Estudo do uso de hidrotalcites de Mg-Al como catalisadores heterogéneos para produção de biodiesel

A transesterificação de óleos vegetais ou gorduras animais com um álcool de baixo peso molecular é o principal processo utilizado na produção de biodiesel. Actualmente os processos industriais utilizam catalisadores homogéneos para acelerar a reacção. No entanto a utilização de catalisadores heterogéneos, no processo de transesterificação, tem sido sugerido por vários investigadores pois, são amigos do ambiente e podem ser regenerados e reutilizados portanto possibilitam a utilização de processos contínuos. Neste contexto, a utilização de hidrotalcites Mg-Al, como catalisadores heterogéneos para produção de biodiesel foi investigada neste trabalho experimental. As hidrotalcites com diferentes razões molares Mg/Al (Mg/Al=1, 2, 3 e 4) foram preparadas pelo método de co-precipitação. As diversas matrizes catalíticas obtidas, calcinadas a diferentes temperaturas, foram caracterizadas por difracção de raios X (DRX), análise térmica (TG-DSC), espectroscopia de infravermelhos (MIR), microscopia electrónica de varrimento (SEM) e isotérmicas de adsorção com azoto (BET). Estes catalisadores foram testados na metanólise de óleos vegetais para produzir biodiesel. As hidrotalcites Mg/Al=2, HT2A e HT2B (preparada com metade da quantidade de NaOH) calcinadas a 507 ºC e 700 ºC, respectivamente, foram as que apresentaram melhores resultados ao catalisar a reacção com um rendimento em éster superior a 97%, utilizando 2.5% da massa de catalisador, em relação à massa do óleo, razão molar metanol/óleo igual a 12, temperatura reaccional de 65 ºC durante 4h. Foi também investigada a reutilização do catalisador e o efeito da temperatura de calcinação. Constatou-se que o catalisador hidrotalcite HT2B apresentou melhor comportamento catalítico pois permitiu catalisar a reacção de transesterificação até três ciclos reaccionais, convertendo em ésteres 97%, 92% e 34% no primeiro, segundo e terceiro ciclos reaccionais, respectivamente. A análise de, algumas propriedades do biodiesel obtido como, o índice de acidez, a viscosidade e o índice de iodo mostraram que os resultados obtidos estão dentro dos valores limite recomendados pela norma EN 14214. Em anexo apresenta-se uma comunicação à First International Conference on Materials for Energy, Karlsruhe, 2010.

Prensa electromagnética com recuperação de energia para corte e moldagem de chapas e tubos de AL

Foi desenvolvido um conversor de potência e atuador mecânico para a moldagem e corte, por ação de pressão magnética, de chapas e tubos de Al, com uma energia máxima de descarga de 10kJ. O conversor é composto por duas malhas de descarga em paralelo e duas malhas de recuperação de energia. O circuito é capaz de gerar uma corrente de pico de 50kA com uma taxa de variação máxima de 2kA/s através de um atuador, recuperar até 32% da sua energia inicial e diminuir o tempo de carga dos bancos de condensadores no mesmo valor, reduzindo assim a potência da fonte de alimentação primária Foram construídos vários atuadores de forma a otimizar o processo, considerando as várias funções pretendidas, como a deformação ou corte de chapas e compressão de tubos. O circuito elétrico aproximado desenvolvido em Matlab/Simulink foi validado, considerando apenas o funcionamento da malha primária sem o atuador e a dinâmica associada, sendo capaz de simular as respostas do sistema para várias situações específicas, tornando-se numa ferramenta para otimização do mesmo. Experimentalmente, os resultados obtidos provam ser possível cortar chapas Al de 0,5 e 0,8mm, com apenas 13% da energia total do circuito, e comprimir tubos de Al com 2mm de espessura e 50mm de diâmetro com apenas 2,4kJ. A topologia do circuito e a construção da máquina tiveram em conta vários aspetos que melhoram a proteção de pessoas e equipamentos e devida à sua configuração este é capaz de suportar variações de capacidade nos bancos de condensadores e variações de indutância nas bobinas de recuperação de energia sem se danificar.

Magnesium, insulin resistance and body composition in healthy postmenopausal women

Objective: This study was conducted to determine the association between magnesium (Mg), body composition and insulin resistance in 136 sedentary postmenopausal women, 50 to 77 years of age. Methods: Diabetics, hypertensives and women on hormonal replacement therapy were excluded and the remaining 74 were divided according to BMI≥25 (obese: OG) and BMI<25 kg/m2 (non-obese: NOG). Nutritional data disclosed that intakes were high for protein and saturated fat, low for carbohydrates, polyunsaturated fat and Mg and normal for the other nutrients, according to recommended dietary allowances (RDA). Mg values in red blood cells (RBC-Mg) and plasma (P-Mg), were determined, as were fasting glucose, and insulin levels, Homeostasis Model Assessment (HOMA), body mass index (BMI), body fat percent (BF %), abdominal fat (AF) and free fat mass (FFM). Results: RBC-Mg values were low in both groups when compared with normal values. There were significant differences in body composition parameters, HOMA and insulin levels, with higher basal insulin levels in OG. RBC-Mg was directly correlated with insulin, HOMA and FFM in both groups, according to Pearson correlations. HOMA in OG was also directly correlated with BMI, FFM and AF. In NOG, HOMA was only correlated with FFM. The low RBC-Mg levels observed were probably due to low Mg intake and to deregulation of factors that control Mg homeostasis during menopause. Conclusions: Both Mg deficit and obesity may independently lead to a higher risk for insulin resistance and cardiovascular disease.

ECG Analysis Using Consensus Clustering

Biosignals analysis has become widespread, upstaging their typical use in clinical settings. Electrocardiography (ECG) plays a central role in patient monitoring as a diagnosis tool in today's medicine and as an emerging biometric trait. In this paper we adopt a consensus clustering approach for the unsupervised analysis of an ECG-based biometric records. This type of analysis highlights natural groups within the population under investigation, which can be correlated with ground truth information in order to gain more insights about the data. Preliminary results are promising, for meaningful clusters are extracted from the population under analysis. © 2014 EURASIP.

Hacia una introducción selectiva al teatro en Portugal: breve encuadramiento

Texto dividido em duas partes, na primeira aborda-se a evolução do teatro em Portugal do pós segunda grande guerra à contemporaneidade, focando particularmente o experimentalismo do final dos anos quarenta, um período que se pode caracterizar por uma ânsia de renovação e modernização; a constituição de um teatro independente fortemente politizado nos anos setenta; e a pluralidade da cena contemporânea. Na segunda parte aborda-se a alegada incapacidade atávica dos escritores portugueses para a escrita dramática, mapeando os nomes mais significativos do pós-25 de Abril de 1974 até à contemporaneidade.

"In-vitro" corrosion behaviour of the magnesium alloy with Al and Zn (AZ31) protected with a biodegradable polycaprolactone coating loaded with hydroxyapatite and cephalexin

Mg alloys are very susceptible to corrosion in physiological media. This behaviour limits its widespread use in biomedical applications as bioresorbable implants, but it can be controlled by applying protective coatings. On one hand, coatings must delay and control the degradation process of the bare alloy and, on the other hand, they must be functional and biocompatible. In this study a biocompatible polycaprolactone (PCL) coating was functionalised with nano hydroxyapatite (HA) particles for enhanced biocompatibility and with an antibiotic, cephalexin, for anti-bacterial purposes and applied on the AZ31 alloy. The chemical composition and the surface morphology of the coated samples, before and after the corrosion tests, were studied by scanning electron microscopy (SEM) coupled with energy dispersive x-ray analysis (EDX) and Raman. The results showed that the presence of additives induced the formation of agglomerates and defects in the coating that resulted in the formation of pores during immersion in Hanks' solution. The corrosion resistance of the coated samples was studied in Hank's solution by electrochemical impedance spectroscopy (EIS). The results evidenced that all the coatings can provide corrosion protection of the bare alloy. However, in the presence of the additives, corrosion protection decreased. The wetting behaviour of the coating was evaluated by the static contact angle method and it was found that the presence of both hydroxyapatite and cephalexin increased the hydrophilic behaviour of the surface. The results showed that it is possible to tailor a composite coating that can store an antibiotic and nano hydroxyapatite particles, while allowing to control the in-vitro corrosion degradation of the bioresorbable Mg alloy AZ31. (C) 2015 Elsevier Ltd. All rights reserved.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.