Investigation of zeolitic imidazolate frameworks–derived carbon nanotubes thin film in solar vapor generation

Journal of Porous Materials - Solar vapor generation is considered a green and practical approach to take advantage of solar energy as a renewable source and provide pure water. However, developing...     

Targeted design of polyaniline-graphene oxide,barium-strontium titanate,hard-soft ferrite,and polyester multi-phase nanocomposite for highly efficient microwave absorption

The current paper focuses on synthesizing a high-efficiency microwave absorber via incorporating the nanofillers of graphene oxide-polyaniline (GO-PANI), barium-strontium titanate (BST), and soft-hard ferrite within the polyester matrix. The nanocomposite magnets of (Ba_0.5Sr_0.5Fe₁₂O₁₉)_1-x hard/(CoFe₂O₄)_x soft (x = 0.2, 0.5, and 0.8) were prepared using sol-gel auto-combustion method. The GO-PANI and BST were successfully synthesized by in situ polymerization and improved polymerization, respectively. The phase structure, chemical structure, morphology, and microwave absorption properties of the synthesized nanocomposites were characterized by X-ray diffractometer (XRD), Fourier-transform infrared spectroscopy (FT-IR), and scanning electron microscope (SEM), vector network analyzer (VNA) techniques, respectively. The results showed that the synergistic effects of the combination of dielectric (BST), conductive (GO-PANI), and magnetic materials (hard-soft ferrites) provided the reflection loss values of less than ?20 dB (>99% absorption) in the X-band region. The minimum reflection loss of ?35 dB (>99.99% absorption) was obtained by the optimal formulation including (Ba_0.5Sr_0.5Fe₁₂O₁₉)_0.2 (CoFe₂O₄)_0.8, and the weight ratio of 1: 2 for both BST/soft-hard ferrite and hard-soft ferrite + BST/GO-PANI with the thickness of 1 mm. According to the results, the thickness factor plays a key role in improving the impedance matching. Consequently, the proposed nanocomposite can be employed as a novel kind of microwave absorbers with good impendence matching and high absorption.     

Investigation of the preparation and catalytic activity of supported Mo,W, and Re oxides as heterogeneous catalysts in olefin metathesis

For several decades olefin metathesis reactions over supported Mo, W, and Re oxides catalysts have attracted remarkable interest owning to their growing industrial applications. Therefore, particular attention was devoted to improve the catalytic activity of these catalysts. In the way of exploration of the catalytic performances of these heterogeneous Mo, W, and Re oxides systems, it was found that high dispersion of metal oxide on the support surface and the oxidation state of the metal oxide on the surface of catalyst play crucial factors on the catalysts efficiency. Importantly, these factors have an origin in the preparation methods and the properties of the used supports. In this regard, we have tried to address the various preparation methods of immobilized Mo, W, and Re oxide catalysts as well as properties of supporting material to better understand their impacts on the catalytic performances of these catalysts.     

Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

This paper investigates the ability of four artificial intelligence techniques, including artificial neural network (ANN), radial basis neural network (RBNN), adaptive neuro-fuzzy inference system (ANFIS) with grid partitioning, and ANFIS with fuzzy c-means clustering, to predict the peak and residual conditions of actively confined concrete. A large experimental test database that consists of 377 axial compression test results of actively confined concrete specimens was assembled from the published literature, and it was used to train, test, and validate the four models proposed in this paper using the mentioned artificial intelligence techniques. The results show that all of the neural network and ANFIS models fit well with the experimental results, and they outperform the conventional models. Among the artificial intelligence models investigated, RBNN model is found to be the most accurate to predict the peak and residual conditions of actively confined concrete. The predictions of each proposed model are subsequently used to study the interdependence of critical parameters and their influence on the behavior of actively confined concrete.