Synthesis and characterization of PCU@C-Ag/AgCl nanoparticles as an antimicrobial material for respiratory tract infection

Abstract The pregnant cow urine (PCU) is an active source of antimicrobial agents that is used for fabricating chitosan coated Ag/AgCl nanoparticles (NPs) in the present study. These PCU@C-Ag/AgCl NPs were physicochemically characterized and evaluated for antimicrobial activity against selected respiratory tract infection (RTI) pathogens. The absorption band around 420 nm in UV-Visible spectrum indicated the presence of Ag NPs. The spherical shape of NPs was observed using TEM. Also, the crystalline structure was confirmed using the XRD pattern. The PCU@C-Ag/AgCl NPs showed strong antimicrobial activity against all tested RTI pathogens. In addition, FESEM analysis showed morphological changes in RTI bacterial pathogens. Thereby, PCU@C-Ag/AgCl NPs may be used as an antimicrobial material to treat RTIs in near future at clinical level.</jats:p

Genetic algorithm and simulated annealing to estimate optimal process parameters of the abrasive waterjet machining

In this study, two computational approaches, Genetic Algorithm and Simulated Annealing, are applied to search for a set of optimal process parameters value that leads to the minimum value of machining performance. The objectives of the applied techniques are: (1) to estimate the minimum value of the machining performance compared to the machining performance value of the experimental data and regression modeling, (2) to estimate the optimal process parameters values that has to be within the range of the minimum and maximum coded values for process parameters of experimental design that are used for experimental trial and (3) to evaluate the number of iteration generated by the computational approaches that lead to the minimum value of machining performance. Set of the machining process parameters and machining performance considered in this work deal with the real experimental data of the non-conventional machining operation, abrasive waterjet. The results of this study showed that both of the computational approaches managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data