Numerical simulation of plasmonic effects in amorphous silicon induced by embedded aluminium nanoparticles

This work reports a theoretical study aimed to identify the plasmonic resonance condition for a system formed by metallic nanoparticles embedded in an a-Si: H matrix. The study is based on a Tauc-Lorentz model for the electrical permittivity of a-Si: H and a Drude model for the metallic nanoparticles. It is calculated the The polarizability of an sphere and ellipsoidal shaped metal nanoparticles with radius of 20 nm. We also performed FDTD simulations of light propagation inside this structure reporting a comparison among the effects caused by a single nanoparticles of Aluminium, Silver and, as a comparison, an ideally perfectly conductor. The simulation results shows that is possible to obtain a plasmonic resonance in the red part of the spectrum (600-700 nm) when 20-30 nm radius Aluminium ellipsoids are embedded into a-Si: H.

Visible range plasmonic effect produced by aluminium nanoparticles embedded in amorphous silicon

We present results, obtained by means of an analytic study and a numerical simulation, about the resonant condition necessary to produce a Localized Surface Plasmonic Resonance (LSPR) effect at the surface of metal nanospheres embedded in an amorphous silicon matrix. The study is based on a Lorentz dispersive model for a-Si:H permittivity and a Drude model for the metals. Considering the absorption spectra of a-Si:H, the best choice for the metal nanoparticles appears to be aluminium, indium or magnesium. No difference has been observed when considering a-SiC:H. Finite-difference time-domain (FDTD) simulation of an Al nanosphere embedded into an amorphous silicon matrix shows an increased scattering radius and the presence of LSPR induced by the metal/semiconductor interaction under green light (560 nm) illumination. Further results include the effect of the nanoparticles shape (nano-ellipsoids) in controlling the wavelength suitable to produce LSPR. It has been shown that is possible to produce LSPR in the red part of the visible spectrum (the most critical for a-Si:H solar cells applications in terms of light absorption enhancement) with aluminium nano-ellipsoids. As an additional results we may conclude that the double Lorentz-Lorenz model for the optical functions of a-Si:H is numerically stable in 3D simulations and can be used safely in the FDTD algorithm. A further simulation study is directed to determine an optimal spatial distribution of Al nanoparticles, with variable shapes, capable to enhance light absorption in the red part of the visible spectrum, exploiting light trapping and plasmonic effects. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Virtual Communities of Practice: Design and Development of Support Platforms and Participation Fostering

Mestrado em Engenharia Informática - Área de Especialização em Sistemas Gráficos e Multimédia

Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states

Heterogeneous multicore platforms are becoming an interesting alternative for embedded computing systems with limited power supply as they can execute specific tasks in an efficient manner. Nonetheless, one of the main challenges of such platforms consists of optimising the energy consumption in the presence of temporal constraints. This paper addresses the problem of task-to-core allocation onto heterogeneous multicore platforms such that the overall energy consumption of the system is minimised. To this end, we propose a two-phase approach that considers both dynamic and leakage energy consumption: (i) the first phase allocates tasks to the cores such that the dynamic energy consumption is reduced; (ii) the second phase refines the allocation performed in the first phase in order to achieve better sleep states by trading off the dynamic energy consumption with the reduction in leakage energy consumption. This hybrid approach considers core frequency set-points, tasks energy consumption and sleep states of the cores to reduce the energy consumption of the system. Major value has been placed on a realistic power model which increases the practical relevance of the proposed approach. Finally, extensive simulations have been carried out to demonstrate the effectiveness of the proposed algorithm. In the best-case, savings up to 18% of energy are reached over the first fit algorithm, which has shown, in previous works, to perform better than other bin-packing heuristics for the target heterogeneous multicore platform.

A framework for memory contention analysis in multi-core platforms

The last decade has witnessed a major shift towards the deployment of embedded applications on multi-core platforms. However, real-time applications have not been able to fully benefit from this transition, as the computational gains offered by multi-cores are often offset by performance degradation due to shared resources, such as main memory. To efficiently use multi-core platforms for real-time systems, it is hence essential to tightly bound the interference when accessing shared resources. Although there has been much recent work in this area, a remaining key problem is to address the diversity of memory arbiters in the analysis to make it applicable to a wide range of systems. This work handles diverse arbiters by proposing a general framework to compute the maximum interference caused by the shared memory bus and its impact on the execution time of the tasks running on the cores, considering different bus arbiters. Our novel approach clearly demarcates the arbiter-dependent and independent stages in the analysis of these upper bounds. The arbiter-dependent phase takes the arbiter and the task memory-traffic pattern as inputs and produces a model of the availability of the bus to a given task. Then, based on the availability of the bus, the arbiter-independent phase determines the worst-case request-release scenario that maximizes the interference experienced by the tasks due to the contention for the bus. We show that the framework addresses the diversity problem by applying it to a memory bus shared by a fixed-priority arbiter, a time-division multiplexing (TDM) arbiter, and an unspecified work-conserving arbiter using applications from the MediaBench test suite. We also experimentally evaluate the quality of the analysis by comparison with a state-of-the-art TDM analysis approach and consistently showing a considerable reduction in maximum interference.

Towards Certifiable Multicore-based Platforms for Avionics

Work in Progress Session, 21st IEEE Real-Time and Embedded Techonology and Applications Symposium (RTAS 2015). 13 to 16, Apr, 2015, pp 27-28. Seattle, U.S.A..

Towards the Certification of Multicore Platforms in the Avionics Domain

Poster presented in Work in Progress Session, The 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 27, Mar, 2015. Porto, Portugal.

Towards the Certification of Multicore Platforms in the Avionics Domain

Presented at Work in Progress Session, The 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 27, Mar, 2015. Porto, Portugal.

Ontologies for the interoperability of multiagent electricity markets simulation platforms

Electricity markets worldwide are complex and dynamic environments with very particular characteristics. These are the result of electricity markets’ restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. The rising complexity and unpredictability in electricity markets has increased the need for the intervenient entities in foreseeing market behaviour. Market players and regulators are very interested in predicting the market’s behaviour. Market players need to understand the market behaviour and operation in order to maximize their profits, while market regulators need to test new rules and detect market inefficiencies before they are implemented. The growth of usage of simulation tools was driven by the need for understanding those mechanisms and how the involved players' interactions affect the markets' outcomes. Multi-agent based software is particularly well fitted to analyse dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. Several modelling tools directed to the study of restructured wholesale electricity markets have emerged. Still, they have a common limitation: the lack of interoperability between the various systems to allow the exchange of information and knowledge, to test different market models and to allow market players from different systems to interact in common market environments. This dissertation proposes the development and implementation of ontologies for semantic interoperability between multi-agent simulation platforms in the scope of electricity markets. The added value provided to these platforms is given by enabling them sharing their knowledge and market models with other agent societies, which provides the means for an actual improvement in current electricity markets studies and development. The proposed ontologies are implemented in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) and tested through the interaction between MASCEM agents and agents from other multi-agent based simulators. The implementation of the proposed ontologies has also required a complete restructuring of MASCEM’s architecture and multi-agent model, which is also presented in this dissertation. The results achieved in the case studies allow identifying the advantages of the novel architecture of MASCEM, and most importantly, the added value of using the proposed ontologies. They facilitate the integration of independent multi-agent simulators, by providing a way for communications to be understood by heterogeneous agents from the various systems.

Evolution in public procurement and the impact of e-procurement platforms: A case study

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

Functional monolithic platforms for antibody purification

Dissertação para obtenção do Grau de Doutor em Química Sustentável

How enterprise 2,0 firms take advantage of emergent social software platforms to manage knowledge

This research aims to provide a better understanding on how firms stimulate knowledge sharing through the utilization of collaboration tools, in particular Emergent Social Software Platforms (ESSPs). It focuses on the distinctive applications of ESSPs and on the initiatives contributing to maximize its advantages. In the first part of the research, I have itemized all types of existing collaboration tools and classify them in different categories according to their capabilities, objectives and according to their faculty for promoting knowledge sharing. In the second part, and based on an exploratory case study at Cisco Systems, I have identified the main applications of an existing enterprise social software platform named Webex Social. By combining a qualitative and quantitative approach, as well as combining data collected from survey’s results and from the analysis of the company’s documents, I am expecting to maximize the outcome of this investigation and reduce the risk of bias. Although effects cannot be universalized based on one single case study, some utilization patterns have been underlined from the data collected and potential trends in managing knowledge have been observed. The results of the research have also enabled identifying most of the constraints experienced by the users of the firm’s social software platform. Utterly, this research should provide a primary framework for firms planning to create or implement a social software platform and for firms willing to increase adoption levels and to promote the overall participation of users. It highlights the common traps that should be avoided by developers when designing a social software platform and the capabilities that it should inherently carry to support an effective knowledge management strategy.

Launching platforms for user-generated content

Field lab: Entrepreneurial and innovative ventures

Integrating data from heterogeneous DNA microarray platforms

DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.

Diseño y caracterización de plataformas de biorreconocimiento basadas en el uso de nanoestructuras, biomoléculas y polímeros. Aplicaciones para el desarrollo de (bio)sensores electroquímicos. Design and characterization of biorecognition platforms based on the use of nanostructures, biomolecules and polymers. Applications for the development of electrochemical (bio)sensors.

Los requerimientos de métodos analíticos que permitan realizar determinaciones más eficientes en diversas ramas de la Química, así como el gran desarrollo logrado por la Nanobiotecnología, impulsaron la investigación de nuevas alternativas de análisis. Hoy, el campo de los Biosensores concita gran atención en el primer mundo, sin embargo, en nuestro país es todavía un área de vacancia, como lo es también la de la Nanotecnología. El objetivo de este proyecto es diseñar y caracterizar nuevos electrodos especialmente basados en el uso de nanoestructuras y estudiar aspectos básicos de la inmovilización de enzimas, ADN, aptámeros, polisacáridos y otros polímeros sobre dichos electrodos a fin de crear nuevas plataformas de biorreconocimiento para la construcción de (bio)sensores electroquímicos dirigidos a la cuantificación de analitos de interés clínico, farmaco-toxicológico y ambiental.Se estudiarán las propiedades de electrodos de C vítreo, Au, "screen printed" y compósitos de C modificados con nanotubos de C (CNT) y/o nanopartículas (NP) de oro y/o nanoalambres empleando diversas estrategias. Se investigarán nuevas alternativas de inmovilización de las biomoléculas antes mencionadas sobre dichos electrodos, se caracterizarán las plataformas resultantes y se evaluarán sus posibles aplicaciones analíticas al desarrollo de biosensores con enzimas y ADNs como elementos de biorreconocimiento. Se funcionalizarán CNT con polímeros comerciales y sintetizados en nuestro laboratorio modificados con moléculas bioactivas. Se diseñarán y caracterizarán nuevas arquitecturas supramoleculares basadas en el autoensamblado de policationes, enzimas y ADNs sobre Au. Se evaluarán las propiedades catalíticas de NP de magnetita y de perovskitas de Mn y su aplicación al desarrollo de biosensores enzimáticos. Se diseñarán biosensores que permitan la detección altamente sensible y selectiva de secuencias específicas de ADNs de interés clínico. Se estudiará la interacción de genotóxicos con ADN (en solución e inmovilizado) y se desarrollarán biosensores que permitan su cuantificación. Se construirán biosensores enzimáticos para la cuantificación de bioanalitos, especialmente glucosa, fenoles y catecoles, y sensores electroquímicos para la determinación de neurotransmisores, ácido úrico y ácido ascórbico. Se diseñarán nuevos aptasensores electroquímicos para la cuantificación de biomarcadores, comenzando por lisozima y trombina y continuando con otros de interés regional/nacional.Se emplearán las siguientes técnicas: voltamperometrías cíclica (CV), de pulso diferencial (DPV) y de onda cuadrada (SWV); "stripping" potenciométrico a corriente constante (PSA); elipsometría; microbalanza de cristal de cuarzo con cálculo de pérdida de energía por disipación (QCM-D); resonancia de plasmón superficial con detección dual (E-SPR); espectroscopía de impedancia electroquímica (EIE); microscopías de barrido electroquímico (SECM), de barrido electrónico (SEM), de transmisión (TEM) y de fuerzas atómicas (AFM); espectrofotometría UV-visible; espectroscopías IR, Raman, de masas, RMN.Se espera que la inclusión de los CNT y/o de las NP metálicas y/o de los nanoalambres en los diferentes electrodos permita una mejor transferencia de carga de diversos analitos y por ende una detección más sensible y selectiva de bioanalitos empleando enzimas, ADN y aptámeros como elementos de biorreconocimiento. Se espera una mayor eficiencia en los aptasensores respecto de los inmunosensores, lo que permitirá la determinacion selectiva de diversos biomarcadores. La modificación de electrodos con nanoestructuras posibilitará la detección altamente sensible y selectiva del evento de hibridación. La respuesta obtenida luego de la interacción de genotóxicos con ADN permitirá un mejor conocimiento de la asociación establecida, de la cinética y de las constantes termodinámicas. Los neurotransmisores podrán ser determinados a niveles nanomolares aún en muestras complejas.