Thermal degradation behavior and kinetic studies of polyacrylamide gel in TiO2 nanoparticles synthesis

Polyacrylamide gel (PAMG) method is a simple, fast and cheap method used for the synthesis of a wide variety of nanopowders. However, no adequate results have been reported on the thermal degradation behavior of PAMG which can be very effective on the final product properties. In this work, thermal degradation behavior of PAMG in the presence of TiCl₄ as a precursor salt for synthesis of TiO₂ nanoparticles was examined in comparison with linear polyacrylamide (LPAM) and pure PAMG by thermogravimetry/differential thermal analysis. Their thermal degradation kinetics was investigated, as well. The results showed that thermal degradation of all samples occurred in two stages at different onset temperatures. Despite the high thermal stability of pure PAMG compared to LPAM, the presence of TiCl₄ as a mineral material in PAMG structure decreases the thermal degradation onset temperature, considerably. Furthermore for LPAM and PAMG, majority of weight loss occurs in the second stage, but in PAMG with TiCl₄ the weight loss occurs mainly at the first stage. For more detailed investigation, residual materials were characterized by Fourier transform infrared spectroscopy and X-ray diffraction (XRD) techniques, attributing this trend to the presence of mineral materials in PAMG structure. XRD and transmission electron microscopy were also applied to confirm anatase crystalline structure and nanoscale distribution of the TiO₂ particles synthesized via PAMG method.     

A new evaluation measure for color image segmentation based on genetic programming approach

One of the greatest challenges while working on image segmentation algorithms is a comprehensive measure to evaluate their accuracy. Although there are some measures for doing this task, but they can consider only one aspect of segmentation in evaluation process. The performance of evaluation measures can be improved using a combination of single measures. However, combination of single measures does not always lead to an appropriate criterion. Besides its effectiveness, the efficiency of the new measure should be considered. In this paper, a new and combined evaluation measure based on genetic programming (GP) has been sought. Because of the nature of evolutionary approaches, the proposed approach allows nonlinear and linear combinations of other single evaluation measures and can search within many and different combinations of basic operators to find a good enough one. We have also proposed a new fitness function to make GP enable to search within search space effectively and efficiently. To test the method, Berkeley and Weizmann datasets besides several different experiments have been used. Experimental results demonstrate that the GP based approach is suitable for effective combination of single evaluation measures.     

Synthesis,characterization and investigation of the electrochemical hydrogen storage properties of CuO–CeO2 nanocomposites synthesized by green method

Pure CuO–CeO₂ nanocomposites were synthesized by simple thermal decomposition method in presence of various Cu salts as a copper source and fructose as a green capping agent. In this study, the effect of various parameters such as the type of copper sources, temperature and time of reaction on the morphology and the particles size were studied. The products were characterized via X-ray diffraction (XRD) pattern, scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM), N2 adsorption (BET), vibrating sample magnetometer (VSM), and infrared spectrum (FT-IR). The optical property of the nanocomposite was examined via UV–vis (DRS) spectroscopy and the band gap was calculated to 3 eV. Also, the hydrogen storage capacity of CuO–CeO₂ nanocomposites and CeO₂ nanoparticles were investigated via chronopotentiometry method for the first time. The discharge capacity of CeO₂ nanoparticles and CuO–CeO₂ nanocomposites in 1 mA current and 20 cycles obtained 2150 and 2450 mAh/g, respectively.     

Barium cobaltite nanoparticles: Sol-gel synthesis and characterization and their electrochemical hydrogen storage properties

The study of the effect of different chelating agents in the Pechini method on the morphology has been a promising strategy that can be used for practical tuning of the nanoparticle's morphology and hence the electrochemical hydrogen storage capacity. In the current study, the conventional Pechini sol-gel approach was used to prepare the Ba₂Co₉O₁₄ nanoparticles as a novel hydrogen storage material. The X-ray diffraction investigation approved the formation of Ba₂Co₉O₁₄ with a Hexagonal crystal structure for all of the synthesized samples. The scanning electron microscopy (SEM) revealed when citric acid was used as a chelating agent, nanoparticles with finer and more uniform morphology were obtained rather than other chelating sources. The transmission electron microscopy (TEM) showed in the presence of citric acid; the size of the synthesized nanoparticles was between 14 and 24 nm. According to the Diffuse Reflectance Spectroscopy (DRS) analysis, the calculated bandgap of synthesized nanoparticles was approximately 3.2 eV, which indicates that synthesized nanoparticles were semiconductors in essence. The electrochemical hydrogen adsorption/desorption results showed that the sample synthesized by the citric acid has an enhancement in electrochemical hydrogen storage of approximately 800 mAh/g after 15 cycles.     

Green synthesis and characterization of RGO/Cu nanocomposites as photocatalytic degradation of organic pollutants in waste-water

Recently, nanocomposite photocatalysts based on semiconductors have attracted much attention due to their suitable bandgap. Combination of tow of several semiconductors can slow down the electron-hole recombination. In this regard, we have depicted an eco-friendly and green fabrication technique to synthesize RGO/Cu nanocomposite by the reduction of graphene oxide and Cu²⁺ ion utilizing spearmint extract as a reductant and capping agent. The sample was identified by FTIR, XRD, FESEM, EDS, HRTEM, and CV. The results of photocatalytic performance revealed that RGO/Cu is an efficient catalyst for degrading organic pollutants. This compound can eliminate Rhodamine B (RhB) and Methylene blue (MB) 91.0% and 72.0%, respectively.     

CuIns2/Cus Nanocomposite: Synthesis via Simple Microwave Approach and Investigation Its Behavior in Solar Cell

CuInS₂/CuS nanocomposite were synthesized by a copper complex, [bis(ethylenediamine)copper(ΙΙ)] sulfate. Eight sulfur sources were used for this experiment. The products were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray analysis spectroscopy, and room temperature photoluminescence spectroscopy. Thin film of nanocomposite powder was fabricated and its feature (V_oc, J_sc and FF) was calculated by current–voltage (I–V) curve.     

Enabling Interoperability among Grid Meta-Schedulers

The goal of Grid computing is to integrate the usage of computer resources from cooperating partners in the form of Virtual Organizations (VO). One of its key functions is to match jobs to execution resources efficiently. For interoperability between VOs, this matching operation occurs in resource brokering middleware, commonly referred to as the meta-scheduler or meta-broker. In this paper, we present an approach to a meta-scheduler architecture, combining hierarchical and peer-to-peer models for flexibility and extensibility. Interoperability is further promoted through the introduction of a set of protocols, allowing meta-schedulers to maintain sessions and exchange job and resource state using Web Services. Our architecture also incorporates a resource model that enables an efficient resource matching across multiple Virtual Organizations, especially where the compute resources and state are dynamic. Experiments demonstrate these new functional features across three distributed organizations (BSC, FIU, and IBM), that internally use different job scheduling technologies, computing infrastructure and security mechanisms. Performance evaluations through actual system measurements and simulations provide the insights on the architecture’s effectiveness and scalability.     

Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach

We propose a bi-objective cell formation problem with demand of products expressed in a number of probabilistic scenarios. To deal with the uncertain demand of products, a framework of two-stage stochastic programming model is presented. The proposed model considers minimizing the sum of the miscellaneous costs (machine constant cost, expected machine variable cost, cell fixed-charge cost, and expected intercell movement cost) and expected total cell loading variation. Because of conflicting objectives, we develop a two-phase fuzzy linear programming approach for solving bi-objective cell formation problem. To show the effectiveness of the proposed approach, numerical examples are solved and the results are compared with the two existing approaches in the literature. The computational results show that the proposed fuzzy method achieves lower objective functions as well as higher satisfaction degrees.