Application of acoustic emission technique for bond characterization in FRP-masonry systems

The acoustic emission (AE) technique is used for investigating the interfacial fracture and damage propagation in GFRP-and SRG-strengthened bricks during debonding tests. The bond behavior is investigated through single-lap shear bond tests and the fracture progress during the tests is recorded by means of AE sensors. The fracture progress and active debonding mechanisms are characterized in both specimen types with the aim of AE outputs. Moreover, a clear distinction between the AE outputs of specimens with different failure modes, in both SRG-and GFRP-strengthened specimens, is found which allows characterizing the debonding failure mode based on acoustic emission data.

Enteroscopia por cápsula e doença de Crohn: Caminhos para a otimização

Tese de Doutoramento em Medicina

Cost-effective energy and carbon emission optimization in building renovation – A case-study in a low income neighbourhood

Construction sector is one of the major responsible for energy consumption and carbon emissions and renovation of existing buildings plays an important role in the actions to mitigate climate changes. Present work is based on the methodology developed in IEA Annex 56, allowing identifying cost optimal and cost effective renovation scenarios improving the energy performance. The analysed case study is a residential neighbourhood of the municipality of Gaia in Portugal. The analysis compares a reference renovation scenario (without improving the energy performance of the building) with a series of alternative renovation scenarios, including the one that is being implemented.

Effect of polymeric matrix on morphology and photoelectric properties of polymer-quantum dots nanocomposites

Recently, CdTe semiconductor quantum dots (QDs) have attracted great interest due to their unique properties [1]. Their dispersion into polymeric matrices would be very for several optoelectronics applications. Despite its importance, there has been relatively little work done on charge transport in the QD polymeric films [2], which is mainly affected by their structural and morphological properties. In the present work, polymer-quantum dot nanocomposites films based on optically transparent polymers in the visible spectral range and CdTe QDs with controlled particle size and emission wavelength, were prepared via solvent casting. Photoluminescent (PL) measurements indicate different emission intensity of the nanocomposites. A blue shift of the emission peak compared to that of QDs in solution occurred, which is attributed to the QDs environment changes. The morphological and structural properties of the CdTe nanocomposites were evaluated. Since better QDs dispersion was achieved, PMMA seemed to be the most promising matrix. Electrical properties measurements indicate an ohmic behavior.

Active magneto-optical control of spontaneous emission in graphene

We investigate the spontaneous emission rate of a two-level quantum emitter near a graphene-coated substrate under the influence of an external magnetic field or strain induced pseudo-magnetic field. We demonstrate that the application of the magnetic field can substantially increase or decrease the decay rate. We show that a suppression as large as 99$\%$ in the Purcell factor is achieved even for moderate magnetic fields. The emitter's lifetime is a discontinuous function of $|{\bf B}|$, which is a direct consequence of the occurrence of discrete Landau levels in graphene. We demonstrate that, in the near-field regime, the magnetic field enables an unprecedented control of the decay pathways into which the photon/polariton can be emitted. Our findings strongly suggest that a magnetic field could act as an efficient agent for on-demand, active control of light-matter interactions in graphene at the quantum level.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.