Search

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 We find that the diameter of graphene nanoflakes is the most useful structural feature of graphene but is superseded by the oxygen concentration and proximity to the edges in graphene oxide.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 tool to predict the properties of materials for a variety of applications. However, generating data sets of carefully characterized materials can be time-consuming and costly, particularly when numerous candidate materials are later found to be irrelevant. The problem could be alleviated if machine learning can be used with minimal information to provide guidance at an early stage before significant investment has been made. Since structural characterization is one of the most expensive parts of the process, this study explores structure-free encoding of materials using Mendeleev encoding, a method that does not require information such as lattice constants, lattice positions, or bonding networks. We evaluate Mendeleev encoding using three data sets of continuous, complex material compounds used for battery applications, with four different unsupervised learning methods, inclusive of six algorithms and four evaluation metrics and in addition visualizations of the results. Our results show that Mendeleev encoding is more accurate, stable, and reliable than alternative structure-free encoding, allowing both principle component analysis and archetypal analysis to capture more of the variance during dimensionality reduction and consistently provide superior clustering results. Mendeleev encoding is a simple and scientifically intuitive way of representing material data that is both human and machine-readable and is applicable to any machine-learning task training with tabular data.

Missed nursing care (MNC) has gained increasing emphasis in nursing literature because of its association with nurse and patient outcomes in healthcare settings. While missed care has been widely studied, little evidence is available on the types and frequency of missed care, reasons for its occurrence, and predictors of missed care in Western Australia.

In Australia, Aboriginal children experience disproportionate rates of type 2 diabetes (T2D) compared with non-Aboriginal children. The aim of this qualitative study was to explore the experiences of Aboriginal adolescents with T2D and their family members to better understand the influences of T2D on self-management, with findings used to inform an enhanced service model of care.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19), which is an ongoing global health concern. The exact source of the virus has not been identified, but it is believed that this novel coronavirus originated in animals; bats in particular have been implicated as the primary reservoir of the virus. SARS-CoV-2 can also be transmitted from humans to other animals, including tigers, cats, and mink. Consequently, infected people who work directly with bats could transfer the virus to a wild North American bat, resulting in a new natural reservoir for the virus, and lead to new outbreaks of human disease. We evaluated a reverse-transcription real-time PCR panel for detection of SARS-CoV-2 in bat guano. We found the panel to be highly specific for SARS-CoV-2, and able to detect the virus in bat guano samples spiked with SARS-CoV-2 viral RNA. Our panel could be utilized by wildlife agencies to test bats in rehabilitation facilities prior to their release to the wild, minimizing the risk of spreading this virus to wild bat populations.

To map and review current literature to describe evidence-based nonpharmacological interventions for delirium prevention and management in adult critically ill patients.

Nanoparticles exhibit diverse structural and morphological features that are often interconnected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanoparticles is developed for the energy of Fermi level, which can be tuned to improve the transfer of electrons in a variety of applications. By combining different machine learning analytical algorithms, including k-mean, logistic regression, and random forest with electronic structure simulations, we find that the degree of twinning (characterized by the fraction of hexagonal closed packed atoms) and the population of the {111} facet (characterized by a surface coordination number of nine) are strongly correlated to the Fermi energy of silver nanoparticles. A concise three layer artificial neural network together with principal component analysis is built to predict this property, with reduced geometrical, structural, and topological features, making the method ideal for efficient and accurate high-throughput screening of large-scale virtual nanoparticle libraries and the creation of single-structure/single-property, multi-structure/single-property, and single-structure/multi-property relationships in the near future.

In this work, we combine 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 are dissolved in a solvent, the pH of this solvent plays a controlling role on the adsorption of DOX molecules on the ND. We find that the binding of DOX molecules occurs only at high pH, and requires at least five ionizable groups per facet for binding to occur. As such, this study reveals important mechanistic insight underlying an ND-based pH-controlled therapeutic platform.

Machine learning classification is a useful technique to predict structure/property relationships in samples of nanomaterials where distributions of sizes and mixtures of shapes are persistent. The separation of classes, however, can either be supervised based on domain knowledge (human intelligence), or based entirely on unsupervised machine learning (artificial intelligence). This raises the questions as to which approach is more reliable, and how they compare? In this study we combine an ensemble data set of electronic structure simulations of the size, shape and peak wavelength for the optical emission of hydrogen passivated silicon quantum dots with artificial neural networks to explore the utility of different types of classes. By comparing the domain-driven and data-driven approaches we find there is a disconnect between what we see (optical emission) and assume (that a particular color band represents a special class), and what the data supports. Contrary to expectation, controlling a limited set of structural characteristics is not specific enough to classify a quantum dot based on color, even though it is experimentally intuitive.

To estimate the level of dispensing of oral corticosteroids (OCS) for managing asthma in Australia, with a particular focus on the cumulative dispensing of doses associated with long term toxicity (≥ 1000 mg prednisolone‐equivalent). Retrospective cohort study; analysis of 10% random sample of Pharmaceutical Benefits Scheme (PBS) dispensing data. People aged 12 years or more treated for asthma during 2014–2018, according to dispensing of controller inhaled corticosteroids (ICS). Number of people dispensed OCSfor managing asthma during 2014–2018; proportion who were cumulatively dispensed at least 1000 mg prednisolone‐equivalent. The secondary outcome was the number of people dispensed at least 1000 mg prednisolone‐equivalent during 2018, stratified by inhaler controller dose and use. 124 011 people had been dispensed at least two prescriptions of ICSduring 2014–2018 and met the study definition for asthma, of whom 64 112 (51.7%) had also been dispensed OCS, including 34 580 (27.9% of the asthma group) cumulatively dispensed 1000 mg prednisolone‐equivalent or more. Of 138 073 people dispensed OCSat this level, 68 077 (49%) were patients with airway diseases. Dispensing of diabetes and osteoporosis medications was more common for people cumulatively dispensed 1000 mg prednisolone‐equivalent or more. During 2018, 4633 people with asthma using high dose ICScontrollers were dispensed 1000 mg prednisolone‐equivalent or more, for 2316 of whom (50%) controller use was inadequate. Cumulative exposure to OCSin Australia reaches levels associated with toxicity in one‐quarter of patients with asthma using ICS. Cumulative dispensing of potentially toxic OCSamounts often accompanies inadequate inhaler controller dispensing. Better approaches are needed to improve adherence to controller therapy, improve outcomes for people with asthma, and to minimise the use and toxicity of OCS.

The CE test for this article is available online only at the journal website, jwocnonline.com, and the test can be taken online at NursingCenter.com.

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.

Generating samples of nanoparticles with specific properties that allow for structural diversity, rather than requiring structural precision, is a more sustainable prospect for industry, where samples need to be both targeted to specific applications and cost effective. This can be better enabled by defining classes of nanoparticles and characterising the properties of the class as a whole. In this study, we use machine learning to predict the different classes of diamond nanoparticles based entirely on the structural features and explore the populations of these classes in terms of the size, shape, speciation and charge transfer properties. We identify 9 different types of diamond nanoparticles based on their similarity in 17 dimensions and, contrary to conventional wisdom, find that the fraction of sp2or sp3hybridized atoms are not strong determinants, and that the classes are only weakly related to size. Each class has been describe in such way as to enable rapid assignment using microanalysis techniques.

Abstract

Unlike other data intensive domains, understanding distributions, trends, correlations, and relationships in materials data sets typically involves navigating high-dimensional spaces with only a limited number of observations. Under these conditions extracting structure/property relationships is not straightforward and considerable attention must be given to the reduction of feature space before predictions can be made. Here we have used Kohonen networks (self-organizing maps) to identify hidden structure/property relationships in computational sets of twinned and single-crystal diamond nanoparticles based on structural similarity in multiple dimensions, and confirmed the importance of a limited number of surface chemical features using regression.

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.

Nanometer-sized diamond particles are used in bio-medical applications, where the nature of the nanodiamond surfaces is crucial to achieving correct functionalisation. Herein, using high-resolution transmission electron microscopy and electronic structure calculations, we study the surface reconstructions that occur in detonation-synthesized nanodiamonds. Our results show that particles smaller than 3 nm exhibit size- and shape-dependent surface reconstructions, and that the surfaces can exhibit a higher-than-expected fraction of sp2+xbonding. This indicates an aliphatic character for sub-3 nm nanodiamond particles. Such behaviour impacts the functionality of nanodiamonds, where both size and surface charge can drive performance. Our observations offer a potential strategy for better functionalization control viathe size range of the particles.

The application of machine learning (ML) to materials chemistry can accelerate the design process, and when coupled with a detailed explanation, can guide future research. Shapley value analysis is a complementary approach capable of providing a comprehensive analysis of the underlying reasons behind a structure/property relationship. In this study, we have used data sets of graphene oxide nanomaterials generated using electronic structure simulations to train ML models with outstanding accuracy, generalizability, and stability to predict the formation energy and the Fermi energy and applied Shapley value analysis to understand the results. Feature important profiles that rank the value of structural characteristics to each property confirmed that the underlying structure/property relationships are relatively simple and scientifically intuitive, even though the ML models need complex information to achieve high performance. We have also reported instance influence profiles that rank the value of each individual graphene oxide structure to the training process. Feature/instance interactions are also investigated to explain which structural characteristics make particular structures influential, revealing that the most influential structures typically have very high or very low concentrations of H or O. Since the range of concentrations is typically chosen by researchers based on domain knowledge at the outset, this highlights that extreme care should be taken when gathering training data as these decisions will have a very big impact on the final model once trained. In general, the reproducible workflow demonstrated here can be applied to any similar materials data set to make reliable model-agnostic predictions of how the structural characteristics and individual structures contribute to the prediction of functional properties.

Producing perfectly regulated nanoparticle samples on a large scale is challenging and costly for manufacturers, so the ability to define and reproduce classes of nanoparticles with similar characteristics is attractive. However, developing structure/class or process/structure/class relationships is not straightforward. In this study we propose a machine learning pipeline of grouping nanoparticles based on their similarity in a high-dimensional feature space via clustering, predicting the nanoparticle classes from their structural features via classification, and identifying the relevant features that should be tuned to produce a specific class via causal inference. Using a simulated ruthenium nanoparticles data set as an exemplar, a support vector machine trained on 22 structural features managed to achieve highly accurate classification of ruthenium nanoparticles into ordered crystalline, polycrystalline, and disordered noncrystalline nanoparticles with virtually no overfitting and underfitting and high precision and recall. A Bayesian network with domain knowledge incorporated via interactive learning was trained using a hill climbing algorithm to confirm which features are causing the classes, as opposed to being just correlated to them.

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.

For many years dealing with the complexity of nanoscale materials, the polydispersivity of individual samples, and the persistent imperfection of individual nanostructures has been secondary to our search for novel properties and promising applications. For our science to translate into technology, however, we will inevitably need to deal with the issue of structural diversity and integrate this feature into the next generation of more realistic structure/property predictions. This is challenging in the field of nanoscience where atomic level precision is typically inaccessible (experimentally), but properties can depend on structural variations at the atomic scale. Fortunately there exists a range of reliable statistical methods that are entirely applicable to nanoscale materials; ideal for navigating and analysing enormous amount of information required to accurately describe realistic samples. Combined with advances in automation and information technology the field of data science can assist us in dealing with our big data, characterising our uncertainties, and more rapidly identifying useful structure/property relationships. Taking greater advantage of data-driven methods involves thinking differently about our research, but applied appropriately these methods can accelerate the discovery of nanomaterials that are optimised to make the transition from science to technology.

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.

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.

Platinum nanoparticles are widely used catalysts in many important industrial applications, in the chemical, petrochemical, automotive and energy sectors. Due to the extremely high cost and the limited abundance of platinum, improving the efficiency of platinum-based nanocatalysts is key to the economic development of these materials, as well as being a challenge for basic research. Ultimately we seek to increase the active surfaces area, per unit volume, and to preserve the activity and selectivity over the functional lifetime of the product. In this work the formation of platinum nanoparticles is investigated by molecular dynamic simulations under different conditions of temperature and atomic deposition rates to identify the conditions that give rise to a greater density of different types of surface active sites. By tuning the growth conditions we obtained highly non-equilibrium morphologies with branches that expose larger surface areas that are consistent with experimental observations. The results are also used to clarify the relationship between growth conditions, surface structure and catalytic functionality based on a simple surface defect classification model, which differentiates between CO oxidation reactions, hydrogen evolution reactions (HER) and hydrogen oxidation reactions (HOR).

Recent developments of metallic nanoparticle catalysts have been largely based on the assumption and evidence that exquisite control over the size or shape (or both) is critically important to the economic efficiency of future products. However, the cost associated with reducing polydispersivity on the industrial scale is also a limiting factor, and at this stage it is unclear if samples that are monodispersed in size or shape are more desirable. In this study we use a combination of thermodynamic and statistical models to explore how restricting different types of structural polydispersivity impacts the performance of platinum electrocatalysts, characterized by the molar density of surface defects, and their respective degree of under-coordination. We find that a combination of simultaneous size and shape control is advantageous, but attention and resources should be directed toward producing shape control. More specifically, a sample containing particles entirely enclosed by {111} facets, regardless of the geometric shape, will always outperform samples where other crystallographic facets are present; but perfect monodispersivity is unnecessary. Distributions in both size and shape are acceptable (and can even be useful), provided they are predictable and reproducible.

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.

Innovations in computational nanoscience have traditionally come in conjunction with experimental innovations, but uncertainty often surrounds the trustworthiness of in silicostudies. 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 silicosamples. 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.

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.

In recent years fluorescent diamond nanoparticles have begun to fill the need for optically bright and stable, non-toxic probes for a growing range of biomedical applications. Significant attention has been given to finding ways of obtaining small particles with a higher yield of the optically active N–V defects, on-demand, by processing mass produced samples post defacto. While it has been established that a combination of irradiation (to introduce an abundance of vacancies) and annealing (to increase vacancy mobility) can achieve these goals, it is desirable to know how many vacancies to introduce a priori. In this paper a numerical model is developed to predict and compare the yield of different nitrogen/vacancy complexes in nanodiamond, as a function of the individual nitrogen and vacancy concentrations, the particle size and annealing temperature. The results reveal that, irrespective of the temperature and nitrogen content, this processing strategy is unlikely to work for detonation nanodiamond, but tuning these concentrations can promote the formation of N–V or H3 defects over ∼20 nm.

BackgroundThe process of offering and scheduling residency interviews varies widely among programmes. Applicants report distress and have advocated for reform. However, there is a paucity of quantitative data to characterise applicant concerns.ObjectiveWe quantified the interview scheduling experience for US allopathic medical students in the 2020 main residency match.MethodsAn anonymous, 13 question survey was sent to student representatives from each Association of American Medical Colleges member institution. Recipients were asked to forward the survey to their entire fourth-year class.ResultsOf 4314 applicants to whom the survey was sent, 786 (18.2%) responded. Overall, 20.4% reported missing the opportunity to interview at a programme because they did not have adequate time to respond to an invitation; applicants into surgical specialties were significantly more likely than their non-surgical peers to report this experience (26.4% vs 18.4%, p<0.05). Most (57.4%) respondents scheduled an interview knowing they would likely cancel it in the future. The most commonly cited reason for this behaviour was concern that applicants would not receive invitations from other programmes (85.6%). A majority (56.4%) of respondents did not believe the match interview process functions based on equity and merit.ConclusionsAbout one in five respondents missed the opportunity to interview at a programme because they did not respond to an invitation in time. Most respondents scheduled interviews knowing that they were likely to cancel them in the future. Standardisation of the interview invitation timeline would address these concerns.

There is significant interest in discovering high-capacity battery materials, prompting the investigation of the electrochemical energy storage potential of the two-dimensional early transition metal carbides known as MXenes. Predicting the relationship between the composition of a MXene and electrochemical properties is a focus of considerable research. In this paper we classify the specific MXene chemical formula using a new categorical descriptor and simultaneously predict multiple target electrochemical properties. We then invert the design challenge and predict the formula for MXenes based on a set of battery performance criteria. This approach involves a workflow that includes multi-target regression and multi-target classification, focusing on the physicochemical features most pertinent to battery design. The final inverse model recommends Li2M2C and Mg2M2C (M = Sc, Ti, Cr) as candidates for more focused research, based on desirable ranges of gravimetric capacity, voltage, and induced charge.

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.

The widespread nanostructures of iron oxides and oxyhydroxides are important reagents in many biogeochemical processes in many parts of our planet and ecosystem. Their functions in various aspects are closely related to their shapes, sizes, and thermodynamic surroundings, and there is much that we can learn from these natural relationships. This review covers these subjects of several phases (ferrihydrite, goethite, hematite, magnetite, maghemite, lepidocrocite, akaganéite and schwertmannite) commonly found in water, soils and sediments. Due to surface passivation by ubiquitous water in aquatic and most terrestrial environments, the difference in formation energies of bulk phases can decrease substantially or change signs at the nanoscale because of the disproportionate surface effects. Phase transformations and the relative abundance are sensitive to changes in environmental conditions. Each of these phases (except maghemite) displays characteristic morphologies, while maghemite appears frequently to inherit the precursor's morphology. We will see how an understanding of naturally occurring iron oxide nanostructures can provide useful insight for the production of synthetic iron oxide nanoparticles in technological settings.

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.

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 initiothermodynamics and in situhigh-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.

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.

Cell-cell contact is required for efficient transmission of human T-lymphotropic virus type 1 (HTLV-1). An HTLV-1–infected cell polarizes its microtubule-organizing center (MTOC) toward the cell-cell junction; HTLV-1 core (Gag) complexes and the HTLV-1 genome accumulate at the point of contact and are then transferred to the uninfected cell. However, the mechanisms involved in this cytoskeletal polarization and transport of HTLV-1 complexes are unknown. Here, we tested the hypothesis that engagement of a specific T-cell surface ligand is synergistic with HTLV-1 infection in causing polarization of the MTOC to the cell contact region. We show that antibodies to intercellular adhesion molecule-1 (ICAM-1; CD54) caused MTOC polarization at a higher frequency in HTLV-1–infected cells. ICAM-1 is upregulated on HTLV-1–infected cells, and, in turn, ICAM-1 on the cell surface upregulates HTLV-1 gene expression. We propose that a positive feedback loop involving ICAM-1 and HTLV-1 Tax protein facilitates the formation of the virologic synapse and contributes to the T-cell tropism of HTLV-1. In contrast, MTOC polarization induced in T cells by antibodies to CD3 or CD28 was significantly inhibited by HTLV-1 infection.

Cell-cell contact is required for efficient transmission of human T-lymphotropic virus type 1 (HTLV-910101). An HTLV-1–infected cell polarizes its microtubule-organizing center (MTOC) toward the cell-cell junction; HTLV-1 core (Gag) complexes and the HTLV-1 genome accumulate at the point of contact and are then transferred to the uninfected cell. However, the mechanisms involved in this cytoskeletal polarization and transport of HTLV-1 complexes are unknown. Here, we tested the hypothesis that engagement of a specific T-cell surface ligand is synergistic with HTLV-1 infection in causing polarization of the MTOC to the cell contact region. We show that antibodies to intercellular adhesion molecule-1 (ICAM-1; CD54) caused MTOC polarization at a higher frequency in HTLV-1–infected cells. ICAM-1 is upregulated on HTLV-1–infected cells, and, in turn, ICAM-1 on the cell surface upregulates HTLV-1 gene expression. We propose that a positive feedback loop involving ICAM-1 and HTLV-1 Tax protein facilitates the formation of the virologic synapse and contributes to the T-cell tropism of HTLV-1. In contrast, MTOC polarization induced in T cells by antibodies to CD3 or CD28 was significantly inhibited by HTLV-1 infection.

An emerging topic in computational and theoretical nanoscience is the study of shape at the nanoscale. Prediction of particle shape based on parameters such as composition, size and chemical environment has previously meant undertaking large numbers of independent simulations; individually examining sets of structures with desired shapes, while explicitly varying each physical parameter of interest. Thanks to a number of new analytical expressions, the need for such large data sets may be a thing of the past.

Review: 124 refs.

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.