Using Core Formulations for Ill-posed Linear Systems

We investigate the application of core problem formulations to the context of ill-posed linear algebraic systems. We note that in order to impose regularization on such systems by using core formulations, one is lead to a technique similar to the Truncated (Total) Least Squares method. Choosing an appropriate truncation level from given data can be made efficient by incorporating the truncation level decision as a stopping criterion into a partial bidiagonalization algorithm. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Singlet-Oxygen Generation from Liposomes: A Comparison of 6β-Cholesterol Hydroperoxide Formation with Predictions from a One-Dimensional Model of Singlet-Oxygen Diffusion and Quenching

Abstract— We measured 6β-cholesterol hydroperoxide (6β-CHP), a specific singlet-oxygen (O₂(δg)) product, during irradiation of unilamellar dimyristoyl 1-α-phosphatidylcholine liposomes containing cholesterol and zinc phthalocyanine (ZnPC). The effects of liposome size, the hydrophobic (O₂(¹δ_g)) quencher, β-carotene, and hydrophilic O₂(¹δ_g) quenchers upon the amount of 6β-CHP formed were determined and interpreted in terms of a one dimensional model of ₂(¹δ_g) quenching and diffusion. The model correctly predicted (1) that the amount of 6β-CHP was increased with increasing liposome size, (2) that P-carotene was more effective at reducing 6β-CHP formation in 400 nm diameter liposomes than 100 nm diameter liposomes and (3) that the hydrophilic quencher, water, was also more effective in large liposomes than in small liposomes.
The hydrophobic quencher, β-carotene, was more effective at reducing the formation of 6β-CHP than at reducing the 1270 nm O₂(¹δ_g) emission. This difference was found to be due to the size distribution present in the liposome preparations because the difference between the 6β-CHP data and the 1270 nm emission data was much smaller in liposome preparations with a narrow size distribution. When a significant size distribution was present, the 6β-CHP data were weighted more heavily with large-diameter liposomes, while the 1270 nm emission data were weighted more heavily with small-diameter liposomes.     

Excellent photocatalytic reduction of nitroarenes to aminoarenes by BiVO4 nanoparticles grafted on reduced graphene oxide (rGO/BiVO4)

A novel heterogeneous composite material based on reduced graphene oxide (rGO) and bismuth vanadate (BiVO₄) was prepared and characterized by various techniques such as powder XRD, HRTEM, EADX, UV–Vis‐DRS, FT‐IR, Raman, BET and XPS analyses. The characterization results reveal that the rGO well decorated by BiVO₄. The electrochemical impedance spectroscopy (EIS) shows the increasing of charge transfer of rGO/BiVO₄ in presence of light irradiation. In this research, the pure BiVO₄ and rGO/BiVO₄ composite have been explored for photocatalytic reduction of nitroarenes. Among the prepared nanocomposites, rGO loaded with 10% BiVO₄ catalyst (noted as rGO/BiVO₄–10%) shows the best performance for the photo‐reduction of various nitroaromatic molecules to their corresponding amine compounds under visible‐light irradiation at room temperature. The catalyst exhibited in particular excellent photocatalytic activity for the conversion of 1,4‐dinitrobenzene to 4‐nitroanilline (100% conversion) in 20 min, 4‐chloronitrobenzene to 4‐chloroaniline and 2‐nitrophenol to 2‐aminophenol (100% conversion) in only 30 min. In addition, the conversion of 4‐bromonitrobenzene, 4‐iodonitrobenzene to their corresponding amine compounds (100% conversion) was achieved in 60 min. The catalyst was recovered for several times and reused without decreasing of its efficiency.     

Imidazole-functionalized magnetic Fe3O4 nanoparticles: an efficient,green, recyclable catalyst for one-pot Friedländer quinoline synthesis

Research on Chemical Intermediates - An efficient and cost-effective procedure for preparation of Fe3O4 nanoparticles and supported Brønsted acidic ionic liquid...     

A novel Raman spectroscopic approach to identify polymorphism in leflunomide: a combined experimental and theoretical study

Polymorphism is an important characteristic which affects the activity, solubility and other physical properties of a compound and can be induced by varying temperature, pressure and solvent. The presence and conversion of α to β polymorphic forms of an anti‐rheumatic drug leflunomide have been studied by temperature‐dependent and in situ Raman observations. Both α and β polymorphs were found to co‐exist in the temperature interval 367–372 K. The α form alone exists below 367 K and the β form alone above 373 K. The CO stretching band clearly demonstrates the α → β conversion because of breaking of N–H···O bond and formation of N–H···N bond. On cooling the Raman spectra suggest the irreversibility of this conversion. Thermodynamic stability, crystal parameters and surface morphology of both forms in the leflunomide powder used for the present study have been verified by differential scanning calorimetry, X‐ray powder diffraction and scanning electron microscopy. Copyright © 2015 John Wiley & Sons, Ltd.     

Enhanced photoreduction of water catalyzed by a cucurbit[8]uril-secured platinum dimer

A cucurbit[8]uril (CB[8])-secured platinum terpyridyl chloride dimer was used as a photosensitizer and hydrogen-evolving catalyst for the photoreduction of water. Volumes of produced hydrogen were up to 25 and 6 times larger than those obtained with the corresponding free and cucurbit[7]uril-bound platinum monomer, respectively, at equal Pt concentration. The thermodynamics of the proton-coupled electron transfer from the Pt(ii)–Pt(ii) dimer to the corresponding Pt(ii)–Pt(iii)–H hydride key intermediate, as quantified by density functional theory, suggest that CB[8] secures the Pt(ii)–Pt(ii) dimer in a particularly reactive conformation that promotes hydrogen formation.

The cucurbit[8]uril macrocycle can secure a platinum terpyridyl complex into a particularly reactive dimer that catalyzes the photoreduction of water.     

Green synthesis of amorphous calcium phosphate nanopowders using Aloe Vera plant extract and assessment of their cytotoxicity and antimicrobial activities

Journal of Sol-Gel Science and Technology - Considering the existing various sources that can supply the nano-biomaterials that are related to the Amorphous Calcium Phosphate (ACP) family, Aloe...     

Efficient and recyclable novel Ni‐based metal–organic framework nanostructure as catalyst for the cascade reaction of alcohol oxidation–Knoevenagel condensation

A novel Ni‐based metal–organic framework (Ni‐MOF) with a Schiff base ligand as an organic linker, Ni₃(bdda)₂(OAc)₂?6H₂O (H₂bdda = 4,4′‐[benzene‐1,4‐diylbis(methylylidenenitrilo)]dibenzoic acid), was synthesized and characterized using powder X‐ray powder diffraction, thermogravimetric analysis, Brunauer–Emmett–Teller measurements, inductively coupled plasma atomic emission spectroscopy, transmission electron microscopy, elemental analysis and Fourier transform infrared spectroscopy. The synthesized Ni‐MOF exhibited a high catalytic activity in benzyl alcohol oxidation using tert‐butyl hydroperoxide under solvent‐free conditions. Also, the efficiency of the catalyst was investigated in the cascade reaction of oxidation–Knoevanagel condensation under mild conditions. The Ni‐MOF catalyst could be recovered and reused four times without significant reduction in its catalytic activity.     

Damage detection in structures using modified back-propagation neural networks

A nonparametric structural damage detection methodology based on neural networks method is presented for health monitoring of structure-unknown systems. In this approach appropriate neural networks are trained by use of the modal test data from a ‘healthy’ structure. The trained networks which are subsequently fed with vibration measurements from the same structure in different stages have the capability of recognizing the location and the content of structural damage and thereby can monitor the health of the structure. A modified back-propagation neural network is proposed to solve the two practical problems encountered by the traditional back-propagation method, i.e., slow learning progress and convergence to a false local minimum. Various training algorithms, types of the input layer and numbers of the nodes in the input layer are considered. Numerical example results from a 5-degree-of-freedom spring-mass structure and analyses on the experimental data of an actual 5-storey-steel-frame demonstrate that neural-networks-based method is a robust procedure and a practical tool for the detection of structural damage, and that the modified back-propagation algorithm could improve the computational efficiency as well as the accuracy of detection Project supported by the National Natural Science Foundation of China (No. 59908003) and the Natural Science Foundation of Hubei Province (No. 99J035).