Effect of carbon steel microstructures and molecular structure of two new Schiff base compounds on inhibition performance in 1 M HCl solution by EIS

In this investigation, attempts have been made to study the inhibitive effect of N,N′-ortho-phenylen acetyle acetone imine (S1) and 4-[(3-{[1-(2-hydroxy phenyl) methylidene] amino} propyl] ethanemidol]-1,3-benzenediol (S2) in the concentration range of 50–400 ppm for mild steel with two different microstructures resulted from two different heat treatments (annealed (A) and quenched and tempered (Q&T)) in 1 M hydrochloric acid by ac impedance spectroscopy. The tests were conducted in acid solutions in the absence and presence of different concentrations of S1 and S2 Schiff bases for both microstructures. A sole time constant was observed from Bode-phase angle plots in the presence of inhibitors which reveals that the action of inhibitors is through adsorption on the surface. The charge transfer resistance and inhibition efficiency increases with the increase in Schiff bases concentration for both microstructures. The perlite samples in the absence of inhibitors in 1 M hydrochloric acid indicated slightly less corrosion than martensite ones, which was because of more protective oxide layers. Furthermore in the presence of S1 and S2, these samples showed better adsorption than martensite one. Schiff base S1 showed a better inhibition against corrosion in comparison with S2. Both S1 and S2 adsorbed on steel surface according to a Langmuir adsorption isotherm model. The associated Gibbs free energies for S1 on both microstructures are more than S2.     

Post operative psychosis

The dimerization of the variable fragment of the Bence-Jones protein Au was examined in phosphate buffers at pH 6.8-6.9 And ionic strength of 0.1 M or 0.2 M at 20 degrees C. The dimerization constant was about 1 X 10(5) M-1. The reaction enthalpy was positive and the process was entropy driven. The association and dissociation rate constants were 9 X 10(6) M-1 s-1 and 1.5 X 10(2) s-1 respectively. Temperature-jump experiments exhibited the presence of two isomers of the dimer, which are present at equilibrium in a ratio of about 1:1. Isomerization occurred with a half-life of about 0.1 s.     

An investigation into the thermal transport properties of PP/EPDM/clay nanocomposites using a new combined experimental/numerical method

This research work is devoted to the study of the thermal transport properties of nanocomposites based on PP/EPDM/Clay (Polypropylene/Ethylene Propylene Diene Monomer/Clay). Six different formulations were designed and the corresponding nanocomposites (with 0, 2, 4 and 6% of clay) were prepared via melt mixing. To achieve the goals, densities, specific heat capacities and thermal conductivities were measured as function of temperature and nanocomposites compositions. A new and novel methodology was developed to determine the thermal conductivity which was based on an inverse heat transfer problem. First, assuming a linear relationship for thermal conductivity, the transient heat transfer equation in a solid specimen was numerically solved. The obtained temperature profile was used as the input to an optimisation technique based on genetic algorithm and the parameters of the thermal conductivity relationship were found. The results showed that the specific heat increases both with increasing of temperature and clay contents. It is also increased with the addition of the rubber to the blend. In all samples, the thermal conductivity decreases with increasing of temperature with a linear relationship. In addition, at relatively constant ratios of PP/EPDM, thermal conductivity of nanocomposite and its sensitivity increase with temperature rise. Moreover, at constant value of clay content, the thermal conductivity is decreased with increase of rubber content. The explanations to above findings were also presented and discussed.     

Supertough (Polyamide 6)/(acrylonitrile butadiene rubber) nano alloy through in situ polymerization of caprolactam in the presence of acrylonitrile butadiene rubber nanophase

In this work, polymerization of caprolactam (CL) was carried out in the presence of acrylonitrile butadiene rubber (NBR) during the reactive melt‐mixing process. During shear mixing, NBR particles swelled and dissolved in the molten CL, which led to separation and distribution of rubber particles to nanoscale in the dissolution stage. Then, in an internal mixer, supertough Polyamide 6 was prepared via melt polymerization of CL/NBR mixture, sodium caprolactam as a catalyst, and hexamethylene diisocyanate as an activator. The effects of various concentrations of catalyst and activator on the initiation time of the reaction were determined. Physical and mechanical properties of different formulations prepared via reactive melt blending were determined by tensile and impact measurements, differential scanning calorimetry, Fourier‐transform infrared spectroscopy, X‐ray scattering techniques, transmission electron microscopy, and dynamic mechanical thermal analysis. Experimental results showed that a recipe with 3% nitrile rubber in a CL/NBR mixture enhances the physical and mechanical properties the best, compared with other formulations. This condition led to the formation of NBR nanospheres during melt polymerization of Polyamide 6 as well. J. VINYL ADDIT. TECHNOL., 21:116–121, 2015. © 2014 Society of Plastics Engineers     

Characterization of the Ion Beam Current Density of the RF Ion Source with Flat and Convex Extraction Systems

Silicon - The characterization of ion beam current density distribution and beam uniformity is crucial for improving broad-beam ion source technologies. The design of the broad ion beam extraction...     

Electrochemical behavior of organic and inorganic complexes of Zn(II) as corrosion inhibitors for mild steel: Solution phase study

This work intends to study inhibitive performance of organic and inorganic complexes of Zn(II) using electrochemical techniques along with surface analysis. In this regard, inorganic zinc aluminum polyphosphate pigment as modified zinc phosphate and zinc acetylacetonate and benzimidazole mixture representing organic replacement of zinc phosphate were employed. Through taking advantage of electrochemical impedance spectroscopy and DC polarization, two mentioned approaches were indicated to be efficient. Charge transfer resistance and corrosion current density values exhibited superiority of zinc aluminum polyphosphate and mixture of zinc acetylacetonate and benzimidazole compared to zinc phosphate and also zinc acetylacetonate and benzimidazole as individual inhibitors. Corrosion inhibition efficiencies calculated based on charge transfer resistance in consistent with those calculated from corrosion current density showed the following sequence; zinc aluminum polyphosphate > mixture of zinc acetylacetonate and benzimidazole > zinc acetylacetonate > zinc phosphate > benzimidazole. Showing film formation, surface analysis SEM/EDX confirmed the results obtained by electrochemical methods.     

Intelligent and independent processes for overcoming big graphs

Poor locality as a natural property of graph data structures causes enormous amount of network traffic in large-scale distributed graph processing systems. Moreover, data transmission through the network is one of the most expensive operations in a distributed system. Therefore, reduction of network usage is highly required by a new graph computational model. In this paper, increasing the degree of machine independency has been considered a key factor of network traffic reduction. The proposed system benefits from a three-layered computational model to perfectly leverage the power of local information as much as possible. Moreover, this model simultaneously takes the advantages of both message-based and shared-state communication paradigms. Vertices can read and update values of others in the lowest layer directly, while they must send messages in other layers. By the use of memorization techniques, the proposed model introduces a new kind of intelligence that has encouraging effects on removing useless communications. Distinctive results of our experiments confirm significant improvements of the proposed model in relation to the previous systems like Pregel, GPS, and Blogel, as well as ExPregel. The results also show that the overhead of making processes independent along with intelligent is negligible in comparison with the cost of additional network communications.     

Microgel electrospinning: A novel tool for the fabrication of nanocomposite fibers

We demonstrate for the first time herein that electrospinning of soluble crosslinked polymer particles (microgels) is feasible and that it can be used to obtain micron-sized fibers from these macromolecules. Most notably, the electrospinning approach is found to be successful also in the case of microgels bearing metal nanoclusters, and allows to prepare composite fibers containing a homogeneous dispersion of metal nanoparticles without alterations in the nanoparticle size and size distribution. Given the broad applicability of microgels as exotemplates and stabilisers for inorganic nanoparticles, the proposed preparation method stands out as a novel, general approach for the synthesis of potentially useful composite fibers containing inorganic nanoparticles.     

Degenerate four-wave mixing in the copolymer of phenylacetylene and norbornene

The results of optical phase conjugation experiments in a degenerate four-wave mixing geometry are reported at 532 nm in a new block copolymer of phenylacetylene and norbornene. The copolymer sample with an equal number of repeating units for the two homopolymers prepared by sequential addition of the monomers gives a third-order susceptibility value which is twice that of the homopolymer of phenylacetylene.     

Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks

This research aims at improving the methods of prediction of hazardous geotechnical structures in the front of a tunnel face. We propose and showcase our methodology using a case study on a water supply system in Cheshmeh Roozieh, Iran. Geotechnical investigations had previously reported three measurements of the newly established method of TSP-203 (Tunnel Seismic Prediction) along 684 m of the 3200 m long tunnel up to a depth of 600 m. We use the results of TSP-203 in a trained artificial neural network (ANN) to estimate the unknown nonlinear relationships between TSP-203 results and those obtained by the methods of Rock Mass Rating classification (RMR – treated here as real values). Our results show that an appropriately trained neural network can reliably predict the weak geological zones in front of a tunnel face accurately.