An Investigation of the Effect of Sintering Conditions on the Mechanical Behavior of Electroplated Nickel Foams

Metallurgical and Materials Transactions B - This study investigates the effect of sintering temperature on the compression strength of nickel foams in an inert atmosphere. The nickel foams were...     

Synthesis,characterization, and optimization of α‐Fe2O3/silica nanocomposites using homogenous precipitation method

The purpose of this study was to systematically synthesize and characterize the high surface area 10 wt% nanocomposites of α‐Fe₂O₃ (hematite)/silica using a simple and economically effective homogenous precipitation (HP) route via Response Surface Method combined with Central Composite Design (CCD). Accordingly, the RSM‐CCD approach including 20 experiments was designed to investigate the effects of three factors including concentration of iron chloride solution, pH and calcinations temperature on the final surface area of α‐Fe₂O₃/silica nanocomposites. The optimum surface area was 373 m²/g at the condition including iron chloride concentration of 0.018 mol/L, pH=8.95, and calcination temperature of 573°C.     

Simultaneous Learning of Nonlinear Manifolds Based on the Bottleneck Neural Network

Manifold learning methods are important techniques for nonlinear extraction of high-dimensional data structures. These methods usually extract a global manifold for data. However, in many real-world problems, there is not only one global manifold, but also additional information about the objects is shared by a large number of manifolds. These manifolds can share information for data reconstruction. To simultaneously extract these data manifolds, this paper proposes a nonlinear method based on the deep neural network (NN) named nonlinear manifold separator NN (NMSNN). Unlike unsupervised learning of bottleneck NN, data labels were used for simultaneous manifold learning. This paper makes use of NMSNN for extracting both expression and identity manifolds for facial images of the CK+ database. These manifolds have been evaluated by different metrics. The identity manifold is used for changing image identity. The result of identity recognition by K-nearest neighbor classifier shows that virtual identities are exactly sanitized. The virtual images for different expressions of test subjects are generated by expression manifold. The facial expression recognition rate of 92.86 % is achieved for virtual expressions of test persons. It is shown that NMSNN can be used to enrich datasets by sanitizing virtual images. As a result, 8 and 19 % improvements are gained in the face recognition task by a single image of each person on CK+ and Bosphorus databases, respectively.     

Modification and optimization of benzene alkylation process for production of ethylbenzene

In this paper, an industrial ethylbenzene production unit has been simulated and the results are compared against five-day experimental data. According to prevailing unit condition, i.e. recycled ratio of benzene, benzene selectivity, and energy consumption, the unit is not working under its optimum conditions for minimum cost of ethylbenzene production. In the current design, high amount of benzene recycle (6:1) causes to have an additional cost due to fractionation of ethylbenzene from benzene. A new approach is proposed to modify the benzene alkylation process and reduce the unit's energy consumption. In the newly designed scheme, two double-bed alkylation reactors converted into four single-bed reactors. The amount of injected ethylene, alkylation reactors temperature, and recycled stream are regulated as adjustable parameters for the optimization of the process. In the modified process, the reflux ratio reduced to 1.87 and the benzene selectivity increased. The optimized process shows a considerable decrease in the unit's energy consumption in compare to the current process. Also, the mass fraction of ethylbenzene would reach to 99.12% of purity before entering to the transalkylation reactor for further purification. Therefore, if the presented purity is acceptable for the final application, the transalkylation reactor could be eliminated from the new design.     

Three​-dimensional porous poly(ε-caprolactone)/beta-tricalcium phosphate microsphere-aggregated scaffold for bone tissue engineering

In this study, porous scaffolds made of polycaprolactone (PCL)/β-tricalcium phosphate (BTCP) biocomposite were fabricated for bone tissue engineering (BTE) applications. The microsphere-aggregated scaffolds were prepared with various BTCP concentrations (10wt%, 20wt%, 50wt%) by the freeze-drying method. The porosity of obtained microsphere-aggregated scaffolds with various pore sizes was 80–85%, where this value was about 70% for the PCL/BTCP (50) sample with no microsphere formation. The results indicated that adding BTCP has enhanced mechanical strength, and the mineralization of PCL/BTCP composite scaffolds has been increased compared to the pure PCL scaffolds in simulated body fluid (SBF). The adhesion and proliferation of mouse bone marrow mesenchymal stem cells (mMSCs) seeded onto PCL/BTCP scaffolds were enhanced compared to the PCL. In addition, in terms of differentiation, the incorporation of BTCP led to increasing the mineral deposition and alkaline phosphatase activity of mMSCs. The synergistic effect of using microsphere-aggregated scaffolds along with BTCP as a reinforcing agent in PCL biocomposite showed that these porous biocomposite scaffolds have the potential application in BTE.     

Predicting rail defect frequency: An integrated approach using fatigue modeling and data analytics

In maintenance planning of rail track, it is imperative to assess the potential and frequency of rail defects. Although this problem has been mainly studied in the literature by either data‐driven or mechanic‐based models, in the present study a new method is proposed to account for the strengths of both approaches in a single model. The envisaged model incorporates fatigue crack growth model, through Finite Element Modeling (FEM), into Approximate Bayesian Computation (ABC) framework. The method is applied to the prediction of rail defect frequency for transverse defects obtained from a US Class I Railroad. The results of the proposed model show that inducing the mechanics of rail defects into a data‐driven model outperforms the traditional pure data‐driven models by over 20%. The outcome of this study, along with necessary future developments to broaden the scope of applicability of the method, will benefit railroad existing practice in capital and maintenance planning.     

Fabrication of carbon nanotube and dysprosium nanowire modified electrodes as a sensor for determination of curcumin

Two sensitive sensors for determination of curcumin (CM) were described. CM can be detected using multiwall carbon nanotube (MWCNT)-modified electrodes and dysprosium nanowire carbon paste electrode using the technique of adsorptive stripping voltammetry (AdSV) in stationary solution and the fast Fourier transform voltammetry at the flowing solution. Both electrodes did show less passivation effect that occurs on the unmodified electrodes and displayed better stability and reproducibility. This electrode enabled selective determination of CM in the presence of interfering species. Under optimized conditions, CM could be detected over a linear range with a detection limit of 5.0 × 10⁻⁹ mol L⁻¹ and 5.0 × 10⁻¹⁰ mol L⁻¹ for the traditional square wave and fast Fourier transform square wave voltammetry (FFTSWV) with RSD between 0.2 and 0.5%. Comparison with other reported methods showed these studies are about 100 times more sensitive than previous ones. Good selectivity and high sensitivity obtained by Square wave voltammetry can open new possibilities of direct CM determination.     

Effect of mineral fillers on physico-mechanical properties and heat conductivity of carbon black-filled SBR/butadiene rubber composite

Iranian Polymer Journal - Heat conductivity, curing rate and fatigue crack growth properties of carbon black-filled styrene butadiene rubber (SBR)/butadiene rubber (BR) blend that is frequently...