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A silica-silver nanocomposite obtained by sol-gel method in the presence of silver nanoparticles

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Racles C., Nistor A., Cazacu M.
Open Chemistry
|
2013
|
vol. 11
|
issue 10
1689-1698
EN
Silver nanoparticles (AgNPs) were obtained by a redox reaction, using a glucose-containing cyclosiloxane as a reduction agent and stabilizer. Then the AgNPs aqueous solution was used as the reaction medium for the sol-gel process, starting from tetraethylorthosilicate (TEOS) as silica precursor. The nanocomposite material resulted (SilAg) after solvent removal, aging and calcination and was investigated by infrared spectroscopy (FT-IR), atomic force microscopy (AFM), scanning electron microscopy coupled with energy dispersive X-ray system (SEM/EDX), transmission electron microscopy (TEM), energy-dispersive X-ray fluorescence spectroscopy (EDXRF), X-ray diffraction (XRD) and dynamic vapor sorption (DVS). The results were compared to model silicas obtained without silver. A higher condensation degree in SilAg was obtained due to the basic medium used in the first step and was confirmed by a sorption capacity lower than for the model silicas. The solid surface area calculated with GAB analysis using DVS data for the water vapors is 210 m2 g−1. The nanocomposite showed good catalytic activity for hydrogen peroxide decomposition. [...]
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NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants

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Furtuna R., Curteanu S., Racles C.
Open Chemistry
|
2011
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vol. 9
|
issue 6
1080-1095
EN
Polydimethylsiloxane nanoparticles were obtained by nanoprecipitation, using a siloxane surfactant as stabilizer. Two neural networks and a genetic algorithm were used to optimize this process, by minimizing the particle diameter and the polydispersity, finding in this way the optimum values for surfactant and polymer concentrations, and storage temperature. In order to improve the performance of the non-dominated sorting genetic algorithm, NSGA-II, a genetic operator was introduced in this study - the transposition operator - “real jumping genes”, resulting NSGA-II-RJG. It was implemented in original software and was applied to the multi-objective optimization of the polymeric nanoparticles synthesis with silicone surfactants. The multi-objective function of the algorithm included two fitness functions. One fitness function was calculated with a neural network modelling the variation of the particle diameter on the surfactant concentration, polymer concentration, and storage temperature, and the other was computed by a neural network modelling the dependence of polydispersity index on surfactant and polymer concentrations. The performance of the software program that implemented NSGA-II-RJG was highlighted by comparing it with the software implementation of NSGA-II. The results obtained from simulations showed that NSGA-II-RJG is able to find non-dominated solutions with a greater diversity and a faster convergence time than NSGA-II. [...]
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