Influence of Mg concentration on structural,morphological and optical properties of nanoceria

Nano crystalline pure and Mg doped ceriaparticles were synthesized by simple chemical co-precipitation method using cerium nitrate hexahydrate as a source material and magnesium nitrate as doping precursor at room temperature. The effect of doping were investigated by X-ray diffraction pattern(XRD), FT-Raman,fourier transform infrared spectroscopy(FTIR), Ultraviolet spectroscopy(UV), photoluminescence spectroscopy(PL), field emission scanning electron microscope(FESEM) and high resolution transmission electron microscopy with energy dispersive spectroscopy (HRTEM &EDS). The X-ray diffraction pattern and FT-Raman studies showed that the prepared samples were nano particulates with cubic fluorite structure. The XRD pattern analysis showed that the size of the particles ranged from 13 to 20?nm, however 4?wt% Mg doping results in reduction of particle size compared with other doping concentrations. The effects of Mg concentration on various structural parameters of the prepared samples were also determined. The slight blue shift observed upon doping in UV–Vis absorption region around 330–360nmrecorded for reduction in particle size. The FTIR unveils the presence of Metal oxygen bonds below 700?cm^?1in the prepared samples. All samples showed a broad emission band at 430?nm with linearly increasing intensity with respect to dopant concentrations. The Spherical morphology with weak agglomeration was identified through FESEM and HRTEM analysis. The elemental analysis of Ce, O and Mg were confirmed through EDS analysis.     

Microwave dielectric properties of ultra-low loss Li2MgTi0.7(Mg1/3Nb2/3)0.3O4 ceramics sintered at low temperature by LiF addition

In this work, ultra-low loss Li₂MgTi_0.7(Mg_1/3Nb_2/3)_0.3O₄ ceramics were successfully prepared via the conventional solid-state method. X-ray photoelectron spectroscopy (XPS), thermally stimulated depolarization current (TSDC) and bond energy were used to determine the distinction between intrinsic and extrinsic dielectric loss in (Mg_1/3Nb_2/3)⁴⁺ ions substituted ceramics. The addition of (Mg_1/3Nb_2/3)⁴⁺ ions enhances the bond energy in unit cell without changing the crystal structure of Li₂MgTiO₄, which results in high Q·f value as an intrinsic factor. The extrinsic factors such as porosity and grain size influence the dielectric loss at lower sintering temperature, while the oxygen vacancies play dominant role when the ceramics densified at 1400?°C. The Li₂MgTi_0.7(Mg_1/3Nb_2/3)_0.3O₄ ceramics sintered at 1400?°C can achieve an excellent combination of microwave dielectric properties: ε_r =?16.19, Q·f?=?160,000?GHz and τ_f =??3.14?ppm/°C. In addition, a certain amount of LiF can effectively lower the sintering temperature of the matrix, and the Li₂MgTi_0.7(Mg_1/3Nb_2/3)_0.3O₄-3?wt% LiF ceramics sintered at 1100?°C possess balanced properties with ε_r?=?16.32, Q·f?=?145,384?GHz and τ_f =??16.33?ppm/°C.     

Search for Solving the Problem of Conditioning the Reactor Graphite

Radiochemistry - Spent reactor graphite constitutes a significant part of the accumulated solid radioactive wastes, and a search for its isolation from the biosphere is a topical task. Radioactive...     

The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype–phenotype maps

Biological information is stored in DNA, RNA and protein sequences, which can be understood as genotypes that are translated into phenotypes. The properties of genotype–phenotype (GP) maps have been studied in great detail for RNA secondary structure. These include a highly biased distribution of genotypes per phenotype, negative correlation of genotypic robustness and evolvability, positive correlation of phenotypic robustness and evolvability, shape-space covering, and a roughly logarithmic scaling of phenotypic robustness with phenotypic frequency. More recently similar properties have been discovered in other GP maps, suggesting that they may be fundamental to biological GP maps, in general, rather than specific to the RNA secondary structure map. Here we propose that the above properties arise from the fundamental organization of biological information into ‘constrained'' and ‘unconstrained'' sequences, in the broadest possible sense. As ‘constrained'' we describe sequences that affect the phenotype more immediately, and are therefore more sensitive to mutations, such as, e.g. protein-coding DNA or the stems in RNA secondary structure. ‘Unconstrained'' sequences, on the other hand, can mutate more freely without affecting the phenotype, such as, e.g. intronic or intergenic DNA or the loops in RNA secondary structure. To test our hypothesis we consider a highly simplified GP map that has genotypes with ‘coding'' and ‘non-coding'' parts. We term this the Fibonacci GP map, as it is equivalent to the Fibonacci code in information theory. Despite its simplicity the Fibonacci GP map exhibits all the above properties of much more complex and biologically realistic GP maps. These properties are therefore likely to be fundamental to many biological GP maps.     

Conveyer-belt accidents in mining and metallurgy

The dynamic interaction between the mechanical and electrical drive parameters when damage suddenly appears in the mechanical part of a conveyer belt is investigated. The possibility of using the change in the motor’s electrical parameters as a diagnostic signal is assessed. A model for investigating the dynamic processes in the system consisting of the grid, the induction motor, and the conveyer belt is proposed. The grid and induction motor are described by a model of fourth-order state space, whose output is the torque at the motor shaft. The moments of inertia and pliability of the links between the elements in the mechanical part of the conveyer belt are determined. By gradually identifying the elements with the minimum moment of inertia and distributing their moments of inertia and pliabilities among the adjacent elements, we obtain a three-mass system simulating the mechanical part of the conveyer belt. This dynamic model is solved by means of Matlab Simulink software. The energy characteristics of the drive are determined in dynamic processes following a mechanical accident.