Graphene supported platinum nanoparticle counter-electrode for enhanced performance of dye-sensitized solar cells

Composites of few layered graphene (G) and platinum (Pt) nanoparticles (NP) with different loadings of Pt were used as counter electrode (CE) in dye-sensitized solar cell (DSSC). NPs were deposited directly on to G using pulsed laser ablation method (PLD). DSSCs formed using the composite CEs show improved performance compared to conventional Pt thin film electrode (Std Pt) and unsupported Pt NPs. Composite with 27% loading of Pt shows 45% higher efficiency (η = 2.9%), greater short circuit current (J(sc) = 6.67 mA cm(-2)), and open circuit voltage (V(oc) = 0.74 V) without any loss of the fill factor (FF = 58%) as compared to the cells fabricated using Std Pt electrodes. Values of η, J(sc) and V(oc) for DSSC using Std Pt CE were 2%, 5.05 mA cm(-2) and 0.68 V, respectively. Electrochemical impedance spectroscopy using I(-)(3)/I(-) redox couple confirm lower values of charge transfer resistance for the composite electrodes, e.g., 2.36 Ω cm(2) as opposed to 7.73 Ω cm(2) of Std Pt. The better catalytic activity of these composite materials is also reflected in the stronger I(-)(3) reduction peaks in cyclic voltammetry scans.     

Reduced Graphene Oxide‐GelMA Hybrid Hydrogels as Scaffolds for Cardiac Tissue Engineering

Biomaterials currently used in cardiac tissue engineering have certain limitations, such as lack of electrical conductivity and appropriate mechanical properties, which are two parameters playing a key role in regulating cardiac cell behavior. Here, the myocardial tissue constructs are engineered based on reduced graphene oxide (rGO)‐incorporated gelatin methacryloyl (GelMA) hybrid hydrogels. The incorporation of rGO into the GelMA matrix significantly enhances the electrical conductivity and mechanical properties of the material. Moreover, cells cultured on composite rGO‐GelMA scaffolds exhibit better biological activities such as cell viability, proliferation, and maturation compared to ones cultured on GelMA hydrogels. Cardiomyocytes show stronger contractility and faster spontaneous beating rate on rGO‐GelMA hydrogel sheets compared to those on pristine GelMA hydrogels, as well as GO‐GelMA hydrogel sheets with similar mechanical property and particle concentration. Our strategy of integrating rGO within a biocompatible hydrogel is expected to be broadly applicable for future biomaterial designs to improve tissue engineering outcomes. The engineered cardiac tissue constructs using rGO incorporated hybrid hydrogels can potentially provide high‐fidelity tissue models for drug studies and the investigations of cardiac tissue development and/or disease processes in vitro.     

Synthesis of single-walled carbon nanotubes by induction thermal plasma

The production of high quality single-walled carbon nanotubes (SWCNTs) on a bulk scale has been an issue of considerable interest. Recently, it has been demonstrated that high quality SWCNTs can be continuously synthesized on large scale by using induction thermal plasma technology. In this process, the high energy density of the thermal plasma is employed to generate dense vapor-phase precursors for the synthesis of SWCNTs. With the current reactor system, a carbon soot product which contains approximately 40 wt% of SWCNTs can be continuously synthesized at the high production rate of ∼100 g/h. In this article, our recent research efforts to achieve major advances in this technology are presented. Firstly, the processing parameters involved are examined systematically in order to evaluate their individual influences on the SWCNT synthesis. Based on these results, the appropriate operating conditions of the induction thermal plasma process for an effective synthesis of SWCNTs are discussed. A characterization study has also been performed on the SWCNTs produced under the optimum processing conditions. Finally, a mathematical model of the process currently under development is described. The model will help us to better understand the synthesis of SWCNTs in the induction plasma process.      

Effects of probiotic and prebiotic supplementation on metabolic parameters,liver aminotransferases,and systemic inflammation in nonalcoholic fatty liver disease: A randomized clinical trial

This study aimed to evaluate the clinical efficacy of probiotic and prebiotic supplementation on the metabolic parameters, liver enzymes, and inflammation in patients with nonalcoholic fatty liver disease (NAFLD). In this study, patients with NAFLD were assigned to receive either probiotic capsule + placebo of prebiotic (probiotic group), oligofructose + placebo of probiotic (prebiotic group), or placebo of probiotic + placebo of prebiotic (control group) for 12 weeks. All participants followed a weight loss diet and physical activity recommendation during intervention. Anthropometric measurements decreased in all three groups, but there was no significant difference among groups. Probiotic supplementation was able to decrease triglyceride, alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyltransferase (GGT), and alkaline phosphatase compared to control group. The serum levels of triglyceride, total and low-density lipoprotein cholesterol, ALT, AST, and GGT differed significantly in prebiotic group in comparison to the placebo. High-sensitive C-reactive protein significantly decreased within all groups; however, there was no significant difference among groups after intervention. Probiotic and prebiotic may be beneficial in improving liver enzymes and lipid profile in patients with NAFLD.     

Proteolysis and textural properties of low‐fat ultrafiltered Feta cheese as influenced by maltodextrin

Maltodextrin was used as a fat replacer in low‐fat ultrafiltered cheese. Fat was replaced with 25% maltodextrin milk solution (w/w) in cheese at 15 and 50% (w/w). The chemical, rheological and sensory properties as well as the microstructure of the cheese samples were evaluated after storage for 2 months at 8 °C. Maltodextrin affected the chemical (pH, dry matter, fat, water‐soluble nitrogen to total nitrogen, nonprotein nitrogen to total nitrogen, total free amino acid) and rheological (mean relaxation time) properties, as well as the microstructure. In general, based on textural properties, sensory evaluation and economic aspects, the 50%‐fat‐reduced sample was selected as the best treatment.     

Novel optically active poly(amide‐imide)s with tetrahydropyrimidinone and tetrahydro‐2‐thioxopyrimidine moieties by microwave‐assisted polycondensation

Rapid and highly efficient synthesis of novel poly(amide‐imide)s (PAIs) were achieved under microwave irradiation by using a domestic microwave oven from the polycondensation reactions of 4,4′‐carbonyl‐bis(phthaloyl‐L ‐alanine) diacid chloride [N,N′‐(4,4′‐carbonyldiphthaloyl)] bisalanine diacid chloride (1) with six different derivatives of tetrahydropyrimidinone and tetrahydro‐2‐thioxopyrimidine compounds (2a–2f) in the presence of a small amount of a nonpolar organic medium that acts as a primary microwave absorber. Suitable organic media was o‐cresol. The polycondensation proceeded rapidly and was almost completed within 10 min, giving a series of PAIs with inherent viscosities of about 0.25–0.45 dL/g. The resulting PAIs were obtained in high yield and are optically active and thermally stable. All of the above compounds were fully characterized by means of Fourier transform infrared spectroscopy, elemental analyses, inherent viscosity (η_inh), solubility test, and specific rotation. Thermal properties of the PAIs were investigated using thermogravimetric analysis. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 80: 2416–2421, 2001     

Prediction of formation of polycyclic aromatic hydrocarbon (PAHs) on sediment of Caspian Sea using artificial neural networks

In this article the amount of polycyclic aromatic hydrocarbon (PAHs) on the sediment of Caspian Sea predicted by artificial neural networks multi-layer perceptron (MLP) and generalized regression (GRNN) models. PAH is a most important pollutant in a marine environment derived from an anthropogenic and natural source. This component is mutagenic and Carcinogenic extremely. In this investigation, a multi-layer perceptron and Generalized regression neural network models have been developed by experimental data (organic matter, latitude, longitude, depth and effective matter (particle size)) reported in the literature. As a result, experimental data compared to the output of models by calculation of mean squared error (MSE), root mean squared error (RMSE), mean absolute error percent (MEAE), maximum absolute error percent (MAAE) and R². Results have shown an appropriate fitting for experimental data with predicted values .Also, MLP neural network has the best performance to predicating of PAHs.     

A comprehensive analytical model of interconnection networks in large‐scale cluster systems

The trends in parallel processing system design and deployment have been toward networked distributed systems such as cluster computing systems. Since the overall performance of such distributed systems often depends on the efficiency of their communication networks, performance analysis of the interconnection networks for such distributed systems is paramount. In this paper, we develop an analytical model, under non‐uniform traffic and in the presence of communication locality, for the m‐port n‐tree family interconnection networks commonly employed in large‐scale cluster computing systems. We use the proposed model to study two widely used interconnection networks flow control mechanism namely the wormhole and store&forward. The proposed analytical model is validated through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions. Copyright © 2007 John Wiley & Sons, Ltd.     

A multi-stage stochastic transmission expansion planning method

This paper presents a multi-stage stochastic model for short-term transmission expansion planning considering the available transfer capability (ATC). The ATC can have a huge impact on the power market outcomes and the power system reliability. The transmission expansion planning (TEP) studies deal with many uncertainties, such as system load uncertainties that are considered in this paper. The Monte Carlo simulation method has been applied for generating different scenarios. A scenario reduction technique is used for reducing the number of scenarios. The objective is to minimize the sum of investment costs (IC) and the expected operation costs (OC). The solution technique is based on the benders decomposition algorithm. The N-1 contingency analysis is also done for the TEP problem.The proposed model is applied to the IEEE 24 bus reliability test system and the results are efficient and promising.     

Potential benefits of solar reflective car shells: Cooler cabins,fuel savings and emission reductions

Vehicle thermal loads and air conditioning ancillary loads are strongly influenced by the absorption of solar energy. The adoption of solar reflective coatings for opaque surfaces of the vehicle shell can decrease the “soak” temperature of the air in the cabin of a vehicle parked in the sun, potentially reducing the vehicle’s ancillary load and improving its fuel economy by permitting the use of a smaller air conditioner. An experimental comparison of otherwise identical black and silver compact sedans indicated that increasing the solar reflectance (ρ) of the car’s shell by about 0.5 lowered the soak temperature of breath-level air by about 5–6 °C. Thermal analysis predicts that the air conditioning capacity required to cool the cabin air in the silver car to 25 °C within 30 min is 13% less than that required in the black car. Assuming that potential reductions in AC capacity and engine ancillary load scale linearly with increase in shell solar reflectance, ADVISOR simulations of the SC03 driving cycle indicate that substituting a typical cool-colored shell (ρ = 0.35) for a black shell (ρ = 0.05) would reduce fuel consumption by 0.12 L per 100 km (1.1%), increasing fuel economy by 0.10 km L⁻¹ [0.24 mpg] (1.1%). It would also decrease carbon dioxide (CO₂) emissions by 2.7 g km⁻¹ (1.1%), nitrogen oxide (NO_x) emissions by 5.4 mg km⁻¹ (0.44%), carbon monoxide (CO) emissions by 17 mg km⁻¹ (0.43%), and hydrocarbon (HC) emissions by 4.1 mg km⁻¹ (0.37%). Selecting a typical white or silver shell (ρ = 0.60) instead of a black shell would lower fuel consumption by 0.21 L per 100 km (1.9%), raising fuel economy by 0.19 km L⁻¹ [0.44 mpg] (2.0%). It would also decrease CO₂ emissions by 4.9 g km⁻¹ (1.9%), NO_x emissions by 9.9 mg km⁻¹ (0.80%), CO emissions by 31 mg km⁻¹ (0.79%), and HC emissions by 7.4 mg km⁻¹ (0.67%). Our simulations may underestimate emission reductions because emissions in standardized driving cycles are typically lower than those in real-world driving.