Chemical analysis of 17th century Millefiori glasses excavated in the Monastery of Sta. Clara‐a‐Velha: comparison with Venetian and façon‐de‐Venise production

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Conservação e Restauro,Área de especialização Cerâmica e Vidro

Real fábrica de vidros de Coina – Chemical analysis of archaeological glass fragments

Actas do 17º Congresso da Associação Internacional para a História do Vidro

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.

Chemical reaction network of flavylium ions in heterogeneous media

Dissertação apresentada para a obtenção do Grau de Doutor em Química, especialidade em Química-Física, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Energy-dispersive x-ray fluorescence analysis of modern coloured glasses from Marinha Grande (Portugal)

X-Ray Spectrom. 2003; 32: 396–401

Analysis of early synthetic dyes with HPLC-DAD-MS: an important database for analysis of colorants used in cultural heritage

Presented thesis at Faculdade de Ciências e Tecnologias, Universidade de Lisboa, to obtain the Master Degree in Conservation and Restoration of Textiles

The sovereign CDS-Bond basis: From a crisis perspective

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Physical and chemical assessment of lime–metakaolin mortars: Influence of binder:aggregate ratio

Cement & Concrete Composites 45 (2014) 264–271

Analysis, testing and development of safe cleaning methods of rusted stone material

Three different treatments were applied on several specimens of dolomitic and calcitic marble, properly stained with rust to mimic real situations (the stone specimens were exposed to the natural environment for about six months in contact with rusted iron). Thirty six marble specimens, eighteen calcitic and eighteen dolomitic, were characterized before and after treatment and monitored throughout the cleaning tests. The specimens were characterized by SEM-EDS (Scanning Electron Microscopy coupled with Energy Dispersion System), XRD (XRay Diffraction), XRF (X-Ray Fluorescence), FTIR (Fourier Transform Infrared Spectroscopy) and color measurements. It was also made a microscopic and macroscopic analysis of the stone surface along with the tests of short and long term capillary absorption. A series of test trials were conducted in order to understand which concentrations and contact times best suits to this purpose, to confirm what had been written to date in the literature. We sought to develop new methods of treatment application, skipping the usual methods of applying chemical treatments on stone substrates, with the use of cellulose poultice, resorting to the agar, a gel already used in many other areas, being something new in this area, which possesses great applicability in the field of conservation of stone materials. After the application of the best methodology for cleaning, specimens were characterized again in order to understand which treatment was more effective and less harmful, both for the operator and the stone material. Very briefly conclusions were that for a very intense and deep penetration into the stone, a solution of 3.5% of SDT buffered with ammonium carbonate to pH around 7 applied with agar support would be indicated. For rust stains in its initial state, the use of Ammonium citrate at a concentration of 5% buffered with ammonium to pH 7 could be applied more than once until satisfactory results appear.

Novel ready-to-eat mango product using gellan gum as gelling agent: physico-chemical, microbial and sensory characteristics

Este trabalho foi efectuado com o apoio da Universidade de Lisboa, Instituto Superior de Agronomia com o Centro de Engenharia dos Biossistemas (CEER

Social impact bond feasibility study

Youth unemployment is one of the most pressing social issues in Portugal, often associated to a lack of skills. Faz-Te Forward (FFWD), a Portuguese employability programme, has demonstrated great potential for impact in solving this issue, especially amongst a neglected segment of the population – those belonging to “sandwich families”. The present thesis, integrated in the SIB Research Programme from the Social Investment Lab, evaluates the feasibility of this programme to be financed through a Social Impact Bond, an innovative outcomes-based financing model. From a data analysis undertaken to FFWD’s historical information, a business case for a SIB was developed.

Analysis of chromium behaviour and speciation during the electrodialytic process

The interest in chromium (Cr) arises from the widespread use of this heavy metal in various industrial processes that cause its release as liquid, solid and gaseous waste into the environment. The impact of Cr on the environment and living organisms primarily depends on its chemical form, since Cr(III) is an essential micronutrient for humans, other animals and plants, and Cr(VI) is highly toxic and a known human carcinogen. This study aimed to evaluate if the electrodialytic process (ED) is an appropriate treatment for Cr removal, through a critical overview of Cr speciation, before and after the ED experiments, to assess possible Cr(III)-Cr(VI) interconversions during the treatment. ED was the treatment technique applied to two types of matrices containing Cr: chromate copper arsenate (CCA) contaminated soil and municipal solid waste incineration (MSWI) fly ash. In order to study Cr remediation, three EDR set-ups were used: a new set-up, the combined cell (2/3C or 3/2C), with three compartments, alternating current between two anodes and different initial experimental conditions, one set-up with three compartments (3C cell) and the other set-up with two compartments (2C cell). The Cr removal rates obtained in this study were between 10-36% for the soil, and 1-13% for the fly ash. The highest Cr removal rates were achieved in the 26 days experiments: 36% for the soil, 13% for the fly ash. Regarding the 13 days experiments, the highest Cr removal rates were attained with the 2/3C set-up: 24% for the soil, 5% for the fly ash. The analysis of Cr(VI) was performed before and after ED experiments to evaluate eventual changes in Cr speciation during the treatment. This analysis was conducted by two methods: USEPA Method 3060A, for the extraction of Cr(VI); and Hach Company Method 8023, for the detection of Cr(VI). Despite the differences in Cr total concentration, both matrices presented a similar speciation, with Cr(III) being the main species found and Cr(VI) less than 3% of Cr total, before and after the treatment. For fly ash, Cr(VI) was initially below the detection limit of the method and remained that way after the treatment. For soil, Cr(VI) decreased after the treatment. Oxidation of Cr(III) to Cr(VI) did not occur during the ED process since there was no increase in Cr(VI) in the matrices after the treatment. Hence, the results of this study indicate that ED is an appropriate technique to remediate matrices containing Cr because it contributes to Cr removal, without causing Cr(III)-Cr(VI) interconversions.