Reducing treatment of coppersmelting slag: Thermodynamic analysis of impurities behavior

A thermodynamic mathematical model, describing behavior of Pb, Zn, and As during reducing slag cleaning in the Vanyukov furnace has been developed. Using a developed model, the influence of different factors, such as temperature, oxygen partial pressure, the ratio of the formed phases on the behavior of impurities, was analyzed. It was found that arsenic can significantly move to the bottom phase, and zinc can be significantly vaporized under conditions in the Vanyukov furnace.     

Collagen scaffolds for orthopedic regenerative medicine

Collagen and collagen-based scaffolds offer distinct advantages when selected as biomaterials for use across a broad spectrum of regenerative medicine applications. However, relatively poor mechanical properties are often perceived to limit their usefulness for orthopedic applications. These problems can be overcome through enhanced crosslinking mechanisms or through the addition of a second, stiffer phase such as hydroxyapatite, thus allowing tailored composite scaffolds to meet specific tissue requirements. This overview will highlight the current state of the art of these scaffolds, and consider the exciting prospects and future directions of collagen-based technologies for orthopedic regenerative medicine.     

Beneficial and technological analysis for the recycling of solar grade silicon wastes

In the current paper, different kinds of silicon wastes during the production of SoG-Si were summarized and the beneficial analyses, such as financial value, energy value, CO₂ emissions, and efficiency and energy payback time, were briefly discussed for the recycling of SoG-Si wastes. Possible technologies to recycle and purify SoG-Si wastes were reviewed: such as filtration, sedimentation, solidification control, electromagnetic separation, plasma oxidation, centrifugation, and high temperature remelting process, et al.     

Cation-promoted supramolecular assembly of bivalent metal tetra-15-crown-5-phthalocyaninates: Controlling the architecture of supramolecular aggregates

Cation-promoted supramolecular assembly of cobalt(II) and ruthenium(II) tetra-15-crown-5-phthalocyaninates is comparatively studied using the electronic absorption and infrared spectroscopy. The effects of the nature of the receptor and substrate on the architecture of the supramolecular aggregates formed are discovered. By contrast to Cu(II), Zn(II), and Ni(II) tetra-15-crown-5-phthalocyaninates, Co(II) and Ru(II) complexes are found to demonstrate sodium-potassium selectivity determined by the peculiarities of supramolecular structures formed upon the interaction with the cations.     

Measuring spatial dispersion: exact results on the variance of random spatial distributions

Measuring the spatial distribution of locations of many entities (trees, atoms, economic activities, etc.), and, more precisely, the deviations from purely random configurations, is a powerful method to unravel their underlying interactions. Several coefficients have been developed in the past to quantify the possible deviations. It is important to quantify the variances of the coefficients for random distributions, to ascertain the statistical significance of an empirical deviation. By lack of a proper analytical expression, the significance is usually obtained by simulating many random configurations by Monte Carlo simulations. In the present paper, we present an exact analytical expression for the variance of several spatial coefficients for random distributions, and we rigorously show that these distributions asymptotically follow a Normal law. These two results eliminate the need for cumbersome Monte Carlo simulations. They also allow to understand qualitatively the main factors that may change the variance: number of sites, spatial inhomogeneity, etc.     

W-based versus latent variables spatial autoregressive models: evidence from Monte Carlo simulations

In this paper, we compare by means of Monte Carlo simulations two approaches to take spatial autocorrelation into account: the classical spatial autoregressive model and the structural equations model with latent variables. The former accounts for spatial dependence and spillover effects in georeferenced data by means of a spatial weights matrix W. The latter represents spatial dependence and spillover effects by means of a latent variable in the structural (regression) model while the observed spatially lagged variables are related to the latent spatial dependence variable in the measurement model. The simulation results based on Anselin’s Columbus, Ohio, crime data set show that the misspecified latent variables approach slightly trails the correctly specified classical approach in terms of bias and root mean squared error of the coefficient estimators.