivity characteristic of extra silico samples. Fortunately, there are various theoretical methods that can be used in conjunction with first-principles simulations, not the least of which are the statistical tools and techniques promised by the emerging fields of materials informatics and data-driven sciences.

Porous platinum nanoparticles provide a route for the development of catalysts that use less platinum without sacrificing catalytic performance. Here, we examine porous platinum nanoparticles using a combination of in situ transmission electron microscopy and calculations based on a first-principles-parametrized thermodynamic model. Our experimental observations show that the initially irregular morphologies of the as-sythesized porous nanoparticles undergo changes at high temperatures to morphologies having faceted external surfaces with voids present in the interior of the particles. The increasing size of stable voids with increasing temperature, as predicted by the theoretical calculations, shows excellent agreement with the experimental findings. The results indicate that hollow-structured nanoparticles with an appropriate void-to-total-volume ratio can be stable at high temperatures.

Although unpassivated detonation nanodiamonds are known to form tightly bound (and sometimes ordered) superstructures, in most high performance applications the surface are deliberately functionalized, and this can profoundly alter the aggregation behavior. In the present study, we model the aggregation of functionalized nanodiamonds and show that functionalization greatly reduces the Coulombic interactions characteristic of unsaturated particles. Our results provide new insights into the interactions of functionalized nanoparticles.

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.

Accurate understanding of the structure of active sites is fundamentally important in predicting catalytic properties of heterogeneous nanocatalysts. We present an accurate determination of both experimental and theoretical atomic structures of surface monatomic steps on industrial platinum nanoparticles. This comparison reveals that the edges of nanoparticles can significantly alter the atomic positions of monatomic steps in their proximity, which can lead to substantial deviations in the catalytic properties compared with the extended surfaces.

Fluorescent defects in noncytotoxic diamond nanoparticles are candidates for qubits in quantum computing, optical labels in biomedical imaging, and sensors in magnetometry. For each application these defects need to be optically and thermodynamically stable and included in individual particles at suitable concentrations (singly or in large numbers). In this Letter, we combine simulations, theory, and experiment to provide the first comprehensive and generic prediction of the size, temperature, and nitrogen-concentration-dependent stability of optically active N-V defects in nanodiamonds.

The development of the next generation of nanotechnologies requires precise control of the size, shape, and structure of individual components in a variety of chemical and engineering environments. This includes synthesis, storage, operational environments and, since these products will ultimately be discarded, their interaction with natural ecosystems. Much of the important information that determines these properties is contained within nanoscale phase diagrams, but quantitative phase maps that include surface effects and critical diameter (along with temperature and pressure) remain elusive. Here we present the first quantitative equilibrium phase map for gold nanoparticles together with experimental verification, based on relativistic ab initio thermodynamics and in situ high-resolution electron microscopy at elevated temperatures.

The incorporation of more experimentally relevant parameters into theoretical descriptions of nanomaterials is important for our understanding of the stability of nanostructures in different chemical environments. Using a size-, shape-, and temperature-dependent thermodynamic model we have generated the first phase map for anatase and rutile nanocrystals, that includes both the equilibrium shape and the affects of surface chemistry. The calculated phase map indicates that the equilibrium boundary between anatase and rutile nanocrystals is surface charge chemistry dependent, which relates to both their formation and postsynthesis environments.