Abstract
New paths to increase the sensing performance of plasmonic sensors have been reported in recent years. There are several methodologies to achieve such purpose, namely by optimizing the nanostructure, nanomaterial and even the sensing platform. Recently the use nanoparticles over plasmonic thin films have been reported and shown sensitivity enhancement, when compared to a bare thin film. Nevertheless, a nanomaterial combination between NP and thin film has not been studied. In this work it was studied such plasmonic materials in order to optimize not only refractometric sensitivity but also decrease the resultant plasmonic band width. It was found that for Au, Ag and Cu thin films, the deposition of plasmonic nanoparticles resulted in an overall refractometric sensitivity and figure of merit (FOM) increase. The larger FOM increase was obtained for the Ag thin film, from 42 to 162 when coupled to Si nanoparticles. The greater sensitivity increase was achieved for a Cu thin film coupled to a Si nanoparticle, with an increase from 1745 to 3230 nm/RIU. © Published under licence by IOP Publishing Ltd.

Abstract
The interrogation of optic fiber sensors usually relies in complex and costly equipment with low portability due to their size such as Optical Spectrum Analyzers (OSA) or high-resolution spectrometers. Because of this, micro spectrometer devices, such as Micro-Electromechanical Systems (MEMS) with Fabry-Pérot tunable filters, are emerging as simpler and compact alternatives capable of being used to acquire spectral information in a wide wavelength band. In this work it is described the development of an interrogation system capable of infrared spectroscopy using a MEMS Fabry-Pérot Interferometer (MEMS-FPI) with a spectral response in the 1350nm to 1650nm range. Its performance is tested with the interrogation of long period fiber gratings both as a refractive index sensor and as a temperature sensor. Deconvolution techniques such as Wiener filtering are used to reduce the impact of the tunable filter's impulse response in the measured signal. Results are comparable to those obtained using a typical OSA which shows the system's potential as a cheaper and more transportable alternative. © Published under licence by IOP Publishing Ltd.

Abstract
The identification of olive-tree cultivars is a lengthy and expensive process, therefore, the proposed work presents a new strategy for identifying different cultivars of olive trees using their leaf and machine learning algorithms. In this initial case, four autochthonous cultivars of the Tras-os-Montes region in Portugal are identified (Cobrancosa, Madural, Negrinha e Verdeal). With the use of this type of algorithm, it is expected to replace the previous techniques, saving time and resources for farmers. Three different machine learning algorithms (Decision Tree, SVM, Random Forest) were also compared and the results show an overall accuracy rate of the best algorithm (Random Forest) of approximately 93%.

Abstract

Abstract

Abstract
Thermoelectric Generators (TEGs) are devices that have the ability to directly convert heat into electrical power, or vice-versa, and are being envisaged as one off-the-grid power source. Furthermore, carbon-based materials have been used as a conducting filler to improve several properties in thermoelectric materials. The present work studied the influence on the thermoelectric performance of Bi2Te3 bulk materials by incorporating different concentrations of Multi-Walled Carbon Nanotubes (MWCNT). In order to control and understand the influence of MWCNT dispersion in the nanocomposite, two different production methods (manual grinding and ultrasonication) were carried out and compared. It was verified that a larger dispersion leads to a better outcome for thermoelectric performance. The achieved Seebeck coefficient was up to-162 µV K-1 with a Power Factor of 0.50 µW K-2 m-1, for the nanocomposite produced with 11.8 %V of MWCNT. This result demonstrates the ability to increase the thermoelectric performance of Bi2Te3 throughout the addition of MWCNT. © 2022, Universidade do Porto - Faculdade de Engenharia. All rights reserved.