Modifications of the chemical composition and microstructure of flash smelting copper slags in the process of their reduction

Blister copper smelting in a flash smelting furnace results in generation of slag that contains high amounts of copper, iron and lead. Most commonly, this material is subjected to reduction with coke in an electrical furnace. In the present paper, results of investigations on reduction of slag with another reducer, i.e. anthracite dust, are discussed. Each experimental slag was analysed for its microstructure, chemical composition and phase composition. Based on the results, a decopperisation level of the study material was estimated. It was shown that anthracite dust might be considered as an alternative for currently used reducers

Electrochemical Formation of Second Generation TiO2 Nanotubes on Ti13Nb13Zr Alloy for Biomedical Applications

The aim of this study was to obtain the second generation TiO2 nanotubes on the Ti13Nb13Zr alloy. Anodic oxidation of the alloy under study was carried out in 1 M (NH4)2SO4 electrolyte under voltage–time conditions of 20 V for 120 min. The morphological parameters of the obtained nanotubes of second generation such as the length (L), internal (Di) and outer (Do) diameter of nanotube were determined. It was found that the anodic oxidation of the Ti13Nb13Zr alloy conducted under proposed conditions allowed to obtain the single-walled nanotubes of the following geometrical parameters: the internal diameter 61 nm, outer diameter 103 nm, and the length 3.9 μm. The total surface area of the single-walled nanotubes was equal to 4.1 μm2, and the specific surface area per cm2 (As) was estimated to be 15.6 cm2/cm2. Formation mechanism, structure and optimal morphological parameters of the obtained single-walled nanotubes on the Ti13Nb13Zr alloy have been discussed in detail

Effect of Nb and Ti micro-additives and thermo-mechanical treatment of high-manganese steels with aluminium and silicon on their microstructure and mechanical properties

The r esults are based on two experimental high-manganese X98MnAlSiNbTi24-11 and X105MnAlSi24-11 steels subjected to thermo-mechanical treatment by hot-rolling on a semi-industrial processing line. The paper presents the results of diffraction and structural studies using scanning and transmission electron microscopy showing the role of Nb and Ti micro-additives in shaping high strength properties of high-manganese austenitic-ferritic steels with complex carbides. The performed investigations of two experimental steels allow to explain how the change cooling conditions after thermo-mechanical treatment of the analysed steels affects the change of their microstructure and mechanical properties. The obtained results allow assessing the impact of both the chemical composition and the applied thermo-mechanical treatment technology on the structural effects of strengthening of the newly developed steels

Spin and orbital magnetic moments of size-selected iron, cobalt, and nickel clusters and their link to the bulk phase diagrams

Spin and orbital magnetic moments of cationic iron, cobalt, and nickel clusters have been determined from x-ray magnetic circular dichroism spectroscopy. In the size regime of $n = 10 - 15$ atoms, these clusters show strong ferromagnetism with maximized spin magnetic moments of 1~$\mu_B$ per empty $3d$ state because of completely filled $3d$ majority spin bands. The only exception is $\mathrm{Fe}_{13}^+$ where an unusually low average spin magnetic moment of $0.73 \pm 0.12$~$\mu_B$ per unoccupied $3d$ state is detected; an effect, which is neither observed for $\mathrm{Co}_{13}^+$ nor $\mathrm{Ni}_{13}^+$.\@ This distinct behavior can be linked to the existence and accessibility of antiferromagnetic, paramagnetic, or nonmagnetic phases in the respective bulk phase diagrams of iron, cobalt, and nickel. Compared to the experimental data, available density functional theory calculations generally seem to underestimate the spin magnetic moments significantly. In all clusters investigated, the orbital magnetic moment is quenched to $5 - 25$\,\% of the atomic value by the reduced symmetry of the crystal field. The magnetic anisotropy energy is well below 65 $\mu$eV per atom

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

A Hepatic Protein, Fetuin-A, Occupies a Protective Role in Lethal Systemic Inflammation

A liver-derived protein, fetuin-A, was first purified from calf fetal serum in 1944, but its potential role in lethal systemic inflammation was previously unknown. This study aims to delineate the molecular mechanisms underlying the regulation of hepatic fetuin-A expression during lethal systemic inflammation (LSI), and investigated whether alterations of fetuin-A levels affect animal survival, and influence systemic accumulation of a late mediator, HMGB1.LSI was induced by endotoxemia or cecal ligation and puncture (CLP) in fetuin-A knock-out or wild-type mice, and animal survival rates were compared. Murine peritoneal macrophages were challenged with exogenous (endotoxin) or endogenous (IFN-γ) stimuli in the absence or presence of fetuin-A, and HMGB1 expression and release was assessed. Circulating fetuin-A levels were decreased in a time-dependent manner, starting between 26 h, reaching a nadir around 24-48 h, and returning towards base-line approximately 72 h post onset of endotoxemia or sepsis. These dynamic changes were mirrored by an early cytokine IFN-γ-mediated inhibition (up to 50-70%) of hepatic fetuin-A expression. Disruption of fetuin-A expression rendered animals more susceptible to LSI, whereas supplementation of fetuin-A (20-100 mg/kg) dose-dependently increased animal survival rates. The protection was associated with a significant reduction in systemic HMGB1 accumulation in vivo, and parallel inhibition of IFN-γ- or LPS-induced HMGB1 release in vitro.These experimental data suggest that fetuin-A is protective against lethal systemic inflammation partly by inhibiting active HMGB1 release