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.

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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)

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Filmes de Langmuir-Blodgett de nanopartículas de prata

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comportamento biomecânico de estruturas multicamadas em restaurações protéticas

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

Mapeamento da cinemática inversa de um manipulador robótico utilizando redes neurais artificiais configuradas em paralelo

Pós-graduação em Engenharia Elétrica - FEIS

Robust isothermal electric control of exchange bias at room temperature

Voltage-controlled spin electronics is crucial for continued progress in information technology. It aims at reduced power consumption, increased integration density and enhanced functionality where non-volatile memory is combined with highspeed logical processing. Promising spintronic device concepts use the electric control of interface and surface magnetization. From the combination of magnetometry, spin-polarized photoemission spectroscopy, symmetry arguments and first-principles calculations, we show that the (0001) surface of magnetoelectric Cr2O3 has a roughness-insensitive, electrically switchable magnetization. Using a ferromagnetic Pd/Co multilayer deposited on the (0001) surface of a Cr2O3 single crystal, we achieve reversible, room-temperature isothermal switching of the exchange-bias field between positive and negative values by reversing the electric field while maintaining a permanent magnetic field. This effect reflects the switching of the bulk antiferromagnetic domain state and the interface magnetization coupled to it. The switchable exchange bias sets in exactly at the bulk Néel temperature.

Immobilization of cholesterol oxidase in LbL films and detection of cholesterol using ac measurements

The preserved activity of immobilized biomolecules in layer-by-layer (LbL) films can be exploited in various applications. including biosensing. In this study, cholesterol oxidase (COX) layers were alternated with layers of poly(allylamine hydrochloride) (PAH) in LbL films whose morphology was investigated with atomic force microscopy (AFM). The adsorption kinetics of COX layers comprised two regimes, a fast, first-order kinetics process followed by a slow process fitted with a Johnson-Mehl-Avrami (JMA) function. with exponent similar to 2 characteristic of aggregates growing as disks. The concept based on the use of sensor arrays to increase sensitivity, widely employed in electronic tongues, was extended to biosensing with impedance spectroscopy measurements. Using three sensing units, made of LbL films of PAH/COX and PAHIPVS (polyvinyl sulfonic acid) and a bare gold interdigitated electrode, we were able to detect cholesterol in aqueous solutions down to the 10(-6) M level. This high sensitivity is attributed to the molecular-recognition interaction between COX and cholesterol, and opens the way for clinical tests to be made with low cost. fast experimental procedures. (C) 2008 Published by Elsevier B.V.

Damage detection in beams by using artificial neural networks and dynamical parameters

In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network.

Long-Term Stability at High Temperatures for Birefringence in PAZO/PAH Layer-by-Layer Films

Optical memories with long-term stability at high temperatures have long been pursued in azopolymers with photoinduced birefringence. In this study, we show that the residual birefringence in layer-by-layer (LbL) films made with poly[1-[4-(3-carboxy-4 hydroxyphenylazo)benzene sulfonamido]-1,2-ethanediyl, sodium salt] (PAZO) alternated with poly(allylamine hydrochloride) (PAH) can be tuned by varying the extent of electrostatic interactions with film fabrication at different pHs for PAH. The dynamics of both writing and relaxation processes could be explained with a two-stage mechanism involving the orientation of the chromophores per se and the chain movement. Upon calculating the activation energies for these processes, we demonstrate semiquantitatively that reduced electrostatic interactions in films prepared at higher pH, for which PAH is less charged, are responsible for the longer stability at high temperatures. This is attributed to orientation of PAZO chromophores via cooperative aggregation, where the presence of counterions hindered relaxation.

Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8.

Multi-element determination in Brazilian honey samples by inductively coupled plasma mass spectrometry and estimation of geographic origin with data mining techniques

Multi-element analysis of honey samples was carried out with the aim of developing a reliable method of tracing the origin of honey. Forty-two chemical elements were determined (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, P, La, Mg, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr) by inductively coupled plasma mass spectrometry (ICP-MS). Then, three machine learning tools for classification and two for attribute selection were applied in order to prove that it is possible to use data mining tools to find the region where honey originated. Our results clearly demonstrate the potential of Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) chemometric tools for honey origin identification. Moreover, the selection tools allowed a reduction from 42 trace element concentrations to only 5. (C) 2012 Elsevier Ltd. All rights reserved.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.