Aplicabilidade das equações isotermas de Freundlich e Langmuir na interação biossortiva de corantes têxteis com Saccharomyces cerevisiae liofilizada

This paper presents a study of the applicability of adsorption isotherms, known as Langmuir and Freundlich isotherm, between the biosorptive interaction of yeast lyophilized Saccharomyces cerevisiae and textile dyes. To that end, we prepared stock solutions of the textile dyes Direct Red 23 and Direct Red 75 in the concentration of 1.000μg/mL and a yeast suspension at 2,5%. We did experiments for two cases, firstly for the case that we have a fix concentration of yeast at 0,500mg/mL and an variable concentration of dye range from40, 50, 60, 80 and 100μg/mL, then for the case that we fixed the concentration of dye at 100μg/mL and the yeast concentration was variable range from 0,250, 0,500, 0,750, 1,000, 1,250mg/mL. For the dye Direct Red 23 we did analysis in the pH 2,5, 4,5 and 6,5; for the Direct Red 75, we just did for the pH 2,5. We leave the dye solution in contact with the yeast for 2 hours at a constant temperature of 30°C and then centrifuged and analyzed the sample in a spectrophotometer and finally made and analysis of parameters for the removal and study of the isotherms. After the biosorption, was observed that for the Direct Red 23 in the pH 2,5 was needed 1,407mg/mL of yeast for total removal, while for the pH 4,5 was needed 8,806mg/mL and in pH 6,5 was 9,286mg/mL; for the Direct Red 75 in pH 2,5 was needed 1,337mg/mL. This difference can be explain by the adsorption isotherms, was observed that in the case when the yeast was fix when we had in a acid pH the behavior of the system was compatible with the Langmuir isotherm, and thus, an monolayer pattern. And that when we decrease the acidity of the medium the system became more compatible with a Freundlich isotherm, and thus, a multilayer pattern; for the case that the yeast was variable this is not much evident, however for the pH 2,5 she became compatible with a Langmuir isotherm... (Complete abstract click electronic access below)

Inspeção de materiais compósitos utilizando ondas acústicas guiadas

Pós-graduação em Engenharia Elétrica - FEIS

Characterization and application of nanostructured films containing Au and TiO2 nanoparticles supported in bacterial cellulose

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The application of neural networks to the analysis of dissolved gases in insulating oil used in transformers

The state of insulating oils used in transformers is determined through the accomplishment of physical-chemical tests, which determine the state of the oil, as well as the chromatography test, which determines possible faults in the equipment. This article concentrate on determining, from a new methodology, a relationship among the variation of the indices obtained from the physical-chemical tests with those indices supplied by the chromatography tests.The determination of the relationship among the tests is accomplished through the application of neural networks. From the data obtained by physical-chemical tests, the network is capable to determine the relationship among the concentration of the main gases present in a certain sample, which were detected by the chromatography tests.More specifically, the proposed approach uses neural networks of perceptron type constituted of multiple layers. After the process of network training, it is possible to determine the existent relationship between the physical-chemical tests and the amount of gases present in the insulating oil.

Comparative study between powers of sigmoid functions, MLP-backpropagation and polynomials in function approximation problems

Function approximation is a very important task in environments where the computation has to be based on extracting information from data samples in real world processes. So, the development of new mathematical model is a very important activity to guarantee the evolution of the function approximation area. In this sense, we will present the Polynomials Powers of Sigmoid (PPS) as a linear neural network. In this paper, we will introduce one series of practical results for the Polynomials Powers of Sigmoid, where we will show some advantages of the use of the powers of sigmiod functions in relationship the traditional MLP-Backpropagation and Polynomials in functions approximation problems.

Obtenção e caracterização óptica e elétrica de 'La''Ta'O IND.4' e LiTaO Ind.3 puros e dopados com Eu POT.3+

Eletronicalceramics are used in many applications such as: multilayer capacitor, transducer, pyroelectric sensors and electrooptic devices. In recent years there has been a growing demand for eletronicalceramics with better performance and functionality. This demand has accelerated the development of synthesis techniques to produce powders with well-defined particle size, shape and crystallinity. The eletronicalceramics in the form of bulk are determined by their performance characteristics of the powders used and the preparation process. So, physical and chemical properties of powders, such as chemical control of stoichiometry, purity, homogeneity, particle size and shape should be observed when choosing the methods of synthesis. Among the techniques used so far, the polymeric precursor method, also known as Pechini, has been considered ideal for the preparation of nanosized powders. Thus, this research project aims to use the polymeric precursor method to prepare powders of lithium tantalate and lanthanum tantalate, with good chemical stability. In this aspect is proposed to investigate the effects of variation of the concentration of europium about the properties of tantalate because doping with Eu3 + indicates that they may occupy different sites in the crystal structure, as in the case of LiTaO3. Effects of things like occupation sites, stability of phases and formation temperature have been previously investigated by the group, which motivated the formulation of this project. Our proposal aims to introduce the Eu3 + LaTaO4 and LiTaO3 and study the structural and optical properties of the powders obtained by Pechini method, as well as correlate these studies with the electrical properties of the material, mainly the Ironelectricty Hysteresis.

Renewable nanocomposite layer-by-layer assembled catalytic interfaces for biosensing applications

A novel, easily renewable nanocomposite interface based on layer-by-layer (LbL) assembled cationic/anionic layers of carbon nanotubes customized with biopolymers is reported. A simple approach is proposed to fabricate a nanoscale structure composed of alternating layers of oxidized multiwalled carbon nanotubes upon which is immobilized either the cationic enzyme organophosphorus hydrolase (OPH; MWNT−OPH) or the anionic DNA (MWNT−DNA). The presence of carbon nanotubes with large surface area, high aspect ratio and excellent conductivity provides reliable immobilization of enzyme at the interface and promotes better electron transfer rates. The oxidized MWNTs were characterized by thermogravimetric analysis and Raman spectroscopy. Fourier transform infrared spectroscopy showed the surface functionalization of the MWNTs and successful immobilization of OPH on the MWNTs. Scanning electron microscopy images revealed that MWNTs were shortened during sonication and that LbL of the MWNT/biopolymer conjugates resulted in a continuous surface with a layered structure. The catalytic activity of the biopolymer layers was characterized using absorption spectroscopy and electrochemical analysis. Experimental results show that this approach yields an easily fabricated catalytic multilayer with well-defined structures and properties for biosensing applications whose interface can be reactivated via a simple procedure. In addition, this approach results in a biosensor with excellent sensitivity, a reliable calibration profile, and stable electrochemical response.

Preliminary diagnosis of ophtalmological diseases through machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Filmes de Langmuir-Blodgett de nanopartículas de prata

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Comportamento biomecânico de estruturas multicamadas em restaurações protéticas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)