Mechanical Properties of Uncoated and Aluminide-Coated René 80

Nickel-base superalloys such as René 80 are widely used in manufacturing aircraft turbine blades. They are usually coated in order to increase their wear, oxidation, erosion, and hot corrosion properties against environmental degradation. In this article, the mechanical behavior (tensile and low-cycle fatigue (LCF)) of uncoated and aluminide-coated (CODEP-B) René 80 has been studied at 871 °C and 982 °C. Experimental results show that the tensile properties of coated specimens are relatively lower than those of uncoated ones in the same conditions, but application of coating increases the LCF life of René 80 at T = 871 °C, 982 °C, R = (ε _min/ε _max) = 0, strain rate of 2 × 10⁻³ s⁻¹, and Δε _t  = 0.8 pct. Scanning electron microscopy (SEM) studies of coated specimens at N = Nf show that the nucleation of cracks occurs merely in substrate, but cracks start from the surfaces in uncoated specimens. Transmission electron microscopy (TEM) investigations have been performed on fractured uncoated specimens to evaluate the microstructures at different temperatures. The misfit dislocation, pair dislocations, and cutting of γ′ were observed at T = 871 °C and 982 °C. The TEM studies also showed that at 982 °C stacking fault was observed in γ′ particles.     

Investigation of styrene/1‐hexene copolymerization by homogeneous and heterogeneous bisindenyl ethane zirconium dichloride catalyst system

Homogeneous copolymerization of styrene and 1‐hexene was carried out in toluene at room temperature using bisindenyl ethane zirconium dichloride/methylaluminoxane (MAO). The supported catalyst was prepared with immobilization of Et(Ind)₂ZrCl₂/MAO on silica (calcinated at 500°C) with premixed method. Heterogeneous copolymerization of styrene/1‐hexene with different mole ratios was carried out in the presence of supported catalyst system. The copolymers obtained from homogeneous and heterogeneous catalyst system were characterized by ¹H NMR and ¹³C NMR. Composition of the resulting copolymers was determined by ¹H NMR data. Analysis of ¹³C NMR spectra of obtained copolymers by homogeneous and heterogeneous catalyst systems present isotactic olefin‐enriched copolymers. Molecular weight and thermal behavior of resulting copolymers was investigated. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 4008–4014, 2007     

Influence of aluminide diffusion coating on the tensile properties of the Ni-base superalloy René 80

Ni-base superalloy René 80 is widely used in manufacturing aircraft turbine blades. The service temperature of this alloy is in the range of 760-982 °C. Although this alloy possesses suitable mechanical, oxidation and hot corrosion properties, it is coated in order to increase its wear, oxidation, erosion and hot corrosion properties against harmful environmental service conditions.In this paper the influence of applying diffusion coating (CODEP-B) on the tensile properties of René 80 has been studied in the temperature range of 22-982 °C. Experimental results show that the tensile properties of the coated specimens are relatively lower than that of uncoated ones in the same conditions. But in the service conditions, coating could have some useful and positive effects practically.In aircraft turbines in service conditions, the maximum strain the blades experience is always bellow 1% and in this range of strain no crack initiates and generates on the applied coating surface. Thus in this situation, the coating would certainly have protective properties preventing from oxidation, hot corrosion and erosion of the base metal.     

Lamp1 Deficiency Enhances Sensitivity to α-Synuclein and Oxidative Stress in Drosophila Models of Parkinson Disease

Parkinson disease (PD) is a common neurodegenerative condition affecting people predominantly at old age that is characterized by a progressive loss of midbrain dopaminergic neurons and by the accumulation of α-synuclein-containing intraneuronal inclusions known as Lewy bodies. Defects in cellular degradation processes such as the autophagy-lysosomal pathway are suspected to be involved in PD progression. The mammalian Lysosomal-associated membrane proteins LAMP1 and LAMP2 are transmembrane glycoproteins localized in lysosomes and late endosomes that are involved in autophagosome/lysosome maturation and function. Here, we show that the lack of Drosophila Lamp1, the homolog of LAMP1 and LAMP2, severely increased fly susceptibility to paraquat, a pro-oxidant compound known as a potential PD inducer in humans. Moreover, the loss of Lamp1 also exacerbated the progressive locomotor defects induced by the expression of PD-associated mutant α-synuclein A30P (α-synA30P) in dopaminergic neurons. Remarkably, the ubiquitous re-expression of Lamp1 in a mutant context fully suppressed all these defects and conferred significant resistance towards both PD factors above that of wild-type flies. Immunostaining analysis showed that the brain levels of α-synA30P were unexpectedly decreased in young adult Lamp1-deficient flies expressing this protein in comparison to non-mutant controls. This suggests that Lamp1 could neutralize α-synuclein toxicity by promoting the formation of non-pathogenic aggregates in neurons. Overall, our findings reveal a novel role for Drosophila Lamp1 in protecting against oxidative stress and α-synuclein neurotoxicity in PD models, thus furthering our understanding of the function of its mammalian homologs.     

Sample preparation optimization for the simultaneous determination of mycotoxins in cereals

The efficiency of three extraction solvents and three clean-up procedures was compared for simultaneous extraction and purification of aflatoxins (AFB1, AFB2, AFG1 and AFG2), ochratoxin A (OTA), and zearalenone (ZEA) from spiked cereal samples. The best recovery rates for all mycotoxins were achieved using methanol: water (80:20) as the extraction solvent and AOZ multi-functional immunoaffinity column (IAC), as clean up method with recovery values of 61–89%, while that of Oasis HLB and MycoSep 226 were 37–67% and 44–78%, respectively. Then, five variables in the IAC clean-up conditions, including primary conditioning with phosphate buffer saline (PBS) (0–10 mL) (X1), extract load up volume (10–20 mL) (X2), washing volume with PBS (10–20 mL) (X3), and eluting solution volumes with methanol (1–3 mL) (X4) and acetic acid (0–1.5 mL) (X5), were optimized for the specific purification and enrichment of the mycotoxins. Results showed that primary conditioning and PBS washing did not have a significant effect on the recovery responses of mycotoxins. Optimized conditions were selected as 0, 15, 10, 1.3, and 1.5 mL for X1–X5, respectively. The recovery rates of AFB1, AFB2, AFG1, AFG2, OTA and ZEA were within 93–104% in spiked rice, under optimal conditions. LOD and LOQ were 0.0125 and 0.05 ng/g for AFB1 and AFG1, 0.0037 and 0.015 ng/g for AFB2 and AFG2, 0.05 and 0.2 ng/g for OTA, and 0.5 and 2 ng/g for ZEA, respectively. Extraction of spiked cereal samples with methanol: water (80:20) and clean up using AOZ IAC column in optimal condition provided recovery range of 77–104% for all targeted mycotoxins.     

Thermoelastic vibration of doubly-curved nano-composite shells reinforced by graphene nanoplatelets

Abstract

The thermo-mechanical vibration characteristics of doubly-curved nano-composite shells reinforced by graphene nanoplatelets are investigated by considering a uniform distribution of graphene and a first-order shear deformation theory. The mechanical properties of the nano-composite shells are estimated by using the modified Halpin–Tsai model. The governing equations are first derived by a variational formulation using Hamilton’s principle and are solved using the Galerkin technique. Numerical results are presented for various shell curvatures and compared with those available in the archival literature. Furthermore, parametric studies are offered to highlight the significant influence of graphene nanoplatelets’ weight fraction, dimensions of graphene nanoplatelets, and temperature variation, on the free vibration of the nano-composite shells.     

Effect of Acidic and Basic Solutions on Electron Beam Irradiated Epoxy Nanocomposites Containing Nanoclay,CaCO3 and TiO2 Nanoparticles

High chemical resistance is the main prerequisites for materials that are intended to be utilized in usages such as chemicals storage containers production. Nanocomposites of epoxy resin containing nanoclay, CaCO₃ and TiO₂ nanoparticles were prepared and their chemical resistance was studied. Moreover, the effect of electron beam irradiation was explored. TEM micrographs proved the dispersion of nano-size particles in the polymeric matrix. XRD patterns showed an exfoliated structure for nanocomposite containing 1 % nanoclay and intercalated structures for nanocomposites with higher nanoclay contents. SEM showed the pits that appeared in epoxy/nanoclay structure due to chemical corrosion. Weight loss measurements revealed that an addition of 1 % nanoclay to the epoxy matrix is effective for improving the chemical properties of the polymer. Desirable effect of 100 kGy irradiation on chemical resistance properties of the samples was also observed in both acidic and basic environments.     

Mechanical properties of carbon fiber/epoxy composites: Effects of number of plies,fiber contents,and angle‐ply layers

Multi‐axial multi‐ply fabric (MMF) composites are becoming increasingly popular as reinforcing materials in high‐performance composites due to their high mechanical properties. This work aimed to study the effects of three variable parameters including fiber contents, numbers of plies, and layer orientations on the mechanical properties of MMF composites. Unidirectional carbon fibers and a two‐part epoxy resin were employed to produce the composite laminates using the manual lay‐up process. It was found that the mechanical properties of composites made with 5‐ply were slightly greater than 3‐ply composites. However, there was no highly significant difference between them. Generally, the angle‐ply of the composites showed the greatest effect on the mechanical properties compared with number of plies and layer orientations. The significant improvements in mechanical properties of the composites were further supported using scanning electron microscopy (SEM). Morphologies of the tensile fracture surfaces of composites revealed that the presence of fiber pulled out results in the creation of voids between the fibers and matrix polymer. This causes the mechanical properties of the composites to be reduced. Finally, the enhancement of mechanical properties of composites clearly confirmed that angle‐ply layer (0°,?35°,0°,+35°,0°) had the most significant reinforcing effect among other parameters evaluated. POLYM. ENG. SCI., 54:2676–2682, 2014. © 2013 Society of Plastics Engineers     

Stabilizing a class of nonlinear parameter-varying systems using interval observer based on a piecewise framework

This article addresses an interval observer-based control for stabilizing a class of nonlinear parameter-varying systems with noisy output by designing a switching surface. An input-dependent interval observer is firstly developed to estimate the lower and upper bounds of the states. Next, a switching-based controller is designed to stabilize the interval observer which implies the stability of the main parameter-varying system. The developed stabilizing switching surfaces are designed based on the outputs of the main system and the bounds of the states of the observer. By choosing an appropriate piecewise Lyapunov function, the closed-loop stability analysis of the interval observer system leads to a set of linear matrix inequalities including stability and Metzler constraints, simultaneously. The effectiveness of the proposed method is verified using the simulation results.     

Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). We also constructed ensemble models such as bagging (Random Forest) and boosting like AdaBoost and Extreme Gradient Boosting (XGBoost) to evaluate the performance of prediction models. The proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the disease. The combined ML models used majority voting to select the best outcomes among the models. The efficacy of the proposed and other models was evaluated for effective diabetes prediction. The investigation concluded that the proposed model, after hyperparameter tuning, outperformed other learning models with an accuracy of 84.42%, a precision of 85.12%, a recall rate of 65.40%, and a specificity of 94.11%.