Ibuprofen-loaded poly(trimethylene carbonate-co-ϵ- caprolactone) electrospun fibres for nerve regeneration

The development of scaffolds that combine the delivery of drugs with the physical support provided by electrospun fibres holds great potential in the field of nerve regeneration. Here it is proposed the incorporation of ibuprofen, a well-known non-steroidal anti-inflammatory drug, in electrospun fibres of the statistical copolymer poly(trimethylene carbonate-co-ε-caprolactone) [P(TMC-CL)] to serve as a drug delivery system to enhance axonal regeneration in the context of a spinal cord lesion, by limiting the inflammatory response. P(TMC-CL) fibres were electrospun from mixtures of dichloromethane (DCM) and dimethylformamide (DMF). The solvent mixture applied influenced fibre morphology, as well as mean fibre diameter, which decreased as the DMF content in solution increased. Ibuprofen-loaded fibres were prepared from P(TMC-CL) solutions containing 5% ibuprofen (w/w of polymer). Increasing drug content to 10% led to jet instability, resulting in the formation of a less homogeneous fibrous mesh. Under the optimized conditions, drug-loading efficiency was above 80%. Confocal Raman mapping showed no preferential distribution of ibuprofen in P(TMC-CL) fibres. Under physiological conditions ibuprofen was released in 24h. The release process being diffusion-dependent for fibres prepared from DCM solutions, in contrast to fibres prepared from DCM-DMF mixtures where burst release occurred. The biological activity of the drug released was demonstrated using human-derived macrophages. The release of prostaglandin E2 to the cell culture medium was reduced when cells were incubated with ibuprofen-loaded P(TMC-CL) fibres, confirming the biological significance of the drug delivery strategy presented. Overall, this study constitutes an important contribution to the design of a P(TMC-CL)-based nerve conduit with anti-inflammatory properties.

Caracterização laboratorial de misturas betuminosas com incorporação de agregado siderúrgico inerte para construção (ASIC)

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Vias de Comunicação e Transportes

Estudo da compactabilidade em laboratório de misturas betuminosas com resíduos plásticos

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização em Vias de Comunicação e Transportes

Occupational co-exposure to several mycotoxins in the waste management setting

Contrary to fungi, exposure to mycotoxins is not usually identified as a risk factor present in occupational settings. This is probably due to the inexistence of limits regarding concentration of airborne mycotoxins, and also due to the fact that these compounds are rarely monitored in occupational environments. Despite the optimal conditions for fungal growth and, consequently, for mycotoxins production in all the waste management chain, only a few articles were dedicated to study occupational exposure to mycotoxins in this occupational setting. Aim of study: A study was developed in Portugal aiming to assess occupational co-exposure to mycotoxins in the waste management setting.

Fungal contamination in feed production in Portugal: what to expect regarding mycotoxins contamination?

Fungi on crops produce mycotoxins in the field, during handling, and in storage. Exposure of animals and humans are usually through consumption of contaminated feedstuffs or foods. Molds can grow and mycotoxins can be produced either pre-harvest or post-harvest, during storage, transport, processing, or feeding. Worldwide, approximately 25% of crops are affected by mycotoxins annually. Because of this is possible to concluded that mycotoxins occur frequently in a variety of feedstuffs that are given to animals causing several effects: subclinical losses in performance, increases the incidence of disease and reduced reproductive performance. Aim of study: A study was developed intending to know environmental fungal contamination in a Portuguese feed production unit. Corn, wheat and soybeans were the most common cereals used in the feed production.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Mechanical characterization of polyhydroxyalkanoate and poly (lactic acid) blends

In this work, the mechanical behavior of polyhyroxyalkanoate (PHA)/poly(lactic acid) (PLA) blends is investigated in a wide range of compositions. The mechanical properties can be optimized by varying the PHA contents of the blend. The flexural and tensile properties were estimated by different models: the rule of mixtures, Kerner–Uemura–Takayanagi (KUT) model, Nicolai–Narkis model and Béla–Pukánsky model. This study was aimed at investigating the adhesion between the two material phases. The results anticipate a good adhesion between both phases. Nevertheless, for low levels of incorporation of PHA (up to 30%), where PLA is expectantly the matrix, the experimental data seem to deviate from the perfect adhesion models, suggesting a decrease in the adhesion between both polymeric phases when PHA is the disperse phase. For the tensile modulus, a linear relationship is found, following the rules of mixtures (or a KUT model with perfect adhesion between phases) denoting a good adhesion between the phases over the composition range. The incorporation of PHA in the blend leads to a decrease in the flexural modulus but, at the same time, increases the tensile modulus. The impact energy of the blends varies more than 157% over the entire composition. For blends with PHA weight fraction lower than 50%, the impact strength of the blend is higher than the pure base polymers. The highest synergetic effect is found when the PLA is the matrix and the PHA is the disperse phase for the blend PHA/PLA of 30/70. The second maximum is found for the inverse composition of 70/30. PLA has a heat-deflection temperature (HDT) substantially lower than PHA. For the blends, the HDT increases with the increment in the percentage of the incorporation of PHA. With up to 50% PHA (PLA as matrix), the HDT is practically constant and equal to PLA value. Above this point (PHA matrix), the HDT of the polymer blends increases linearly with the percentage of addition of PHA.

Eliminação de dioxinas em cinzas volantes com nanopartículas de ferro

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Química e Bioquímica

The effect of metal transfer modes and shielding gas composition on the emission of ultraafine particles in MAG steel welding

The present study aims to characterize ultrafine particles emitted during gas metal arc welding of mild steel and stainless steel, using different shielding gas mixtures, and to evaluate the effect of metal transfer modes, controlled by both processing parameters and shielding gas composition, on the quantity and morphology of the ultrafine particles. It was found that the amount of emitted ultrafine particles (measured by particle number and alveolar deposited surface area) are clearly dependent from the main welding parameters, namely the current intensity and the heat input of the Welding process. The emission of airborne ultrafine particles increases with the current intensity as fume formation rate does. When comparing the shielding gas mixtures, higher emissions were observed for more oxidizing mixtures, that is, with higher CO2 content, which means that these mixtures originate higher concentrations of ultrafine particles (as measured by number of particles. by cubic centimeter of air) and higher values of alveolar deposited surface area of particles, thus resulting in a more hazardous condition regarding welders exposure.

Emission of airborne ultrafine particles during welding of steel plates

The present study is focused on the characterization of ultrafine particles emitted in welding of steel using mixtures of Ar+CO2, and intends to analyze which are the main process parameters which may have influence on the emission itself. It was found that the amount of emitted ultrafine particles (measured by particle number and alveolar deposited surface area) are clearly dependent from the distance to the welding front and also from the main welding parameters, namely the current intensity and heat input in the welding process. The emission of airborne ultrafine particles seem to increase with the current intensity as fume formation rate does. When comparing the tested gas mixtures, higher emissions are observed for more oxidant mixtures, that is, mixtures with higher CO2 content, which result in higher arc stability. The later mixtures originate higher concentrations of ultrafine particles (as measured by number of particles by cm3 of air) and higher values of alveolar deposited surface area of particles, thus resulting in a more hazardous condition regarding worker's exposure. © 2014 Sociedade Portuguesa de Materiais (SPM). Published by Elsevier España, S.L. All rights reserved.

Synthesis and characterization of COPPER(II) 4´-phenyl-terpyridine compounds and catalytic application for aerobic oxidation of benzylic alcohols

The reactions between 4'-phenyl-terpyridine (L) and nitrate, acetate or chloride Cu(II) salts led to the formation of [Cu(NO3)(2)L] (1), [Cu(OCOCH3)(2)L]center dot CH2Cl2 (2 center dot CH2Cl2)and [CuCl2L]center dot[Cu(Cl)(mu-Cl)L](2) (3), respectively. Upon dissolving 1 in mixtures of DMSO-MeOH or EtOH-DMF the compounds [Cu(H2O){OS(CH3)(2)}L]-(NO3)(2) (4) and [Cu(HO)(CH3CH2OH)L](NO3) (5) were obtained, in this order. Reaction of 3 with AgSO3CF3 led to [CuCl(OSO2CF3)L] (6). The compounds were characterized by ESI-MS, IR, elemental analysis, electrochemical techniques and, for 2-6, also by single crystal X-ray diffraction. They undergo, by cyclic voltammetry, two single-electron irreversible reductions assigned to Cu(II) -> Cu(I)and Cu(I) -> Cu(0) and, for those of the same structural type, the reduction potential appears to correlate with the summation of the values of the Lever electrochemical EL ligand parameter, which is reported for the first time for copper complexes. Complexes 1-6 in combination with TEMPO (2,2,6,6-tetramethylpiperidinyl-1-oxyl radical) can exhibit a high catalytic activity, under mild conditions and in alkaline aqueous solution, for the aerobic oxidation of benzylic alcohols. Molar yields up to 94% (based on the alcohol) with TON values up to 320 were achieved after 22 h.

UV-Vis spectroscopic study preferential solvation and intermolecular interactions in methanol/1-propanol/acetonitrile by means of solvatochromic probes

Solvatochromic UV-Vis shifts of four indicators (4-nitroaniline, 4-nitroanisole, 4-nitrophenol and N,N-dimethy-1-4-nitro aniline) have been measured at 298.15 K in the ternary mixture methano1/1-propanol/acetonitrile (MeOH/1-PrOH/MeCN) in a total of 22 mole fractions, along with 18 additional mole fractions for each of the corresponding binary mixtures, MeOH/1-PrOH, 1-PrOH/MeCN and MeOH/MeCN. These values, combined with our previous experimental results for 2,6-dipheny1-4-(2,4,6-triphenylpyridinium-1-yl)phenolate (Reichardt's betaine dye) in the same mixtures, permitted the computation of the Kamlet-Taft solvent parameters, alpha, beta, and pi*. The rationalization of the spectroscopic behavior of each probe within each mixture's whole mole fraction range was achieved through the use of the Bosch and Roses preferential solvation model. The applied model allowed the identification of synergistic behaviors in MeCN/alcohol mixtures and thus to infer the existence of solvent complexes in solution. Also, the addition of small amounts of MeCN to the binary mixtures was seen to cause a significant variation in pi*, whereas the addition of alcohol to MeCN mixtures always lead to a sudden change in a and The behavior of these parameters in the ternary mixture was shown to be mainly determined by the contributions of the underlying binary mixtures. (C) 2014 Elsevier B.V. All rights reserved.

A study on the modeling of sandwich functionally graded particulate composites

Dual-phase functionally graded materials are a particular type of composite materials whose properties are tailored to vary continuously, depending on its two constituent's composition distribution, and which use is increasing on the most diverse application fields. These materials are known to provide superior thermal and mechanical performances when compared to the traditional laminated composites, exactly because of this continuous properties variation characteristic, which enables among other advantages smoother stresses distribution profile. In this paper we study the influence of different homogenization schemes, namely the schemes due to Voigt, Hashin-Shtrikman and Mod-Tanaka, which can be used to obtain bounds estimates for the material properties of particulate composite structures. To achieve this goal we also use a set of finite element models based on higher order shear deformation theories and also on first order theory. From the studies carried out, on linear static analyses and on free vibration analyses, it is shown that the bounds estimates are as important as the deformation kinematics basis assumed to analyse these types of multifunctional structures. Concerning to the homogenization schemes studied, it is shown that Mori-Tanaka and Hashin-Shtrikman estimates lead to less conservative results when compared to Voigt rule of mixtures.

Densities and refractive indices for the ternary mixture methanol/propan-1-Ol/acetonitrile

Refractive indices, n(D), and densities, rho, at 298.15 K were measured for the ternary mixture methanol (MeOH)/propan-1-ol (1-PrOH)/acetonitrile (MeCN) for a total of 22 mole fractions, along with 18 mole fractions of each of the corresponding binary mixtures, methanol/propan-1-ol, propan-1-ol/acetonitrile and methanol/acetonitrile. The variation of excess refractive indices and excess molar volumes with composition was modeled by the Redlich-Kister polynomial function in the case of binary mixtures and by the Cibulka equation for the ternary mixture. A thermodynamic approach to excess refractive indices, recently proposed by other authors, was applied for the first time to ternary liquid mixtures. Structural effects were identified and interpreted both in the binary and ternary systems. A complex relationship between excess refractive indices and excess molar volumes was identified, revealing all four possible sign combinations between these two properties. Structuring of the mixtures was also discussed on the basis of partial molar volumes of the binary and ternary mixtures.

An interdisciplinary approach to the study of colour in portuguese manuscript illuminations

Dissertação apresentada para obtenção do Grau de Doutor em Conservação e Restauro, especialidade Teoria, História e Técnicas, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia