Highly robust, novel aluminum counter cation-based monophosphate tungsten bronze electro-catalysts for oxygen evolution in acidic solution.

This study describes the successful synthesis of novel bronze with a low tungsten oxidation state for the efficient electro-catalytic oxidation of water. An extraordinarily robust monophosphate tungsten bronze (MPTB)-modified graphite anode was successfully fabricated for the oxygen evolution reaction (OER) at a thermodynamic potential of 1.23 V in H2SO4 acidic solution. Several Al, Cr and Fe counter-cation-based MPTBs were synthesized by the solution combustion method. Novel Al-based MPTBs calcined at 700 °C in O2 (AlO7) showed almost zero onset overpotential, high current density, high turnover frequency for OER and steady catalysis in repeated use even after 30 weeks. The orthorhombic AlO7 comprising crystallites of 9.89 nm and an indirect band gap (1.89 eV), is an unusually stable MPTB that contains 98% W5+ state stabilized with the Al3+ counter cation. The catalysis decreases as the ratio of W5+ : W6+ in MPTBs decreases and [410] and [601] facets play main roles in the first H2O association and nucleophilic attack of the second H2O molecule on the catalyst surface. Thus, MPTBs can be non-noble metal anode materials for robust acidic H2O electrolyzers.

Zizyphus mauritiana Fruit Extract-Mediated Synthesized Silver/Silver Chloride Nanoparticles Retain Antimicrobial Activity and Induce Apoptosis in MCF-7 Cells through the Fas Pathway.

Recently, green synthesis of silver/silver chloride nanoparticles (Ag/AgCl-NPs) has gained a lot of interest because of the usage of natural resources, rapidness, eco-friendliness, and benignancy. Several researchers reported that silver-based biogenic NPs have both antimicrobial and anticancer properties. In the present study, Ag/AgCl-NPs were synthesized from Zizyphus mauritiana fruit extract, and their antibacterial, antifungal, and antiproliferative mechanisms against human MCF-7 cell lines were evaluated. Synthesis of Ag/AgCl-NPs from the Z. mauritiana fruit extract was confirmed by the changes of color and a peak of the UV-visible spectrum at 428 nm. The nanoparticles were characterized by transmission electron microscopy, energy dispersive X-ray, X-ray powder diffraction, thermal gravimetric analysis, atomic force microscope, and Fourier transform infrared. Antibacterial activity was checked against four pathogenic bacteria and two fungi. Cytotoxicity was checked against human breast cancer cell line (MCF-7) and mice Ehrlich ascites carcinoma (EAC) cells by MTS assay and clonogenicity assay. Cell morphology of the control and nanoparticle-treated MCF-7 cells were checked by Hoechst 33342, YF488-Annexin V, and caspase-3 substrates. The level of reactive oxygen species (ROS) was studied by using 2',7'-dichlorofluorescein-diacetate staining. Real-time polymerase chain reaction was used for gene expression. Synthesized nanoparticles were heat stable cubic crystals with an average size of 16 nm that contain silver and chlorine with various functional groups. The synthesized Ag/AgCl-NPs inhibited the growth of three pathogenic bacteria (Bacillus subtilis, Shigella boydii, and Escherichia coli) and two fungi (Aspergillus niger and Trichoderma spp.). Ag/AgCl-NPs inhibited the growth of MCF-7 and EAC cells with the IC50 values of 28 and 84 µg/mL, respectively. No colony was formed in MCF-7 cells in the presence of these nanoparticles as compared with control. Ag/AgCl-NPs induced apoptosis and generated ROS in MCF-7 cells. The expression level of FAS, FADD, and caspase-8 genes increased several folds with the decrease of PARP gene expression. These results demonstrated that the anti-proliferation activity of Ag/AgCl-NPs against MCF-7 cells resulted through ROS generation and induction of apoptosis through the Fas-mediated pathway.

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment.

Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every year in the past decade. But recently, this disease has been witnessed in other regions to a large extent and can become an epidemic very soon. In this paper, a new fog computing based e-Healthcare framework has been proposed to monitor the KFD infected patients in an early phase of infection and control the disease outbreak. For ensuring high prediction rate, a novel Extremal Optimization tuned Neural Network (EO-NN) classification algorithm has been developed using hybridization of the extremal optimization with the feed-forward neural network. Additionally, a location based alert system has also been suggested to provide the global positioning system (GPS)-based location information of each KFD infected user and the risk-prone zones as early as possible to prevent the outbreak. Furthermore, a comparative study of proposed EO-NN with state of art classification algorithms has been carried out and it can be concluded that EO-NN outperforms others with an average accuracy of 91.56%, a sensitivity of 91.53% and a specificity of 97.13% respectively in classification and accurate identification of risk-prone areas.