The periodic table: contest and exhibition

The systemization and organization of ideas and concepts is an integral part of science. In chemistry, the organization of the periodic table of the chemical elements in the 1860s was one of the greatest scientific breakthroughs ever made and in fact during the 20th century it became a universally recognized scientific icon (1). The periodic table is the fundamental classificatory scheme of the elements and summarizes the realm of chemistry (2). Simply knowing the position of an element in the periodic table tells us about its properties and is usually enough to predict how the element will behave in a wide variety of different situations or reactions (1). Based on this potential mine of information, it is possible to make reliable predictions of the properties of the compounds that each element forms. Nowadays, the concept of the periodic table is starting to interact with other sciences and reports of periodic tables of amino acids (3), genetic codes (4), protein structures (5), and biology (6) can be found in the specialized literature. Symbiosis between science and art, for example, “The Periodic Table of The Elephants” (7), can also be seen. To appeal to a better understanding of the periodic table, the Instituto Superior de Engenharia do Instituto Politécnico do Porto and the Centro de Química da Universidade do Porto promoted a contest and exhibit with the goal of stimulating a wide and heterogeneous audience, ranging from young children and their parents to graduate students from several disciplines, to explore the nature of this icon. Imaginative educational activities such as contests (8–10), games (11, 12), and puzzles (13–15) provided a way to communicate with the general public with the goal of attracting students to science. This also constituted an interesting, informative, and entertaining alternative to non-interactive lectures. Simultaneously, artistic creativity was combined with scientific knowledge.

Self-assembly in chains, rings, and branches: a single component system with two critical points

Agências financiadoras: FCT - PEstOE/FIS/UI0618/2011; PTDC/FIS/098254/2008 ERC-PATCHYCOLLOIDS e MIUR-PRIN

A new A(4) model for lepton mixing

We present a new model of the lepton sector that uses a family symmetry A(4) to make predictions for lepton mixing which are invariant under any permutation of the three flavours. We show that those predictions broadly agree with the experimental data, leading to a largish sin(2)theta(12) greater than or similar to 0.34, to vertical bar cos delta vertical bar greater than or similar to 0.7, and to vertical bar 0.5 - sin(2)theta(23)vertical bar greater than or similar to 0.08; cos delta and 0.5 - sin(2)theta(23) are predicted to have identical signs. (C) 2013 Elsevier B.V. All rights reserved.

Study of optical and structural properties of Cu2ZnSnS4 thin films

Cu2ZnSnS4 is a promising semiconductor to be used as absorber in thin film solar cells. In this work, we investigated optical and structural properties of Cu2ZnSnS4 thin films grown by sulphurization of metallic precursors deposited on soda lime glass substrates. The crystalline phases were studied by X-ray diffraction measurements showing the presence of only the Cu2ZnSnS4 phase. The studied films were copper poor and zinc rich as shown by inductively coupled plasma mass spectroscopy. Scanning electron microscopy revealed a good crystallinity and compactness. An absorption coefficient varying between 3 and 4×104cm−1 was measured in the energy range between 1.75 and 3.5 eV. The band gap energy was estimated in 1.51 eV. Photoluminescence spectroscopy showed an asymmetric broad band emission. The dependence of this emission on the excitation power and temperature was investigated and compared to the predictions of the donor-acceptor-type transitions and radiative recombinations in the model of potential fluctuations. Experimental evidence was found to ascribe the observed emission to radiative transitions involving tail states created by potential fluctuations.

Modelling adhesive joints with cohesive zone models: effect of the cohesive law shape of the adhesive layer

Adhesively-bonded joints are extensively used in several fields of engineering. Cohesive Zone Models (CZM) have been used for the strength prediction of adhesive joints, as an add-in to Finite Element (FE) analyses that allows simulation of damage growth, by consideration of energetic principles. A useful feature of CZM is that different shapes can be developed for the cohesive laws, depending on the nature of the material or interface to be simulated, allowing an accurate strength prediction. This work studies the influence of the CZM shape (triangular, exponential or trapezoidal) used to model a thin adhesive layer in single-lap adhesive joints, for an estimation of its influence on the strength prediction under different material conditions. By performing this study, guidelines are provided on the possibility to use a CZM shape that may not be the most suited for a particular adhesive, but that may be more straightforward to use/implement and have less convergence problems (e.g. triangular shaped CZM), thus attaining the solution faster. The overall results showed that joints bonded with ductile adhesives are highly influenced by the CZM shape, and that the trapezoidal shape fits best the experimental data. Moreover, the smaller is the overlap length (LO), the greater is the influence of the CZM shape. On the other hand, the influence of the CZM shape can be neglected when using brittle adhesives, without compromising too much the accuracy of the strength predictions.

Experimental study of proton induced nuclear reactions in 6,7 Li

This work presents the results of the experimental study of proton induced nuclear reactions in lithium, namely the 7Li(p,α) 4He, 6Li(p,α) 3He and 7Li(p,p)7Li reactions. The amount of 7Li and 6Li identified as primordial and observed in very old stars of the Milky Way galactic halo strongly deviates from the predictions of primordial nucleosynthesis and stellar evolution models which depend, among other factors, on the cross sections of reactions like 7Li(p,α) 4He and 6Li(p,α) 3He. These discrepancies have triggered a large amount of research in the fields of stellar evolution, cosmology, pre-galactic evolution and low energy nuclear reactions. Focusing on nuclear reactions, this work has measured the 7Li(p,α) 4He and 6Li(p,α) 3He reactions cross sections (expressed in terms of the astrophysical S -factor) with higher accuracy, and the electron screening effects in these reactions for different environments (insulators and metallic targets). The 7Li(p,α) 4He angular distributions were also measured. These measurementstook place in two laboratory facilities, in the framework of the LUNA (Laboratory for Undergroud Nuclear Astrophysics) international collaboration, namely the Laboratorio ´ de Feixe de Ioes ˜ in ITN (Instituto Tecnologico ´ e Nuclear) Sacavem, ´ Portugal, and the Dynamitron-TandemLaboratorium in Ruhr-Universitat¨ Bochum, Germany. The ITN target chamber was modified to measure these nuclear reactions, with the design and construction of new components, the addition of one turbomolecular pump and a cold finger. The 7Li(p,α) 4He and 6Li(p,α) 3He reactions were measured concurrently with seven and four targets, respectively. These targets were produced in order to obtain adequate and stable lithium depth profiles. In metallic environments, the measured electron screening potential energies are much higher than the predictions of atomic-physics models. The Debye screening model applied to the metallic conduction electrons is able to explain these high values. It is a simple model, but also very robust. Concerning primordial nucleosynthesis and stellar evolution models, these results are very important as they show that laboratory measurements are well controlled, and the model inputs from these cross sections are therefore correct. In this work the 7Li(p,p)7Li differential cross section was also measured, which is useful to describe the 7Li(p,α) 4He entrance channel.

Metaheuristic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Metaheuristhic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

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.

Influence of the cohesive law parameters on the strength prediction of adhesively-bonded joints

Adhesive joints are largely employed nowadays as a fast and effective joining process. The respective techniques for strength prediction have also improved over the years. Cohesive Zone Models (CZM’s) coupled to Finite Element Method (FEM) analyses surpass the limitations of stress and fracture criteria and allow modelling damage. CZM’s require the energy release rates in tension (Gn) and shear (Gs) and respective fracture energies in tension (Gnc) and shear (Gsc). Additionally, the cohesive strengths (tn0 for tension and ts0 for shear) must also be defined. In this work, the influence of the CZM parameters of a triangular CZM used to model a thin adhesive layer is studied, to estimate their effect on the predictions. Some conclusions were drawn for the accuracy of the simulation results by variations of each one of these parameters.

Using a cohesive damage model to predict the tensile behaviour of CFRP single-strap repairs

This work addresses both experimental and numerical analyses regarding the tensile behaviour of CFRP single-strap repairs. Two fundamental geometrical parameters were studied: overlap length and patch thickness. The numerical model used ABAQUS® software and a developed cohesive mixed-mode damage model adequate for ductile adhesives, and implemented within interface finite elements. Stress analyses and strength predictions were carried out. Experimental and numerical comparisons were performed on failure modes, failure load and equivalent stiffness of the repair. Good correlation was found between experimental and numerical results, showing that the proposed model can be successfully applied to bonded joints or repairs.

Buckling strength of adhesively-bonded single and double-strap repairs on carbon-epoxy structures

This work reports on an experimental and finite element method (FEM) parametric study of adhesively-bonded single and double-strap repairs on carbon-epoxy structures under buckling unrestrained compression. The influence of the overlap length and patch thickness was evaluated. This loading gains a particular significance from the additional characteristic mechanisms of structures under compression, such as fibres microbuckling, for buckling restrained structures, or global buckling of the assembly, if no transverse restriction exists. The FEM analysis is based on the use of cohesive elements including mixed-mode criteria to simulate a cohesive fracture of the adhesive layer. Trapezoidal laws in pure modes I and II were used to account for the ductility of most structural adhesives. These laws were estimated for the adhesive used from double cantilever beam (DCB) and end-notched flexure (ENF) tests, respectively, using an inverse technique. The pure mode III cohesive law was equalled to the pure mode II one. Compression failure in the laminates was predicted using a stress-based criterion. The accurate FEM predictions open a good prospect for the reduction of the extensive experimentation in the design of carbon-epoxy repairs. Design principles were also established for these repairs under buckling.

Are there hidden scalars in LHC Higgs results?

The Higgs boson recently discovered at the Large Hadron Collider has shown to have couplings to the remaining particles well within what is predicted by the Standard Model. The search for other new heavy scalar states has so far revealed to be fruitless, imposing constraints on the existence of new scalar particles. However, it is still possible that any existing heavy scalars would preferentially decay to final states involving the light Higgs boson thus evading the current LHC bounds on heavy scalar states. Moreover, decays of the heavy scalars could increase the number of light Higgs bosons being produced. Since the number of light Higgs bosons decaying to Standard Model particles is within the predicted range, this could mean that part of the light Higgs bosons could have their origin in heavy scalar decays. This situation would occur if the light Higgs couplings to Standard Model particles were reduced by a concomitant amount. Using a very simple extension of the SM - the two-Higgs doublet model we show that in fact we could already be observing the effect of the heavy scalar states even if all results related to the Higgs are in excellent agreement with the Standard Model predictions.

Analysis of glycosylation heterogeneity in antibody production by Pichia pastoris

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

Quantitative description of the self-assembly of patchy particles into chains and rings

We numerically study a simple fluid composed of particles having a hard-core repulsion complemented by two patchy attractive sites on the particle poles. An appropriate choice of the patch angular width allows for the formation of ring structures which, at low temperatures and low densities, compete with the growth of linear aggregates. The simplicity of the model makes it possible to compare simulation results and theoretical predictions based on the Wertheim perturbation theory, specialized to the case in which ring formation is allowed. Such a comparison offers a unique framework for establishing the quality of the analytic predictions. We find that the Wertheim theory describes remarkably well the simulation results.