Effect of Al on the hydrogenation characteristics of nanocrystalline Mg powder

We have fabricated nanocrystalline Mg–Al powders with nominal Al compositions of 4 and 8 at% by the electrodeposition technique and have compared their hydrogenation characteristics with those for a commercially available pure Mg powder. It is elucidated that interestingly the amount of Al incorporated in MgH₂ increases with decreasing the hydrogenation temperature. This observation indicates that the magnesium hydride phase has a limited solubility for aluminum and the supersaturation of hydride with Al is attributed to the slower mobility of aluminum at low temperatures. The rejection of Al during hydride formation is shown to result in a higher fraction of the intermetallic phase, thus reducing the hydrogen capacity. It is suggested that the presence of the intermetallic phase prior to hydrogenation enhances the hydride nucleation rate, which further decreases the hydrogen capacity in comparison to pure Mg.     

The effect of promoters on the CO<Subscript>2</Subscript> reforming activity and coke formation of nanocrystalline Ni/Al<Subscript>2</Subscript>O<Subscript>3</Subscript> catalysts prepared by microemulsion method

Mesoporous nanocrystalline nickel-alumina promoted catalysts with high surface area were prepared by microemulsion (ME) method and employed in dry reforming of methane reaction. The catalysts were characterized by X-ray diffraction (XRD), Brunauer-Emmett-Teller surface area analysis (BET), temperature programmed reduction (TPR) and temperature programmed oxidation (TPO) techniques. The results showed that the prepared catalysts had high porosity with great surface area and small crystallite size. Among the K₂O, MgO, CaO and BaO promoters, the MgO promoter showed considerable effect on catalytic performance and coke suppression of catalyst.     

Mesoporous silica coated gold nanorods: a multifunctional theranostic platform for radiotherapy and X-ray imaging

Journal of Porous Materials - The development of theranostic nanostructures is one of the most advanced branches of pharmaceutical and medical sciences in the world today. Due to the unique...     

Preparation and Characterization of Polyvinylpyrrolidone/L-leucine Amino Acid–Modified Montmorillonite/Chiral Diacid–Functionalized Mg-Substituted Fluorapatite Nanocomposites by Ultrasonic-Assisted Rapid Process

In this work, the surface of synthetic nanosized Mg-substituted fluorapatite particles was modified with biodegradable diacid N-trimellitylimido-L-leucine as a coupling agent in methanol. Furthermore, different contents of N-trimellitylimido-L-leucine-modified Mg-substituted fluorapatite were doped into polyvinylpyrrolidone/L-leucine-modified montmorillonite material to prepare novel ternary polyvinylpyrrolidone/modified montmorillonite/N-trimellitylimido-L-leucine-Mg-substituted fluorapatite nanocomposites by an efficient sonication process. Nanocomposite containing 5?wt% modified montmorillonite and 3, 5, and 7?wt% N-trimellitylimido-L-leucine-modified Mg-substituted fluorapatite contents were prepared and labeled as polyvinylpyrrolidone/modified montmorillonite/N-trimellitylimido-L-leucine-Mg-substituted fluorapatite 3, polyvinylpyrrolidone/modified montmorillonite/N-trimellitylimido-L-leucine-Mg-substituted fluorapatite 5, and polyvinylpyrrolidone/modified montmorillonite/N-trimellitylimido-L-leucine-Mg-substituted fluorapatite 7, respectively. The structure and morphology of the aforesaid products were characterized by different analytical apparatuses.     

A Comparative Assessment of Artificial Neural Network,Generalized Regression Neural Network,Least-Square Support Vector Regression,and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Monthly forecasting of streamflow is of particular importance in water resources management especially in the provision of rule curves for dams. In this paper, the performance of four data-driven models with different structures including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Least Square-Support Vector Regression (LS-SVR), and K-Nearest Neighbor Regression (KNN) are evaluated in order to forecast monthly inflow to Karkheh dam, Iran, in linear and non-linear conditions while the optimized values of the model parameters are determined in the same condition via the Leave-One-Out Cross Validation (LOOCV) method. Results show that the performance of the models is different in linear and nonlinear conditions; the cumulative ranking of the models according to the three assessment criteria including NSE, RMSE and R² indicates that ANN performs best in linear conditions while LS-SVR, GRNN and KNN are in the next ranks, respectively. But in nonlinear conditions, the best performance belongs to LS-SVR, followed by KNN, ANN, and GRNN models.     

Design of a reversible structure for memory in quantum-dot cellular automata

Complementary metal oxide semiconductor (CMOS) technology has limitations in reducing the area and size of circuits. The disadvantages of this technology include high power consumption and temperature problems. Quantum-dot cellular automata (QCA) is a new technology that can overcome these shortcomings. Reversible logic is technology used to reduce the power loss in QCA. QCA can be used to design memories that require high operating speed. In this paper, we propose a structure for the reversible memory in QCA. The proposed structure utilizes three-layer technology, which has a significant impact on circuit size reduction. The proposed structure for the reversible memory has 63% improvement in cell number, a 75% improvement in area occupancy, and a 60% reduction in delay compared to the previous best structure.     

Extraction of pomegranate (Punica granatum L.) seed oil using superheated hexane

Superheated hexane extraction (SHHE), Soxhlet extraction and cold pressing method were compared for the extraction of pomegranate seed oil. The extraction efficiencies of different temperatures (80, 100, 120 °C), mean particle sizes (0.25, 0.50 and 1.00 mm), and n-hexane flow rates (0.5, 1.0 and 2.0 mL/min) were investigated. The fatty acids profile of the seed oils were quantitatively determined by GC-FID. The optimum extraction temperature, mean particle size, and flow rate were selected as 80 °C, 0.25 mm, and 1 mL/min, respectively. The SHHE showed a higher extraction efficiency (22.18 wt%) within 2 h than Soxhlet extraction (17.94 wt%) for 24 h and cold pressing (4.29 wt%) for 72 h. The fatty acids profile for SHHE was more similar to that obtained by cold pressing method than Soxhlet extraction.