Modelling of the pH effect on the biosorption of heavy metals by marine algae Sargassum filipendula

Modelling of the pH effect on the biosorption of heavy metals by marine algae Sargassum filipendula. In this paper the biosorption of metal ions Cu(2+), Cd(2+) and Zn(2+) in single-component system by Sargassum filipendula pre-treated with 0.5 M CaCl(2) was studied. The experiments were carried out in a batch reactor at different fixed pH (3.0, 4.0, 5.0 and 6.0) and 30 degrees C. All the equilibrium data obtained were described using two pH-dependent isotherm models, based on the Langmuir isotherm. Artificial neural networks was also used to represent the pH effect on the biosorption equilibrium. The input of the networks were the equilibrium concentration of the metal in the fluid phase and the pH. As output the concentration of the metallic specie in the biosorbent was used. The results showed that the modeling using artificial neural networks technique represented the equilibrium data much better than the conventional modeling by the pH-dependent isotherm models.33443944

Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action

The Law of Mass Action generally models the equilibrium data from ion exchange processes. This methodology is rigorous in terms of thermodynamics and takes into consideration the non-idealities in the solid and aqueous phases. However, the artificial neural networks may also be employed in the phase equilibrium modeling. In this study, both methodologies were tested to describe the ion exchange equilibrium in the binary systems SO42--NO3-, SO42--Cl-, NO3-Cl- and in the ternary system SO42--Cl--NO3-, by AMBERLITE IRA 400 resin as ion exchanger. Datasets used in current study were generated by the application of the Law of Mass Action in the binary systems. Results showed that in the equilibrium modeling of binary systems both methodologies had a similar performance. However, in the prediction of the ternary system equilibrium, the Artificial Neural Networks were not efficient. Networks were also trained with the inclusion of ternary experimental data. The Law of Mass Action in the equilibrium modeling of the ternary system was more efficient than Artificial Neural Networks in all cases.341536

Gastro-Resistant Controlled Release of OTC Encapsulated in Alginate/Chitosan Matrix Coated with Acryl-EZE (R) MP in Fluidized Bed

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)A gastro-resistant system of acryl-EZE (R) MP coated alginate/chitosan microparticles was developed to improve the controlled release of oxytetracycline (OTC). Microparticles were obtained by complex coacervation and, thereafter, were coated using fluidized polymer dispersion with acryl-EZE (R) MP solution. OTC distribution inside the microparticles was determined by multiphoton confocal microscopy, demonstrating the efficiency of encapsulation process. In vitro OTC release kinetic was performed in order to obtain the release profile in gastric and intestinal simulated fluids. A fast initial release, or burst effect, was observed with uncoated microparticles loaded with OTC in gastric conditions. When a 50% mass increase in acryl-EZE (R) MP coating was achieved, OTC release in acidic medium was greatly reduced, resulting in the expected gastro-resistant effect. Different mathematical models were applied to describe the drug diffusion across the polymer matrix. The Logistic model was the best tool to interpret the experimental data in most of the systems studied. (c) 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40444.13112Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES