The microwave-assisted synthesis and characterization of novel metal-free and metallophthalocyanines peripherally fused to four 13-membered diazadithia macrocycles

The novel tetrasubstituted metal-free phthalocyanine and metallophthalocyanines 7, bearing four 13-membered diazadithia macrocycle moieties at peripheral positions were synthesized by cyclotetramerization of the corresponding phthalonitrile derivative in a multi-step reaction sequence. The metal-free phthalocyanine was synthesized by microwave irradiation of the corresponding dicyano compound in 2-(dimethylamino)ethanol for 10 min. Metallophthalocyanines were obtained from the respective dicyano derivative and corresponding anhydrous metal salts NiCl₂ and ZnCl₂, CoCl₂, CuCl₂, respectively, by microwave irradiation in 2-(dimethylamino)ethanol at 175 °C, 350 W for 10 min. The new compounds were characterized by a combination of IR, ¹H NMR, ¹³C NMR, UV–vis, elemental analysis and MS spectral data.     

Zinc oxide nanowire enhanced multifunctional coatings for cotton fabrics

Zinc oxide (ZnO) nanowires have been grown on cotton fabric to impart self-cleaning, superhydrophobicity and ultraviolet (UV) blocking properties. ZnO nanowires were grown by the microwave assisted hydrothermal method and subsequently functionalized with steraic acid to obtain a water contact angle of 150°, showing their superhydrophobic nature which is found to be stable up to 4 washings. UV protection of the resulting cotton fabric was also examined and significant decrease in the transmission of UV range was observed. Self-cleaning activity of the ZnO nanowire coated cotton fabric was also studied and it showed considerable degradation of methylene blue under UV light irradiation. These results suggest that ZnO nanowires could form ideal multifunctional coatings for textiles.     

Mechanical and Thermal Properties of 3D-Printed Biocomposites of Polylactic Acid and Thermally Modified Wood Flour with Silver Nanoparticles

This study investigates the effects of thermal treatment and silver nanoparticles on the mechanical and thermal properties of additively manufactured wood/polylactic acid (PLA) specimens. The 5 and 10 wt% additions of the wood flour into the PLA matrix are done in the twin screw extruder. The test specimens are produced from the filaments with a diameter of 1.75 mm using 3D printer. The mechanical properties of the 3D-printed wood/PLA specimens are affected by the wood flour content, thermal treatment of wood, and silver nanoparticles. The 3D-printed neat PLA specimens have better mechanical properties than the wood flour-filled PLA specimens. The bending strength of the specimens enhances with increasing wood flour content while the tensile strength decreases. The increases in the mechanical properties reveal that the thermal treatment of the wood improves the compatibility between wood and PLA matrix, which hereby improves the interfacial adhesion. The addition of the silver nanoparticles positively affects the mechanical properties and it is more compatible with thermally modified wood particles as compared to the untreated wood particles.     

Reduction of methomyl and acetamiprid residues from tomatoes after various household washing solutions

The removal of pesticide residues is essential in order to control and reduce the harmful effects of pesticides commonly used in agriculture. The aim of this study was to investigate the effectiveness of 18 different household washing solutions for the reduction of methomyl and acetamiprid residues from tomatoes. These basic household solutions were used to prepare washing solution to remove pesticides. During this study, tomatoes were divided into two groups: in the first group, tomatoes were treated with the pesticides and the second group was washed with household solutions (distilled water, acetic acid, sodium chloride, ethanol, sodium bicarbonate, hydrogen peroxide, sodium hypochlorite, and liquid soap, lemon juice, mineral water, zam-zam water, whey, milk, and their combinations) after treating with pesticides. The pesticides residues in all samples were extracted by the QuEChERS technique, analyzed by LC-MS/MS. The results showed significant reduction in residues of all washed groups compared with the control (p < 0.01). Acetic acid, sodium chloride+sodium bicarbonate, zam-zam water, acetic acid+sodium chloride, sodium hypochlorite, milk, acetic acid+sodium chloride+sodium bicarbonate, liquid whey, liquid soap, lemon juice, ethanol, and sodium bicarbonate were found to remove more than 50% of both pesticide residues. The effectiveness of washing solutions was different for every type of pesticide. In the same way, blend of washing solutions has shown a synergetic effect on the removal of pesticides and was more effective compared to one solution. This research has raised the potential use of household solutions easily found at home that could be used to remove pesticide residues.     

Controlling phosphonic acid substitution degree on proton conducting polyphosphazenes

This paper is to describe the development of a synthetic strategy for the preparation of phosphonic acid functionalized poly(aryloxyphosphazene) membranes with different substitution degree of phosphonic acid. Synthesized polymers have been characterized by standard spectroscopic techniques; FT-IR, ¹H, ³¹P and ¹⁹F NMR and element analysis. Proton conductivities of phosphonic acid substituted polymers have been investigated with impedance spectroscopy at different temperatures. Furthermore, the correlations of the proton conductivity and ion exchange capacity with the substitution degree of phosphonic acid have also been investigated. Thermal properties and water uptake properties of the polymers are also investigated. It is found that the proton conductivity and initial decomposition temperature of the polymers increases up to an appropriate amount of phosphonic acid substitution degree, which can be a useful PEM candidate for fuel cells.     

The determinants of agglomeration for the manufacturing sector in the Istanbul metropolitan area

This paper examines the determinants of agglomeration by seeking the patterns of urbanization economies and localization economies in the Istanbul metropolitan area (IMA). The research is developed in two steps. The first step is the measurement of concentration levels for the IMA; the Ellison–Glaeser localization index (EGI) is applied to the 22 manufacturing sector (2-digit level) at three different geographical levels. The second step is to determine the structural pattern of agglomeration. By regressing the Ellison–Glaeser localization index values on proxies for urbanization and localization economies, the determinants of agglomeration are demonstrated. The determinants of agglomeration are estimated by 12 different two-stage OLS regressions. While three of these regressions represent the agglomeration factors at each geographical level, the other nine equations represent the agglomeration factors at the industry-specific level. The results suggest that urbanization economies have a strong effect on agglomeration both at the geographical level and industry-specific level. It is noticed that density, market area potential, and labor market potential are the most effective proxies for urbanization economies on agglomeration. The effects of localization economies are consistent with Marshall for labor pooling and manufactured input. However, the results do not provide any evidence that knowledge spillovers have an influence on agglomeration in this case.     

Combined neural network model to compute wavelet coefficients

Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients.     

Modified mixture of experts employing eigenvector methods and Lyapunov exponents for analysis of electroencephalogram signals

Abstract: The use of diverse features in detecting variability of electroencephalogram (EEG) signals is presented. The classification accuracies of the modified mixture of experts (MME), which was trained on diverse features, were obtained. Eigenvector methods (Pisarenko, multiple signal classification – MUSIC, and minimum-norm) were selected to generate the power spectral density estimates. The features from the power spectral density estimates and Lyapunov exponents of the EEG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the EEG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on the diverse features achieved high accuracy rates (total classification accuracy of the MME is 98.33%).