Facile growth of novel morphology correlated Ag/Co-doped ZnO nanowire/flake-like composites for superior photoelectrochemical water splitting activity

In this paper, novel morphology correlation between silver nanowires (AgNWs) and cobalt (Co)-doped ZnO (Co-ZnO) flake-like thin films (nanowire/flake-like) has been proposed for enhanced photoelectrochemical (PEC) water splitting activity. Here in, high-quality AgNWs/Co-ZnO heterostructures enabled superior visible light water splitting activity compared to the pure ZnO and AgNWs/ZnO. To address the strategic effect of AgNWs coupling and transition metal (Co-2?at%) doping into the ZnO host lattice, we have carried out the X-ray diffraction, field emission scanning microscopy, X-ray photoelectron spectroscopy, UV–Vis transmittance, water contact angle and PEC analyses. In this way, PEC water splitting activity was mainly examined by linear sweep voltammetry (I-V), amperometric I-t and photoconversion efficiency (η) studies. The experimental results provide clear evidence of morphology correlation between AgNWs and Co-ZnO flake-like structures for strong visible light absorption. Specifically, AgNWs/Co-ZnO composites exhibited significant enhancement in the photocurrent density (7.0?×?10^?4 A/cm²) than AgNWs/ZnO (3.2?×?10^?4 A/cm²) and pure ZnO (1.5?×?10^?6 A/cm²). As a result, detailed AgNWs/Co-ZnO geometry has great potential for photoconversion efficiency (0.73%). In a word, the merits of controllable AgNWs/Co-ZnO heterostructure are proposed to improve the visible light harvesting and charge carrier generation for energy conversion devices.     

Multiwalled Nanotubes: Their Formation by Irradiating Graphite with High Doses of Electrons

We report the production of carbon nanotubes by high dose of electron irradiation. The irradiation was performed with a 2 MeV Van de Graaff accelerator, while the irradiation conditions were the following: voltage 1.3 MeV, current 5 µA, dose rate 25 kGy/min, and total dosage 1000 kGy. The samples were analyzed in a high-resolution transmission electron microscope. The main features observed on the samples, were huge nanotubes of several nanometer long and few nanometer wide, which are capped at one end. It is good to point out, that at this level of irradiation, we were not able to find either onion-like or particle structures throughout the material, as it is usual in similar hexagonal structures. This behavior could be attributed to the level of irradiation used to create the nanotubes under investigation.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.     

Fractionation of transgenic corn for recovery of recombinant enzymes

Two germ-separation methods, dry-milling and density separation by flotation, were evaluated for recovering recombinant β-glucuronidase (rGUS) that accumulated primarily in the germ of transgenic corn. The dry-milling process consisted of (i) seed tempering, (ii) degerming with a horizontal-drum degermer/dehuller, (iii) particle size fractionation with standard sieves, (iv) germ and endosperm separation by roller milling and sifting, and (v) removal of hulls by aspiration. Sieves nos. 5, 6, and 7 retained the majority of germ, and subfractions from these sieves were pooled as a germ-rich fraction. Mass balances showed that the germ-rich fraction, which constituted 17% of the total dry-milled corn weight, contained 49% of rGUS activity and 64% of the total recoverable oil. Germ fractionation by flotation was tested as a proof-of-concept method aimed at separating corn fractions based on their difference in specific gravity (sp gr). The process consisted of impact-grinding of corn kernels followed by density separation using 1.15 or 1.3 specific gravity sodium nitrate solution. The oil-containing germ fraction floated, whereas the heavier endosperm fraction sedimented. The flotation method was simpler and resulted in higher enzyme recovery, that is, the germ-rich fraction was 20% (w/w) of the initial corn weight, and accounted for 80% of rGUS activity and 77% of total oil. The sodium nitrate solution did not have an adverse effect on the enzyme activity.     

Noise analysis of robot manipulator using neural networks

Due to a lot of robot manipulators application in industry, low noise degree is very important criteria for robot manipulator's joints. In this paper, joint noise problem of a robot manipulator with five joints is investigated both theoretically and experimentally. The investigation is consisted of two steps. First step is to analyze the noise of joints using a hardware and software. The hardware is a part of noise sensors. The second step; according to experimental results, some neural networks are employed for finding robust neural noise analyzer. Five types of neural networks are used to compare each other. From the results, it is noted that the proposed RBFNN gives the best results for analyzing joint noise of the robot manipulator.     

Derivation of empirical equations for neutronic performance in a thorium fusion breeder with various coolants using regression analysis

In this paper, regression analyses (RA) are presented for the neutronic calculation of ThO₂ mixed ²⁴⁴CmO₂ fuel with different neutronic parameters for various coolants, natural lithium, Li₂₀Sn₈₀ and Flinabe, respectively. The tritium breeding ratio (TBR), energy multiplication factor (M), total fission rate (Σ_f) and ²³²Th(n, γ) reaction is computed by XSDRNPM. In addition, this numerical results are estimated by RA depends on neutronic parameters and the empirical equations for neutronic performance are acquired. The results obtained by using XSDRNPM and the results of the RA, obtained empirical equations, are compared. The empirical equations indicate that RA can successfully be used for the prediction of the neutronic performance parameters in the hybrid reactor with a high degree of accuracy. In addition, correlation matrix is calculated to determined statistical relationships between variables TBR, M, Σ_f, and ²³²Th(n, γ).     

Approximate periodic solutions for the Helmholtz-Duffing equation

Approximate periodic solutions for the Helmholtz-Duffing oscillator are obtained in this paper. He’s Energy Balance Method (HEBM) and He’s Frequency Amplitude Formulation (HFAF) are adopted as the solution methods. Oscillation natural frequencies are analytically analyzed. Error analysis is carried out and accuracy of the solution methods is evaluated.     

Variational sensitivity analysis and design optimization

Variational method (VM) is employed to derive the co-state equations, boundary (transversality) conditions, and functional sensitivity derivatives. The converged solutions of the state equations together with the steady state solution of the co-state equations are integrated along the domain boundary to uniquely determine the functional sensitivity derivatives with respect to the design function. The application of the variational method to aerodynamic shape optimization problems is demonstrated on internal flow problems at supersonic Mach number range of 1.5. Optimization results for flows with and without shock phenomena are presented. The study shows that while maintaining the accuracy of aerodynamical objective function and constraint within the reasonable range for engineering prediction purposes, variational method provides a substantial gain in computational efficiency, i.e., computer time and memory, when compared with the finite difference computations.