This study synthesized polyacetal from the reaction of polyvinyl alcohol with para-nitrobenzaldehyde. Polyacetal/polyvinylpyrrolidone polymer blends were prepared using solution casting. Gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) were biosynthesized using onion peel extract as the reducing agent. Nanocomposites were fabricated by blending polyacetal/PVP with AuNPs and AgNPs at different ratios. XRD and FESEM characterized the AuNPs and AgNPs. FTIR, FESEM, TGA, and DSC characterized the polyacetal, polymer blends, and nanocomposites. DSC and TGA confirmed the improved thermal stability of the polymer blends and nanocomposites. Nanocomposites demonstrated higher efficacy in inhibiting lung cancer cell lines compared t

Improved oral bioavailability of lipophilic substances can be achieved using self-emulsifying drug delivery systems. However, because the properties of self-emulsifying are greatly influenced by surfactant amount and type, type of oil used, droplet size, charge, cosolvents, and physiological variables, the synthesis of self-emulsifying is highly complex; consequently, only a small number of excipient self-emulsifying formulations has been developed so far for clinical use. This study reports a highly effective procedure for developing self-emulsifying formulations using a novel approach based on the hydrophilic-lipophilic difference theory. Microemulsion characteristics, such as the constituents and amounts of oil and surfactant electrolyte

A series of batch demulsification runs were carried out to evaluate the final emulsified water content of emulsion samples after the exposure to microwave. An experimental study was conducted to evaluate the effects of a set of operating variables on the demulsification performance. Several microwave irradiation demulsification runs were carried out at different irradiation powers (700, 800, and 900 watt), using water-in-oil emulsion samples containing different water contents (20-80%, 30-70%, and 50-50%) and salt contents (10000, 20000, and 30000 ppm). It was found that the best separation efficiency was obtained at 900watt, 50% water content and 160 s of irradiation time. Experimental results showed that microwave radiation method can

Liquid membrane electrodes for the determination iron(III) were constructed based on chloramphenicol sodium succinate and iron(III) CPSS-Fe(III) as ion pair complex, with four plasticizers Di-butyl phosphate (DBP); Di-butyl phthalate (DBPH); Di-octyl phthalate (DOP); Tri-butyl phosphate (TBP); in PVC matrix . These electrodes give Nernstian and sub-Nernstian slopes (19.79, 24.60, 16.01 and 13.82mV/decade) and linear ranges from (1x10-5-1x10-2 M, 1x10-5-1x10-2 M, 1x10-6-1x10-2 M and 1x10-5-1x10-2 M) respectively. The best electrode was based on DBP plasticizer which gave a slope 19.79 mV/decade, correlation coefficient 0.9999, detection limit of 9×10-6 M, lifetime 37 day displayed good stability and reproducibility and used to determine

The electrode in the microbial fuel cell has a significant effect on cell performance. The treatment of the electrode is a crucial step to make the electrode surface more habitable for bacteria growth, thus, increases the power production as well as waste treatment. In the current study, two graphite electrodes were treated by a microwave. The first electrode was treated with 100W microwave energy, while the second one was treated with 600W microwave energy. There is a significant enhancement in the surface of the graphite anode after the pretreatment process. The results show an increase in the power density from 10 mW/m² to 15 mW/m² with 100w treatment and to 13.47 mW/m² with 600w treatment. An organic

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func