Synthesis, Characterization, and Formation Process of Na-X Zeolite from Coal Fly Ash

Using the SiO₂ and Al₂O₃ components of the amorphous phase in coal fly ash (Fa), Fa was converted to Na-X zeolites in NaOH-NaAlO₂ solutions by stirring at 35°C for 72 hr and then aging at 85°C for a given period. The molar ratio SiO₂/Al₂O₃ of the starting materials was controlled from 2.0 to 13.2. The resulting materials were characterized by various means. Increasing the SiO₂/Al₂O₃ ratio of the starting material increased the degree of crystallinity of faujasite, exhibiting a maximum at SiO₂/Al₂O₃ = 8.0. The faujasite formed was identified as Na-X zeolite with Si/Al = 1.20. The amorphous phase in Fa was dissolved during the stirring to form a precursor of zeolite, such as amorphous aluminosilicate. The Na-X zeolite was formed by aging for 24 hr, and the degree of crystallinity of this material was increased with the increasing aging period. The cation exchange capacity and specific surface area were increased with the increasing degree of crystallinity of the Na-X zeolites.     

Antimony activities in copper mattes

A mass spectrometric technique combined with a double Knudsen cell was used to determine the antimony and copper activities in the Cu-Sb binary system at 1373 K and in the two-melt composition range of the Cu−S−Sb ternary system at 1423 K. The antimony and copper activities were calculated based on the intensity ration of the gaseous Sb and Cu species, over the unknown and known activity samples, respectively. γ _Sb ^o were found to be 1.1×10⁻² in molten copper at 1373 K, and 1.8×10⁻² and 0.44 in a copper-rich phase and in a matter phase, of the Cu−S−Sb ternary system at 1423 K, respectively. These values indicate, that antimony can be removed during the matte smelting and slagging stage of the copper smelting process. Interaction parameters of antimony in molten copper slagging stage of the copper smelting process. Interaction parameters of antimony in molten copper at 1423 K were calculated and found to be 10.7, −5.4, and 6.3 for ε _Sb ^Sb · ρ_Sb ^Sb, and ε _Sb ^S , respectively. M. HINO, formerly Visiting Scientist at the University of Toronto     

Surface Hardening of Carbon Steel Using High Powered YAG Laser

A high powered YAG laser with kaleidoscope for surface modification was applied to the surface hardening of carbon steels containing 0. 18-0.54 wt% C without the absorbents, and the relationships between laser processing and surface hardening were investigated by hardness and microstructure. The structure of the hardened zone underwent complete martensitic transformation in all of the carbon steels tested, and its hardness increased with greater carbon content. Under identical irradiated conditions, the hardened zone expanded with increasing carbon content. A hardened zone extending from the surface to a depth of 1.0 mm was obtained at a laser power of 1.0 kW and a scanning speed of 1 mm/sec. It was found that in the surface hardening of carbon steels, a high powered YAG laser can be used to control the hardened zone by selecting the appropriate irradiation conditions, however, the hardened zone was affected by the assistant gas and the flow rate.     

Paxillin isoforms in mouse. Lack of the gamma isoform and developmentally specific beta isoform expression

Paxillin, a focal adhesion protein, exists as multiple isoforms in humans (alpha, beta, and gamma). To understand more about the physiological role of each isoform, we have employed the mouse system. We found that although the alpha and beta isoforms are present in the mouse, the gamma isoform is not. The alpha isoform protein was detected clearly in most adult tissues, whereas the beta isoform protein was almost undetectable except in spleen, testis, thymus, and lung. On the other hand, mRNAs of both isoforms were detectable in all tissues we examined. High levels of the beta isoform protein was detected in peritoneal exudate macrophage cells in adult mouse as well as in cultured fibroblasts, together with the alpha isoform. The alpha isoform was expressed at a constant level throughout the embryonic stages we examined, whereas the beta isoform protein was detected at the mid-stages of development and increased to levels almost equal to those of the alpha isoform during the late stages of embryogenesis. Therefore, unlike the alpha isoform, expression of the beta isoform protein is restricted in adult tissues. Moreover, we showed that alpha and beta isoforms were colocalized within the same focal adhesion plaques, and cytoplasmic pools of both isoforms exist in the perinuclear area, colocalized with the Golgi apparatus.     

Investigation of the effect of surface phosphate ester dispersant on viscosity by coarse-grain modeling of BaTiO slurry

To understand the role of phosphate ester dispersant, we investigated the rheology of a BaTiO slurry. For the model case, a coarse-grain molecular dynamics (CGMD) simulation was performed with the butyral polymer didodecyl hydrogen phosphate (DHP) in the toluene/ethanol solvent. By systematically analyzing the effect of DHP from an atomic-scale first principle and from all-atom MD to microscale CGMD simulation, we investigated how the adsorption of a DHP dispersant on a BaTiO surface affects the microstructure rheology of a BaTiO slurry. The first-principle and all-atom MD simulation suggests that DHP molecules prefer to locate near the BaTiO surface. CGMD simulation shows a reduction in viscosity with an increase in dispersants, suggesting that the dispersant population near the BaTiO surface plays a key role in controlling the rheology of the BaTiO slurry. In this study, we propose an approach for understanding the BaTiO slurry with molecular-level simulations, which would be a useful tool for efficient optimization of slurry preparation.     

Preparation of superacid of iron-supported zirconia for reaction of butane to isobutane

A highly active superacid of 2 wt% Fe-supported ZrO₂ for the skeletal isomerization of butane to isobutane was obtained by sfexposing Fe₂O₃/ZrO₂ to 1 N H₂SO₄ followed by calcining in air at 700°C for more than 24 h; the Fe₂O₃/ZrO₂ was prepared by impregnating zirconia gel with a solution of Fe(NO₃)₃ followed by drying at 300°C (2 wt% Fe). A much lower activity was observed with the opposite procedure, where the first impregnation was sulfation of the gel, followed by a second impregnation with the iron compound. It was proved from analysis of the sulfur content in the catalysts that residual sulfur species were not related with generation of the superacidic sites. XPS showed the catalyst to be Fe₂O₃ supported on ZrO₂.Superacids by metal oxides, VIII. For previous publications VI and VII in this series see refs. [10,11].     

Set-based particle swarm optimization with status memory for knapsack problem

Set-based particle swarm optimization (S-PSO) operates on discrete space. S-PSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In S-PSO, a velocity is a set with possibility and a position is a candidate solution. In this paper, we present a novel algorithm of set-based particle swarm optimization with status memory (S-PSOSM) to decide the position based on the previous position for solving knapsack problem. Some operators are redefined for S-PSOSM. S-PSOSM is a simple algorithm because the state of probability reduces. In addition, the weight of S-PSOSM is discussed. S-PSOSM shows high qualities in experimental results.     

Multiple kernel learning with gaussianity measures

Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power.