Desenvolvimento e caracterização de díodos orgânicos emissores de luz

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Effect of argon ion energy on the performance of silicon nitridemultilayer permeation barriers grown by hot-wire CVD on polymers

One of the authors (S.M.) acknowledges Direction des Relations Extérieures of Ecole Polytechnique for financial support.

The influence of nitrogen and oxygen additions on the thermal characteristics of aluminium-based thin films

The ternary aluminium oxynitride (AlNxOy) system offers the possibility to obtain a wide range of properties by tailoring the ratio between pure Al, AlNx and AlOy and therefore opening a significant number of possible applications. In this work the thermal behaviour of AlNxOy thin films was analysed by modulated infrared radiometry (MIRR), taking as reference the binary AlOy and AlNx systems. MIRR is a non-contact and non-destructive thermal wave measurement technique based on the excitation, propagation and detection of temperature oscillations of very small amplitudes. The intended change of the partial pressure of the reactive gas (N2 and/or O2) influenced the target condition and hence the deposition characteristics which, altogether, affected the composition and microstructure of the films. Based on the MIRR measurements and their qualitative and quantitative interpretation, some correlations between the thermal transport properties of the films and their chemical/physical properties have been found. Furthermore, the potential of such technique applied in this oxynitride system, which present a wide range of different physical responses, is also discussed. The experimental results obtained are consistent with those reported in previous works and show a high potential to fulfil the demands needed for the possible applications of the systems studied. They are clearly indicative of an adequate thermal response if this particular thin film system is aimed to be applied in small sensor devices or in electrodes for biosignal acquisition, such as those for electroencephalography or electromyography as it is the case of the main research area that is being developed in the group.