Síntese e caracterização de materiais mesoporosos para a captura de CO2: influência do óxido de níquel

Several materials are currently under study for the CO2 capture process, like the metal oxides and mixed metal oxides, zeolites, carbonaceous materials, metal-organic frameworks (MOF's) organosilica and modified silica surfaces. In this work, evaluated the adsorption capacity of CO2 in mesoporous materials of different structures, such as MCM-48 and SBA- 15 without impregnating and impregnated with nickel in the proportions 5 %, 10 % and 20 % (m/m), known as 5Ni-MCM-48, 10Ni-MCM-48, 20Ni-MCM-48 and 5Ni-SBA-15, 10NiSBA-15, 20Ni-SBA-15. The materials were characterized by means of X-ray diffraction (XRD), thermal analysis (TG and DTG), Fourier transform infrared spectroscopy (FT-IR), N2 adsorption and desorption (BET) and scanning electron microscopy (SEM) with EDS. The adsorption process was performed varying the pressure of 100 - 4000 kPa and keeping the temperature constant and equal to 298 K. At a pressure of 100 kPa, higher concentrations of adsorption occurred for the materials 5Ni-MCM-48 (0.795 mmol g-1 ) and SBA-15 (0.914 mmol g-1 ) is not impregnated, and at a pressure of 4000 kPa for MCM-48 materials (14.89 mmol g-1) and SBA-15 (9.97 mmol g-1) not impregnated. The results showed that the adsorption capacity varies positively with the specific area, however, has a direct dependency on the type and geometry of the porous structure of channels. The data were fitted using the Langmuir and Freundlich models and were evaluated thermodynamic parameters Gibbs free energy and entropy of the adsorption system

Uso de metamaterial em antenas de microfita com supercondutor

Metamaterials have attracted great attention in recent decades, due to their electromagnetic properties which are not found in nature. Since metamaterials are now synthesized by the insertion of artificially manufactured inclusions in a specified homogeneous medium, it became possible for the researcher to work with a wide collection of independent parameters, for example, the electromagnetic properties of the material. An investigation of the properties of ring resonators was performed as well as those of metamaterials. A study of the major theories that clearly explain superconductivity was presented. The BCS theory, London Equations and the Two-Fluid Model are theories that support the application of superconducting microstrip antennas. Therefore, this thesis presents theoretical, numerical and experimental-computational analysis using full-wave formalism, through the application of the Transverse Transmission Line – LTT method applied in the Fourier Transform Domain (FTD). The LTT is a full wave method, which, as a rule, obtains the electromagnetic fields in terms of the transverse components of the structure. The inclusion of the superconducting patch is performed using the complex resistive boundary condition. Results of resonant frequency as a function of antenna parameters are obtained. To validate the analysis, computer programs were developed using Fortran, simulations were created using the commercial software, with curves being drawn using commercial software and MATLAB, in addition to comparing the conventional patch with the superconductor as well as comparing a metamaterial substrate with a conventional one, joining the substrate with the patch, observing what improves on both cas

Encapsulamento de solventes em nanotubos de haloisita para promoção de auto-reparo em polímeros

Micro cracking during service is a critical problem in polymer structures and polymer composite materials. Self-healing materials are able to repair micro cracks, thus their preventing propagation and catastrophic failure of structural components. One of the self-healing approaches presented in the literature involves the use of solvents which react with the polymer. The objective of this research is to investigate a procedure to encapsulate solvents in halloysite nanotubes to promote self-healing ability in epoxy. Healing is triggered by crack propagation through embedded nanotubes in the polymer, which then release the liquid sovent into the crack plane. Two solvents were considered in this work: dimethylsulfoxide (DMSO) and nitrobenzene. The nanotubes were coated using the layer-by-layer technique of oppositely charged polyelectrolytes: cetyltrimethylammonium bromide (CTAB) and sodium polyacrylate. Solvent encapsulation was verified by X-ray diffraction (XRD), Fourier transform infrared (FTIR), analysis thermogravimetry (TGA), adsorption and desorption of nitrogen and scanning electron microscopy (SEM). The introduction of the solvent DMSO into the cavity of the nanotubes was confirmed by the techniques employed. However, was not verified with nitrobenzene only promoted clay aggregation. The results suggest that the CTAB reacted with the halloystite to form a sealing layer on the surface of the nanotubes, thus encapsulating the solvent, while this was not verified using sodium polyacrylate.

Detecção e diagnostico de agarramento em válvulas posicionadoras

Valve stiction, or static friction, in control loops is a common problem in modern industrial processes. Recently, many studies have been developed to understand, reproduce and detect such problem, but quantification still remains a challenge. Since the valve position (mv) is normally unknown in an industrial process, the main challenge is to diagnose stiction knowing only the output signals of the process (pv) and the control signal (op). This paper presents an Artificial Neural Network approach in order to detect and quantify the amount of static friction using only the pv and op information. Different methods for preprocessing the training set of the neural network are presented. Those methods are based on the calculation of centroid and Fourier Transform. The proposal is validated using a simulated process and the results show a satisfactory measurement of stiction.

Desenvolvimento de um material têxtil termosenssível com micro/nanocápsulas imobilizadas em fibras regeneradas

Intelligent and functional Textile Materials have been widely developed and researched with the purpose of being used in several areas of science and technology. These fibrous materials require different chemical and physical properties to obtain a multifunctional material. With the advent of nanotechnology, the techniques developed, being used as essential tools to characterize these new materials qualitatively. Lately the application of micro and nanomaterials in textile substrates has been the objective of many studies, but many of these nanomaterials have not been optimized for their application, which has resulted in increased costs and environmental pollution, because there is still no satisfactory effluent treatment available for these nanomaterials. Soybean fiber has low adsorption for thermosensitive micro and nanocapsules due to their incompatibility of their surface charges. For this reason, in this work initially chitosan was synthesized to functionalise soybean fibres. Chitosan is a natural polyelectrolyte with a high density of positive charges, these fibres have negative charges as well as the micro/nanocápsules, for this reason the chitosan acts as auxiliary agent to cationize in order to fix the thermosensitive microcapsules in the textile substrate. Polyelectrolyte was characterized using particle size analyses and the measurement of zeta potential. For the morphological analysis scanning Electron Microscopy (SEM) and x-Ray Diffraction (XRD) and to study the thermal properties, thermogravimetric analysis (TGA), Differential Scanning Calorimetry (DSC), Near Infrared Spectroscopy analysis in the Region of the Fourier Transform Infrared (FTIR), colourimetry using UV-VIS spectrum were simultaneously performed on the substrate. From the measurement of zeta potential and in the determination of the particle size, stability of electrostatic chitosan was observed around 31.55mV and 291.0 nm respectively. The result obtained with (GD) for chitosan extracted from shrimp was 70 %, which according to the literature survey can be considered as chitosan. To optimize the dyeing process a statistical software, Design expert was used. The surface functionalisation of textile substrate with 2% chitosan showed the best result of K/S, being the parameter used for the experimental design, in which this showed the best response of dyeing absorbance in the range of 2.624. It was noted that soy knitting dyed with the thermosensitive micro andnanocapsules property showed excellent washing solidity, which was observed after 25 home washes, and significant K/S values.

Tratamento de argila montmorilonítica para a produção de nanocompósitos poliméricos com retardo de propagação de chama

Compared to conventional composites, polymer matrix nanocomposites typically exhibit enhanced properties at a significantly lower filler volume fraction. Studies published in the literature indicate t hat the addition of nanosilicate s can increase the resistance to flame propagation in polymers. In this work, a treatment of montmorillonite (MMT) nano clay and the effect of its ad dition o n flame propagation characteristics of vinyl ester were studied. The resea rch was conducted in two stages. The first stage focused on the purification and activation of the MMT clay collected from a natural deposit to improve compatibility with the polymer matrix . Clay modification with sodium acetate was also studied to improve particle dispersion in the polymer. The second step was focused on the effect of the addition of the treated clay on nanocomposites ’ properties. Nanocomposites with clay con tents of 1, 2, 4 wt. % were processed. T he techniques for the characterization of the clay included X - ray fluorescence (XRF), X - r ay d iffraction (XRD), thermogravimetric a nalysis (TGA), d ifferential scanning c alorimetry (DSC) , s urface area (BET) and Fourier transform infrared spectroscopy (FTIR). For t he characterization of the nanocomposites , the techniques used were thermogravimetric a nalysis (TGA) , differential scanning c alorimetry (DSC), Fourier transform infrared spectroscopy (FTIR) , scanning electron mi croscopy (SEM), transmission electron m icroscopy (TEM), and the determination of tensile strength, modulus of elasticity and resistance to flame propagation. According to the results, the purification and activation treatment with freeze - drying used in thi s work for the montmorillonite clay was efficient to promote compatibility and dispersion in the polymer matrix as evidenced by the characterization of the nanocomposite s . It was also observed that the clay modifica tion using sodium acetate did not produce any significant effect to improve compatibilization of the clay with the polymer. The addition of the treated MMT resulted in a reduction of up to 53% in the polymer flame propagation speed and did not affect the mechanical tensile strength and modulus o f elas ticity of the polymer, indicating compatibility between the clay and polymer. The effectiveness in reducing flame propagation speed peaked for nanocomposites with 2 wt. % clay, indicating that this is the optimum clay concentration for this property. T he clay treatment used in this work enables the production of vinylester matrix nanocomposites with flame - retardancy properties .

Utilização da cinza da casca de arroz em cimentação de poços de petróleo

Pozzolanic materials such as rice husk ash are widely used to substitute part of cement, because they react with calcium hydroxide (CH) producing calcium silicate hydrate (C-S-H), which aggregate better physical, chemical and mechanical properties to the cement slurry. The usage of rice husk biomass ash from agribusiness in addition to or partially replacing cement is a noble purpose and a good way of sustainable development which currently is an obsession around the world. The ashes utilized in this study were characterized by: scanning electron microscopy technique (SEM), Fourier transform infrared spectroscopy (FTIR), Energy-dispersive X-ray spectroscopy (EDX) and BET method. The pozzolanic activity of RHA and WRHA in cement slurries was evaluated by: thermal-gravimetric technique and derivative thermogravimetry (TGA/DTG), X-ray diffraction (XRD) and Compressive Strength. The slurries formulated with additions of 10% and 20% of RHA and WRHA were cured for 28 days at 58 °C. The results of thermal analysis demonstrated that a 20% WRHA addition caused a reduction of approximately 73% of Portlandite (calcium hydroxide – CH) phase related to standard slurry (STD). The XRD scans also demonstrated the reduction of the Portlandite peaks’ intensity for each slurry compared with STD slurry. The RHA and WRHA react chemically with Portlandite producing calcium silicate hydrate (C-S-H), confirming their effect as a pozzolanic agent. The WRHA presented the best results as a pozzolanic material.

Classificador máquina de suporte vetorial com análise de Fourier aplicada em dados de EEG e EMG

The classifier support vector machine is used in several problems in various areas of knowledge. Basically the method used in this classier is to end the hyperplane that maximizes the distance between the groups, to increase the generalization of the classifier. In this work, we treated some problems of binary classification of data obtained by electroencephalography (EEG) and electromyography (EMG) using Support Vector Machine with some complementary techniques, such as: Principal Component Analysis to identify the active regions of the brain, the periodogram method which is obtained by Fourier analysis to help discriminate between groups and Simple Moving Average to eliminate some of the existing noise in the data. It was developed two functions in the software R, for the realization of training tasks and classification. Also, it was proposed two weights systems and a summarized measure to help on deciding in classification of groups. The application of these techniques, weights and the summarized measure in the classier, showed quite satisfactory results, where the best results were an average rate of 95.31% to visual stimuli data, 100% of correct classification for epilepsy data and rates of 91.22% and 96.89% to object motion data for two subjects.

Estudo de famílias wavelets para aplicação na análise em DOAS

Trace gases are important to our environment even though their presence comes only by ‘traces’, but their concentrations must be monitored, so any necessary interventions can be done at the right time. There are some lower and upper boundaries which produce nice conditions for our lives and then monitoring trace gases comes as an essential task nowadays to be accomplished by many techniques. One of them is the differential optical absorption spectroscopy (DOAS), which consists mathematically on a regression - the classical method uses least-squares - to retrieve the trace gases concentrations. In order to achieve better results, many works have tried out different techniques instead of the classical approach. Some have tried to preprocess the signals to be analyzed by a denoising procedure - e.g. discrete wavelet transform (DWT). This work presents a semi-empirical study to find out the most suitable DWT family to be used in this denoising. The search seeks among many well-known families the one to better remove the noise, keeping the original signal’s main features, then by decreasing the noise, the residual left after the regression is done decreases too. The analysis take account the wavelet decomposition level, the threshold to be applied on the detail coefficients and how to apply them - hard or soft thresholding. The signals used come from an open and online data base which contains characteristic signals from some trace gases usually studied.

Estudo de famílias wavelets para aplicação na análise em DOAS

Trace gases are important to our environment even though their presence comes only by ‘traces’, but their concentrations must be monitored, so any necessary interventions can be done at the right time. There are some lower and upper boundaries which produce nice conditions for our lives and then monitoring trace gases comes as an essential task nowadays to be accomplished by many techniques. One of them is the differential optical absorption spectroscopy (DOAS), which consists mathematically on a regression - the classical method uses least-squares - to retrieve the trace gases concentrations. In order to achieve better results, many works have tried out different techniques instead of the classical approach. Some have tried to preprocess the signals to be analyzed by a denoising procedure - e.g. discrete wavelet transform (DWT). This work presents a semi-empirical study to find out the most suitable DWT family to be used in this denoising. The search seeks among many well-known families the one to better remove the noise, keeping the original signal’s main features, then by decreasing the noise, the residual left after the regression is done decreases too. The analysis take account the wavelet decomposition level, the threshold to be applied on the detail coefficients and how to apply them - hard or soft thresholding. The signals used come from an open and online data base which contains characteristic signals from some trace gases usually studied.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

Síntese e caracterização de complexos de Co(II) e Ni(II) com l-dopa, carbidopa e benzimidazol

Were synthesized in this work in the following aqueous solution coordination compounds: [Ni(LDP)(H2O)2Cl2].2H2O, [Co(LDP)Cl2].3H2O, [Ni(CDP)Cl2].4H2O, [Co(CDP)Cl2].4H2O, [Ni(BDZ)2Cl2].4H2O and [Co(BDZ)2Cl2(H2O)2]. These complexes were synthesized by stoichiometric addition of the binder in the respective metal chloride solutions. Precipitation occurred after drying the solvent at room temperature. The characterization and proposed structures were made using conventional analysis methods such as elemental analysis (CHN), absorption spectroscopy in the infrared Fourier transform spectroscopy (FTIR), X-ray diffraction by the powder method and Technical thermoanalytical TG / DTG (thermogravimetry / derivative thermogravimetry) and DSC (differential scanning calorimetry). These techniques provided information on dehydration, coordination modes, thermal performance, composition and structure of the synthesized compounds. The results of the TG curve, it was possible to establish the general formula of each compound synthesized. The analysis of X-ray diffraction was observed that four of the synthesized complex crystal structure which does not exhibit the complex was obtained from Ldopa and carbidopa and the complex obtained from benzimidazole was obtained crystal structures. The observations of the spectra in the infrared region suggested a monodentate ligand coordination to metal centers through its amine group for all complexes. The TG-DTG and DSC curves provide important information and on the behavior and thermal decomposition of the synthesized compounds. The molar conductivity data indicated that the solutions of the complexes formed behave as a nonelectrolyte, which implies that chlorine is coordinated to the central atom in the complex.

Síntese e caracterização de complexos de Co(II) e Ni(II) com l-dopa, carbidopa e benzimidazol

Were synthesized in this work in the following aqueous solution coordination compounds: [Ni(LDP)(H2O)2Cl2].2H2O, [Co(LDP)Cl2].3H2O, [Ni(CDP)Cl2].4H2O, [Co(CDP)Cl2].4H2O, [Ni(BDZ)2Cl2].4H2O and [Co(BDZ)2Cl2(H2O)2]. These complexes were synthesized by stoichiometric addition of the binder in the respective metal chloride solutions. Precipitation occurred after drying the solvent at room temperature. The characterization and proposed structures were made using conventional analysis methods such as elemental analysis (CHN), absorption spectroscopy in the infrared Fourier transform spectroscopy (FTIR), X-ray diffraction by the powder method and Technical thermoanalytical TG / DTG (thermogravimetry / derivative thermogravimetry) and DSC (differential scanning calorimetry). These techniques provided information on dehydration, coordination modes, thermal performance, composition and structure of the synthesized compounds. The results of the TG curve, it was possible to establish the general formula of each compound synthesized. The analysis of X-ray diffraction was observed that four of the synthesized complex crystal structure which does not exhibit the complex was obtained from Ldopa and carbidopa and the complex obtained from benzimidazole was obtained crystal structures. The observations of the spectra in the infrared region suggested a monodentate ligand coordination to metal centers through its amine group for all complexes. The TG-DTG and DSC curves provide important information and on the behavior and thermal decomposition of the synthesized compounds. The molar conductivity data indicated that the solutions of the complexes formed behave as a nonelectrolyte, which implies that chlorine is coordinated to the central atom in the complex.

Utilização da transformada de wavelet para detecção de variações anormais de tensão em sistemas elétricos com geração distribuída

The increasing demand in electricity and decrease forecast, increasingly, of fossil fuel reserves, as well as increasing environmental concern in the use of these have generated a concern about the quality of electricity generation, making it well welcome new investments in generation through alternative, clean and renewable sources. Distributed generation is one of the main solutions for the independent and selfsufficient generating systems, such as the sugarcane industry. This sector has grown considerably, contributing expressively in the production of electricity to the distribution networks. Faced with this situation, one of the main objectives of this study is to propose the implementation of an algorithm to detect islanding disturbances in the electrical system, characterized by situations of under- or overvoltage. The algorithm should also commonly quantize the time that the system was operating in these conditions, to check the possible consequences that will be caused in the electric power system. In order to achieve this it used the technique of wavelet multiresolution analysis (AMR) for detecting the generated disorders. The data obtained can be processed so as to be used for a possible predictive maintenance in the protection equipment of electrical network, since they are prone to damage on prolonged operation under abnormal conditions of frequency and voltage.