Application of Nano-Plasmonics for SERS Bio-Detection and Photocatalysis in the Same Platform

Nano-biological systems interfacing nano-structured solid surfaces with biological compounds such as oligonucleotides or proteins are highly regarded as enabling materials for biosensing and biocatalysis applications. In particular, nanostructures of noble metals such as gold or silver, when exposed to light, exhibit a phenomenon known as surface plasmon resonance. When a proper metal nanostructure (plasmonic substrate) is exposed to light, very efficient absorption of incoming photons is possible, resulting in a buildup of localized high-energy regions, or “hot-spots”, where energetic carriers or “hot carriers” can be created. These hot-carriers can be used to catalyze desired chemical transformations in materials located nearby. Furthermore, plasmonic hot-spots are also known to enhance inelastic scattering of light by the same materials, promising multi-functional applications that combine photo-catalytic stimulation of materials with their ultrasensitive characterization in the same design. In this thesis work, we developed a conjugate nano-biological system interfacing plasmonic gold nanostructures with thiolated single-stranded DNA carrying an important reduction-oxidation indicator, methylthioninium chloride, also known as methylene blue. Using surface-enhanced Raman spectroscopy, we have detected characteristic bands of DNA-bound immobilized methylene blue in sub-monolayer quantities. We also have detected reversible reduction-oxidation of methylene blue during laser excitation of the samples at neutral pH, in the absence of electrodes or chemical agents

Correlating the switch-on time of two identical PV modules using a simplified measuring approach

Published Conference ProceedingThe installation of different renewable energy systems is gaining momentum globally, as governments and industry seek to reduce carbon emissions and fossil fuel usage. PV systems have become more common place, as evidenced by the number of PV array roof-installations visible in many communities today. However, it is assumed that the modules in these arrays are identical, as it is important to have similar currents flow through each branch of an array. Partial shading or unmatched PV modules may lead to power mismatches, hot spots and ultimately a lower overall output power than what is desired. The purpose of this paper is to present a simplified measuring approach to validate the operating performance of two identical PV modules by determining their switch-on time. Any significant differences in switch-on time between identical PV modules could lead to possible power mismatches in a PV array. Results confirm that both PV modules are very similar in switch-on time, with an average on-time difference of 2 minutes and 27 seconds.. This indicates that both PV modules start operating at roughly the same time of the day, thereby providing similar current flows which contribute to the optimum performance of a PV array

Some Reliability Aspects of Photovoltaic Modules

Solar cells and photovoltaic modules are energy conversion components that produce electricity when exposed to light. The originality of photovoltaic energy as we understand it here is to directly transform light into electricity. Thin-film silicon in particular is better at low and diffuse illuminations and decreases less than the crystalline when the temperature increases while reducing the amount of material and manufacturing costs. However, the quality of the material and the efficiency of the conversion limit their use on a large scale. If the light absorption of the ultra-thin layers of the active material could be improved, this would lead to low recombination currents, higher open-circuit voltages and higher conversion efficiency. PV systems often communicate with utilities, aggregators and other grid operators over the public Internet, so the power system attack surface has significantly expanded. Solar energy systems are equipped with a range of grid-support functions, which—if controlled or programmed improperly—present a risk of power system disturbances

Defining the best-fit machine learning classifier to early diagnose photovoltaic solar cells hot-spots

Photovoltaic (PV) hot-spots is a reliability problem in PV modules, where a cell or group of cells heats up significantly, dissipating rather than producing power, and resulting in a loss and further degradation for the PV modules’ performance. Therefore, in this article, we present the development of a novel machine learning-based (ML) tool to diagnose early-stage PV hot-spots. To achieve the best-fit ML structure, we compared four distinct machine learning classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbour (KNN), and the discriminant classifiers (DC). Results confirm that the DC classifiers attain the best detection accuracy of 98%, while the least detection accuracy of 84% was observed for the decision tree. Furthermore, the examined four classifiers were also compared in terms of their performance using the confusion matrix and the receiver operating characteristics (ROC)

Assessing Energy Performance and Management Processes in Solar Photovoltaic Farms

La finalidad de la tesis es analizar cualitativamente los procesos de mantenimiento y gestión de las diferentes plantas fotovoltaicas, así como cuantitativamente los resultados de producción de energía solar y determinar las principales causas del funcionamiento actual. Los objetivos son realizar un análisis detallado de todas las características de cada instalación e identificar sus procedimientos de gestión y mantenimiento, recoger los datos de generación de energía y compararlos con un software y comparar los diferentes sistemas fotovoltaicos y aportar recomendaciones de mejora. Se utiliza una metodología para todas las instalaciones fotovoltaicas que incluye un análisis DAFO y una evaluación del rendimiento energético. Los resultados muestran varios puntos en común respecto a los principales problemas de las plantas fotovoltaicas, siguen el mismo patrón respecto a los ratios de generación de energía. Por último, se mencionan las posibles medidas de mejora a tener en cuenta para cada instalación.The purpose of the dissertation is to analyse qualitatively the maintenance and management processes of the different photovoltaic plants, as well as quantitatively the results of solar energy production and determine the main causes for the current operation. The objectives are to perform a detailed analysis of all the characteristics of every installation and identify their management and maintenance procedures, to collect data from energy generation and compare it with a software and compare different PV systems and provide recommendations of improvement. There is a methodology used for all PV sites including a SWOT analysis and a energy performance evaluation. The results show various commonalities regarding the main problems of the photovoltaic plants, they follow the same pattern regarding energy generation ratios. Finally, the possible improvement measures to take into consideration for each installation are mentioned.Departamento de Ingeniería Energética y FluidomecánicaGrado en Ingeniería en Organización Industria

ALGORITHM TO EXTRACT MODEL PARAMETERS OF PARTIALLY SHADED PHOTOVOLTAIC MODULES

Uneven irradiation, due to partial shading, can produce hot spots in photovoltaic modules. A classical solution to avoid hot spot consists in using bypass diodes in antiparallel to series-connected cell groups. This solution brings a new problem: the presence of multiple local maximum power points. We present a simple algorithm for fast extraction of the model parameters of partially shaded photovoltaic panels with bypass diodes. An example of the application of the proposed algorithm is illustrated using the data from a real monocrystalline silicon technology photovoltaic module measured under uniform illumination and partial shading conditions. The possibility of using the algorithm as a practical approximate solution is also discussed. The simulations, using only four parameters, represent reasonably well the measured data