Physical properties of fish gelatin-based bio-nanocomposite films incorporated with ZnO nanorods

Well-dispersed fish gelatin-based nanocomposites were prepared by adding ZnO nanorods (NRs) as fillers to aqueous gelatin. The effects of ZnO NR fillers on the mechanical, optical, and electrical properties of fish gelatin bio-nanocomposite films were investigated. Results showed an increase in Young''s modulus and tensile strength of 42% and 25% for nanocomposites incorporated with 5% ZnO NRs, respectively, compared with unfilled gelatin-based films. UV transmission decreased to zero with the addition of a small amount of ZnO NRs in the biopolymer matrix. X-ray diffraction showed an increase in the intensity of the crystal facets of (10^ī1) and (0002) with the addition of ZnO NRs in the biocomposite matrix. The surface topography of the fish gelatin films indicated an increase in surface roughness with increasing ZnO NR concentrations. The conductivity of the films also significantly increased with the addition of ZnO NRs. These results indicated that bio-nanocomposites based on ZnO NRs had great potentials for applications in packaging technology, food preservation, and UV-shielding systems.     

Effect of pH on Aerobic Granulation and Treatment Performance in Sequencing Batch Reactors

Two sequencing batch reactors were operated to investigate the effect of influent alkalinity and reactor pH on aerobic granulation. In the first reactor R1 with high influent alkalinity the pH was adjusted in the neutral range, and in the second reactor R2 with low alkalinity the pH was held within the acidic range. The R1‐dominating species were bacteria and the appearance time of granules was three weeks after reactor start‐up. On the other hand, the acidic environment of R2 provided favorable conditions for fungal growth, and rapid granule formation occurred within the first week of operation. The varying microbial structure of granules resulted in different reactor performance in terms of evolution time and morphology of granules, suspended solids in the reactors, settling ability of granules, effluent quality of treated wastewater, and physical strength of the granules.     

Operation simulation of a recycled electrochemical ozone generator using artificial neural network

The present work has focused on the modeling and simulation of a recycled ozone generator system via electrochemical oxidation of water. To produce ozone, a Pyrex glass electrochemical reactor, comprised of two separate half-cell by Nafion 117 membrane was applied. The applied anode and cathode electrodes were Ti/Sn-Sb-Ni and platinized titanium, respectively. The modeling and simulation of the reactor operation were done via artificial neural network (ANN) technique. In this regard, four important operational parameters (i.e. electrolyte concentration, applied voltage, flow rate and electrolysis time) and the generated ozone concentration were considered as the independent inputs and the network output, respectively. To find out the best model, six numbers of three-layered ANNs with different functions were constructed and optimized. Best simulation was related to a model, consist of Levenberg–Marquardt Back propagation learning algorithm (trainlm) and tangent sigmoid (tansig) as transfer function in the both hidden and output layers. Also, application of 10 hidden neurons and 80 iterations for the network calibration caused to satisfy the network training while overfitting was prevented. The K-fold cross-validation method, employed for the model evaluation, showed high correlation coefficient (0.9936) and low mean square error (3.58 × 10⁻⁴) for the testing data. Sensitivity analysis indicated order of relative importance the operational parameters on the ozone production as: time > [electrolyte] > voltage > flow rate.     

Design of a general point focusing crystal geometry for x-ray spectroscopy

A nearly exact general equation for geometrical angular deviations from the Bragg angle over entire curved crystal surfaces is derived using a toroidally curvilinear coordinate system and applied on the nine conventional crystal geometries. Although the derived formula confirms Wittry's results for the first five cases, it shows considerable differences for the more important cases, such as 45° point focusing, general point focusing, and Berreman geometries. The effective scattering areas for the mentioned cases have been derived, plotted, and interpreted. A point-to-point focusing crystal geometry is introduced, and it is shown that it approaches Wittry's and spherical plane-spherical Johansson geometries as θ(B) → 90° and θ(B) → 0°, respectively.     

Catalytic NO–H2–CO–O2 reactions over Pt-supported mesoporous yttrium oxide

Catalytic light-off of a stream of NO, H₂, CO in an excess O₂ has been studied over various metal oxides loading 1 wt% Pt. Because a low-surface area Y₂O₃ (<5 m² g⁻¹) was found to exhibit the highest de-NO_x activity, a mesoporous Y₂O₃ was then synthesized from an yttrium-based surfactant mesophase templated by dodecyl sulfate , which was anion-exchanged by acetate (AcO = CH₃COO⁻). The product showed a 3-D mesoporosity with a large surface area (396 m² g⁻¹) and the Pt-supported catalyst achieved much improved light-off characteristics suitable for the low-temperature de-NO_x in the presence of CO and excess O₂.     

Manufacturing cell performance improvement: a simulation study

This paper reports on the study of the “underbody front”-automated welding cell at Opel Belgium, a major automobile manufacturer of General Motors International Operations. It employs the use of simulation in an experimental design framework to identify potential improvements in average daily output through management of buffer sizes at key buffer locations within the cell. Many practical applications of animated computer simulation stop at the modeling and displaying of the process under study. Simulation as a tool for process reengineering or enhancement can only reach its full potential if incorporated in a comprehensive statistical study, so as to attain statistically significant results. The paper also reports on the reactions of, and issues raised by, management when the experimental design methodology was presented as a tool for process enhancement and productivity improvement.     

Correction to: Deposition of bismuth sulfide and aluminum doped bismuth sulfide thin films for photovoltaic applications

Journal of Materials Science: Materials in Electronics -     

In vitro antibacterial activity of ceftazidime,unlike ciprofloxacin,improves in the presence of ZnO nanofluids under acidic conditions

The development of antibiotic resistance among hospital pathogens has provided a great need for new antimicrobial agents. Zinc oxide nanoparticles (ZnO NPs) in combination with various antibiotics can act as a reducing agent for antibiotic resistance. The aim of this study was to investigate the influence and the mechanism of ZnO NPs on the antimicrobial activity of ciprofloxacin (CP) and ceftazidime (CAZ) against Enterococcus faecalis (E. faecalis) and Acinetobacter baumannii (A. baumannii) bacteria in acidic conditions (pH 5.5). ZnO NPs were synthesised using the solvothermal method and characterised. The MIC90 value of ZnO NPs against A. baumannii was 0.25 mg ml⁻¹ and its highest growth‐inhibitory activity was observed at 0.125 mg ml⁻¹ for E. faecalis. The Fourier transform infrared spectroscopy spectra of ZnO NPs treated with antibiotics showed the interaction between ZnO NPs and each of the two antibiotics. ZnO NPs at a sub‐inhibitory concentration had no effect on the antibacterial activity of CP and CAZ against E. faecalis and CP against A. baumannii. The action mechanism of ZnO NPs for enhancing the antibacterial efficacy of CAZ against A. baumannii was evaluated. ZnO NPs caused to increase in the antibacterial activity of CAZ against A. baumannii, possibly through the release of Zn²⁺ and increasing of membrane permeability.Inspec keywords: nanofluidics, antibacterial activity, drugs, nanoparticles, nanomedicine, microorganisms, pH, zinc compounds, II‐VI semiconductors, wide band gap semiconductors, semiconductor growth, X‐ray diffraction, light scattering, scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectra, ultraviolet spectra, visible spectra, DNA, molecular biophysics, biochemistry, atomic absorption spectroscopy, membranes, permeability, nanofabricationOther keywords: in vitro antibacterial activity, ceftazidime, nanofluids, acidic conditions, antibiotic resistance, hospital pathogens, antimicrobial agents, zinc oxide nanoparticles, reducing agent, antimicrobial activity, ciprofloxacin, Enterococcus faecalis bacteria, Acinetobacter baumannii bacteria, pH, solvothermal method, X‐ray diffraction, dynamic light scattering, ultraviolet‐visible spectrum, scanning electron microscopy, transmission electron microscopy, MIC90 value, growth‐inhibitory activity, Fourier transform infrared spectroscopy, subinhibitory concentration, reactive oxygen species measurement, DNA fragmentation, atomic absorption spectroscopy, SEM, membrane permeability, glycerol‐ammonium citrate. mixture, ZnO     

Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models

Forecasting precipitation as a major component of the hydrological cycle is of primary importance in water resources engineering, planning and management as well as in scheduling irrigation practices. In the present study the abilities of hybrid wavelet-genetic programming [i.e. wavelet-gene-expression programming, WGEP] and wavelet-neuro-fuzzy (WNF) models for daily precipitation forecasting are investigated. In the first step, the single genetic programming (GEP) and neuro-fuzzy (NF) models are applied to forecast daily precipitation amounts based on previously recorded values, but the results are very weak. In the next step the hybrid WGEP and WNF models are used by introducing the wavelet coefficients as GEP and NF inputs, but no satisfactory results are produced, even though the accuracies increased to a great extent. In the third step, the new WGEP and WNF models are built; by merging the best single and hybrid models’ inputs and introducing them as the models inputs. The results show the new hybrid WGEP models are effective in forecasting daily precipitation, while the new WNF models are unable to learn the non linear process of precipitation very well.