Avaliação das condições de precipitação do azul da Prússia sobre pó de grafite visando o desenvolvimento de eletrodos de pasta de carbono para a quantificação de nitrito

An amperometric FIA method for nitrite quantification based on nitrite electroreduction and employing a carbon paste electrode (CPE) chemically modified with iron hexacyanoferrate (HCF) as an amperometric detector was developed. The influence of experimental conditions on the preparation of the electrode materials was evaluated and the materials obtained in each study were used for the development of modified electrodes. The electrochemical sensors were prepared by a fast, simple, and inexpensive procedure, and the long-term performance of the electrodes were quite satisfactory as the stability was maintained over one year. HCF was an effective redox mediator for nitrite electroreduction in acidic media, allowing nitrite detection at +0.2 V vs. Ag/AgClsat, which is a potential free of possible interfering species that are normally present in food and water samples. The electrochemical cell used in the FIA system was similar to a batch injection analysis cell, enabling recirculation of the carrier solution. This is an attractive feature because it allows the use of a high flow rate (6 mL min-1) leading to high sensitivity and analysis speed, while keeping reagent consumption low. The proposed method had a detection limit of 9 μmol L-1 and was successfully employed for nitrite quantification in spiked water and sausage samples. The obtained results were in good agreement with those provided by the spectrophotometric official method. At a 95 % confidence level it was not observed statistical differences neither in nitrite content nor in the precision provided by both methods. The experimental conditions for the synthesis of HCF were optimized and the best electrode material was prepared by mixing FeCl3, K4[Fe(CN)6] and carbon powder subjected to an acid and thermal treatment (400 ºC), followed by ultrasonic agitation at 4 °C. This material was used to construct an electrode with improved analytical performance to reduce nitrite, which presented greater stability compared to HCF film electrodeposited on the EPC, showing that the preparation procedure of the electrode material is an effective strategy for the development of HCF modified electrodes.

Determinação voltamétrica de zinco empregando eletrodos quimicamente modificados com bismuto

This dissertation presents the development of voltammetric methods to zinc determination in multivitamin commercial samples, talc, and art materials for painting (soft pastel) combining an alkaline extraction with 1.0 mol L-1 NaOH aqueous solution and bismuth modified electrodes. Two electrodes were used to zinc quantification in the samples, bismuth film electrode (BiFE) plated in situ onto glassy carbon and carbon paste electrode chemically modified with strongly acidic ion exchange resin Amberlite® IR 120 and bismuth nanostructures (EPCAmbBi). It was verified that the best concentration of Bi3+ for Bi film deposition onto glassy carbon was 4.0 μmol L-1 using an 0.1 mol L-1 acetate buffer aqueous solution (pH = 4.5) as supporting electrolyte. The best condition to formation of Bi nanostructures in the EPC modified with 10 % Amberlite® IR 120 was the use of 30 s to pre-concentration (open circuit) in 0.5 mmol L-1 Bi3+ aqueous solution (pH 5.5) prepared with supporting electrolyte solution. The obtained analytical curve for Zn2+ using BiFE presented linear range from 0.5 to 5.0 μmol L-1, the limit of detection (LD) was 41 nmol L-1. For EPCAmbBi only one linear range was observed for the analytical curve varying the Zn2+ concentration from 0.05 to 8.2 μmol L-1, LD obtained in this curve it was equal to 10 nmol L-1. The EPCAmbBi presented the most intense and sharp anodic stripping peaks for Zn2+ presenting, therefore, a better voltammetric profile, with sensitivity higher than obtained with the BiFE. Moreover, the EPCAmbBi presented a LD lower than that obtained with the BiFE. Alkaline extraction was an efficient sample pretreatment to extract Zn2+ from solid samples, besides that, this procedure was less susceptible to interferences from Cu2+, since it remains at extracting vessel as insoluble Cu(OH)2. The combination of alkaline extraction with the EPCAmbBi is a simple, fast, efficient and low cost for the zinc determination in pharmaceutical formulations and art materials for painting (soft pastel) samples, which can be employed as a low-cost alternative method to the atomic absorption spectroscopy.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.