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Considerable effort is directed toward controlling the physicochemical structure of graphene and graphene oxide, but complex structure/property relationships are difficult to identify and utilize when the materials are multifunctional and the properties are correlated. In this study, we propose and demonstrate a workflow for predicting which structural features to use to tune correlated properties simultaneously. Highly accurate multitarget regressors predict the ionization potential and electron affinity of graphene and graphene oxide nanoflakes and report the most important structural features as a basis for ensemble filtering that reflects design decisions. To challenge the approach, multiobjective optimization was used to find filters that simultaneously lower the ionization potential by −0.5 eV and raise the electron affinity by 0.5 eV. We find that the diameter of graphene nanoflakes is the most useful structural feature of graphene but is superseded by the oxygen concentration and proximity to the edges in graphene oxide. Achieving our challenging design goal was not possible, but a significant and balanced shift in the properties (in the right directions) could be obtained and accompanied by improved quality and performance. This general approach could be used to predict filters and to guide experimental design to separate samples for specific applications.

Machine learning is a powerful way of uncovering hidden structure/property relationships in nanoscale materials, and it is tempting to assign structural causes to properties based on feature rankings reported by interpretable models. In this study of defective graphene oxide nanoflakes, we use classification, regression, and causal inference to show that not all important structural features directly influence the concentration of broken bonds, as a representative property. We find that while the presence of oxygen is important for actual bond breakage the presence and distribution of hydrogen determines how often bond breakage occurs.

Although the majority of computational predictions of the properties of diamond nanoparticles (nanodiamonds) are based on sets of exclusively single-crystal structures, most experimental observations contain twins. The influence of twins is difficult to characterize computationally since they are metastable and their relative stability will depend on the thermochemical conditions. The properties of entire samples of nanodiamonds will also depend on how many twins there actually are. In this study, we have used a combination of electronic structure simulations, ab initiothermodynamics, and a simple statistical method called ensemble filtering to investigate the probability of twinning in nanodiamond and their impact on charge transfer properties such as the ionization potential, the electron affinity, and the electronic band gap. We find that, provided some degree of control can be exercised over the surfaces, increasing the number of twinned particles in samples could shift the selectivity of the electron affinity and the band gap and could greatly improve the quality of samples by decreasing the bandwidth, improving the sensitivity and specificity.

Understanding the complicated relationship between various synthetic processing parameters and the functional properties or performance of nanoparticles is one of the goals of computational materials design, and is an ideal problem for materials informatics. In this study, we use machine learning to build predictions of how bulk and surface disorder is controlled by the growth time, atom deposition rate, and temperature, and how they can impact some indicators of the functional properties of platinum nanoparticles used in catalysis, including a range of structural features. We have used an ensemble of 690 unique nanoparticles generated from molecular dynamics trajectories that sample a large variety of different temperature/growth rate combinations, and genetic algorithms providing a prediction accuracy over 85% for all models. We have developed reliable and insightful predictions of structure/property and process/structure relationships (even when the polydispersed structures are almost entirely disordered), but our results suggest that the transcendent process/property relationship requires more detailed descriptors than those traditionally available from conventional computational studies.

Although zinc oxide (ZnO) is a widely studied nanomaterial and a useful photocatalyst, the structures predicted by traditional morphology models, such as the Wulff construction, are largely inconsistent with experimental observations. As well as being scientifically perplexing, this disparity hinders our ability to predict the conditions requires to produce specific ZnO nanostructures on demand. Using density functional theory calculations we compute and compare the surface and edge energies for surfaces of zinc oxide and their intersections, and use a nanomorphology model to predict the thermodynamically optimal shape of zinc oxide nanoparticles and nanowires as a function of size and aspect ratio. We find that edge energies play a significant role in determining the optimal morphology of small nanowires, with hexagonal cross sections preferred for cross sectional areas below 10 nm2and dodecagonal cross sections thermodynamically stable for larger nanostructures.

Silicon nanosheets (or multilayer silicene) are one of the most exciting recent discoveries, being a two-dimensional form of silicon that is less than a nanometer thick, with large lateral dimensions. It has been shown previously that organo-modified silicene can be synthesized with phenyl groups covalently bonded to both sides of the nanosheet, with hydrogen atoms terminating the undercoordinated silicon atoms (J. Am. Chem. Soc.2010, 132, 5946−5947; Phys. Chem. Chem. Phys.2011, 13, 15418–15422). In this work, we use density functional theory calculations and ab initio molecular dynamics (MD) simulations to determine the effect of hydroxyl (OH) group substitution on the phenyl-modified silicene. We show that van der Waals forces need to be included in the simulation to represent the interactions between the groups on the nanosheet. Different positions of the OH groups on the phenyl rings were modeled including ortho-, meta-, and para-substituted positions. The para-substituted position was favored, followed by the meta- then ortho-substituted positions. Our ab initio MD simulations showed that the phenol groups will freely rotate on the nanosheet, aligning so as to form hydrogen bonds between adjacent phenol groups. Such a property may allow the material to be soluble in aqueous solutions, extending its application areas.

Hydrogen, as a surface adsorbate and a bulk impurity, plays an important role in determining the electronic and catalytic properties of zinc oxide, but the nature of the interaction between these two species remains poorly understood. In this work we study the thermodynamics of hydrogen adsorption on zinc oxide’s (1010)) surface. We find that the adsorption of hydrogen on the oxygen sites only, as observed in several previous experiments, is metastable and consider the thermodynamics of exchange of hydrogen between surface and bulk as an explanatory mechanism. We propose that the hydrogen termination of zinc oxide at room temperature is strongly dependent on the hydrogen concentration of the interior and thus that surface reactions involving hydrogen may proceed in an unreliable way depending on the history of the sample, particularly for ZnO in nanostructures.

Understanding the interactions between nanoparticles (NPs) and proteins is essential for the design of bionanotechnology and biomedicine and for delineating the biological implications of nanomaterials for safe nanotechnology. In the present study we have examined protein denaturation in the presence of NPs, using the high-throughput technique of differential scanning fluorometry. Specifically, the melting temperature of human immunoglobulin (IgG) rose from 59.5 to 68.5 °C while that of lysozyme dropped from 74.0 to 68.8 °C for increasing NP:protein molar ratios. This contrast in protein stability was further examined by circular dichroism spectroscopy and Thioflavin T measurements, where a marked increase in β-sheets as well as amyloid fibrillation occurred in lysozyme while small changes were seen in the secondary structure of IgG. Our immunoassays further revealed a greatly elevated cytokine production in the cells treated with fullerol-lysozyme and a mostly unchanged TNF-α secretion in THP-1 cells exposed to fullerol-IgG, suggesting a connection between changes in protein secondary structure induced by fullerol binding and their triggered immune responses. These contrasting effects imply that, due to their finite solubility and size NPs display the duality of both a particle and a chemical and, therefore, do not conform to the conventional role of a ligand in protein stabilization.

Control of the shape and surface termination of semiconductor nanoparticles via control of the fabrication conditions is desirable for many technological applications. We present a theoretical model based on density functional theory first principles thermodynamics for the optimal shape and surface reconstruction of hydrogen-terminated silicon nanoparticles, as a function of nanoparticle size and the temperature and hydrogen partial pressure of the formation conditions. We predict nanoparticle shapes to be dominated by (111) facets under most conditions, with fully octohedral shapes available for higher H2chemical potential and (113) and (001) facets appearing as H2chemical potential decreases. Comparison to previously published nanoparticle shapes reveals agreement between the observed and predicted nanoparticle shapes for the reported chemical conditions. Control of the hydrogen chemical potential should allow nanoparticles with variable fractions of (100) and (113) surfaces to be created, with potential applications for functionalization or control of optoelectronic properties.

The stability of finite-sized graphene nanostructures under different conditions is contingent on the stability of the highly reactive undercoordinated atoms around the circumference. Chemical passivation is a convenient way of stabilizing the edges and corners, provided the conditions support adsorption. In this paper we examine the stabilities of hydrogen, oxygen, hydroxyl, and water functionalization of the edges of graphene nanoflakes using a combination of ab initio thermodynamics and self-consistent charge density functional tight-binding simulations. We find that the adsorption of hydrogen and oxygen on graphene nanoflakes is selective and sensitive to the edge and corner structure and the local environment. Under hydrogen-rich conditions (or in vacuo), we find that armchair edges are preferred, whereas under oxygen-rich conditions the stability of the zigzag edge is enhanced. In addition to this, we find that the types of corners play an important role, and the presence of acute 60° corners is thermodynamically preferred, particularly under humid conditions. In each case, the adsorption efficiency is related to the temperature and the pressure, which may be used to stabilize one type of structure or another.

Hematite (α-Fe2O3) nanoparticles are typically synthesized, stored, or used in hydrous environments, and the mineral/water interfaces are important for the surface stability and reactivity of these nanoparticles. Under such conditions the exposed facets are often passivated by hydroxyl groups. The configurations of surface hydroxylation vary with environmental conditions and affect the morphology and surface chemistry. Among the low-index hematite surfaces, the {100} are the only nonpolar surfaces and are often present on nanorods or nanotubes elongated along the [001] direction. In this paper we explore the relaxation and hydroxylation of this surface using first principles thermodynamics. Our results reveal that depending on the supersaturation of water and oxygen, various extents of hydroxylation may appear. In humid or hydrous environments, undercoordinated subsurface oxygen atoms are hydrogenated. In water singly and doubly coordinated hydroxyl groups coexist with chemisorbed water molecules at the surfaces. In environments where the humidity is reduced, the surface is terminated exclusively by doubly coordinated hydroxyl groups. The clean surface occurs when the humidity is further reduced or when temperature is elevated. On the basis of these findings, we have constructed the surface phase diagrams to describe the thermodynamic stability for two different temperatures. The phase diagrams enable us to predict the density and type of hydroxylation, which is relevant to surface stability, reactivity, and catalytic properties in hydrous or humid environments.

Knowledge about the surface functionalization of nanodiamonds is of great importance for nanomedical and nanobiological applications. In this paper, we explore the relative stability of the nanodiamond passivated by amidogen (NH2), imidogen (NH), and nitrogen (N) in N2+ H2and ammonia environments, using density functional tight binding simulations over a range of temperatures. The N-adsorption is found to be energetically unfavorable on the nanodiamond surfaces up to 1500 K. In contrast, adsorption of NH and NH2is found to be thermodynamically stable at least around room temperature. We also compare the possibility of anisotropic (facet-dependent) adsorption and find that {100} facets of the nanodiamond are more preferred for the NH adsorption in the entire temperature range considered but are only favored at high temperature for the NH2adsorption. We also show that this chemical functionalization is more stable in the N2+ H2environment than in the ammonia environment, but its stability depends on the size, shape, and morphology of the nanodiamond.

Following recent reports of the successful low-temperature synthesis of high quality and single-crystal CdS quantum nanostructures, attention has turned toward identifying the critical factors affecting the preferred shape and surface structure of these important nanomaterials. Presented here are the results of first principles calculations, examining the surface structure, surface energy, and isotropic surface stress of clean reconstructed CdS low index surfaces. These results have been used as input for a shape-dependent thermodynamic model to determine the equilibrium shapes and aspect ratio of CdS nanostructures over a range of experimentally relevant sizes. The predicted shape is also directly compared with experimental results for CdS nanorods prepared using hydrothermal synthesis methods.