Modeling for the world crude oil and natural gas markets

Crude oil and natural gas have been essential energy sources and play a crucial role in the world economy. Changes in energy prices significantly impact economic growth. This study builds an econometric model to illustrate the substitute relation between crude oil and natural gas markets. Additionally, the determination of the oil and natural gas prices are endogenized, assuming imperfect competition to reflect a real market strategy. Our empirical results show that the overall performance of this system is acceptable, and the model can be applied to policy analysis for determining monetary or energy policy by introducing this model to the more comprehensive system.

Growth of AlN oriented films on insulating substrates.

This work describes the structural and piezoelectric assessment of aluminum nitride (AlN) thin films deposited by pulsed-DC reactive sputtering on insulating substrates. We investigate the effect of different insulating seed layers on AlN properties (crystallinity, residual stress and piezoelectric activity). The seed layers investigated, silicon nitride (Si3N4), silicon dioxide (SiO2), amorphous tantalum oxide (Ta2O5), and amorphous or nano-crystalline titanium oxide (TiO2) are deposited on glass plates to a thickness lower than 100 nm. Before AlN films deposition, their surface is pre-treated with a soft ionic cleaning, either with argon or nitrogen ions. Only AlN films grown of TiO2 seed layers exhibit a significant piezoelectric activity to be used in acoustic device applications. Pure c-axis oriented films, with FWHM of rocking curve of 6º, stress below 500 MPa, and electromechanical coupling factors measured in SAW devices of 1.25% are obtained. The best AlN films are achieved on amorphous TiO2 seed layers deposited at high target power and low sputtering pressure. On the other hand, AlN films deposited on Si3N4, SiO2 and TaOx exhibit a mixed orientation, high stress and very low piezoelectric activity, which invalidate their use in acoustic devices.

An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.

Intraply fracture of fiber-reinforced composites: microscopic mechanisms and modeling

The fracture behavior parallel to the fibers of an E-glass/epoxy unidirectional laminate was studied by means of three-point tests on notched beams. Selected tests were carried out within a scanning electron microscope to ascertain the damage and fracture micromechanisms upon loading. The mechanical behavior of the notched beam was simulated within the framework of the embedded cell model, in which the actual composite microstructure was resolved in front of the notch tip. In addition, matrix and interface properties were independently measured in situ using a nanoindentor. The numerical simulations very accurately predicted the macroscopic response of the composite as well as the damage development and crack growth in front of the notch tip, demonstrating the ability of the embedded cell approach to simulate the fracture behavior of heterogeneous materials. Finally, this methodology was exploited to ascertain the influence of matrix and interface properties on the intraply toughness.

Extended description of tunnel junctions for distributed modeling of concentrator multi-junction solar cells

One of the key components of highly efficient multi-junction concentrator solar cells is the tunnel junction interconnection. In this paper, an improved 3D distributed model is presented that considers real operation regimes in a tunnel junction. This advanced model is able to accurately simulate the operation of the solar cell at high concentraions at which the photogenerated current surpasses the peak current of the tunnel junctionl Simulations of dual-junction solar cells were carried out with the improved model to illustrate its capabilities and the results have been correlated with experimental data reported in the literature. These simulations show that under certain circumstances, the solar cells short circuit current may be slightly higher than the tunnel junction peak current without showing the characteristic dip in the J-V curve. This behavior is caused by the lateral current spreading toward dark regions, which occurs through the anode/p-barrier of the tunnel junction.

Modeling the evolution of item rating networks using time-domain preferential attachment

The understanding of the structure and dynamics of the intricate network of connections among people that consumes products through Internet appears as an extremely useful asset in order to study emergent properties related to social behavior. This knowledge could be useful, for example, to improve the performance of personal recommendation algorithms. In this contribution, we analyzed five-year records of movie-rating transactions provided by Netflix, a movie rental platform where users rate movies from an online catalog. This dataset can be studied as a bipartite user-item network whose structure evolves in time. Even though several topological properties from subsets of this bipartite network have been reported with a model that combines random and preferential attachment mechanisms [Beguerisse Díaz et al., 2010], there are still many aspects worth to be explored, as they are connected to relevant phenomena underlying the evolution of the network. In this work, we test the hypothesis that bursty human behavior is essential in order to describe how a bipartite user-item network evolves in time. To that end, we propose a novel model that combines, for user nodes, a network growth prescription based on a preferential attachment mechanism acting not only in the topological domain (i.e. based on node degrees) but also in time domain. In the case of items, the model mixes degree preferential attachment and random selection. With these ingredients, the model is not only able to reproduce the asymptotic degree distribution, but also shows an excellent agreement with the Netflix data in several time-dependent topological properties.

A model of the spatially dependent mechanical properties of the axon during its growth

Neuronal growth is a complex process involving many intra- and extracellular mechanisms which are collaborating conjointly to participate to the development of the nervous system. More particularly, the early neocortical development involves the creation of a multilayered structure constituted by neuronal growth (driven by axonal or dendritic guidance cues) as well as cell migration. The underlying mechanisms of such structural lamination not only implies important biochemical changes at the intracellular level through axonal microtubule (de)polymerization and growth cone advance, but also through the directly dependent stress/stretch coupling mechanisms driving them. Efforts have recently focused on modeling approaches aimed at accounting for the effect of mechanical tension or compression on the axonal growth and subsequent soma migration. However, the reciprocal influence of the biochemical structural evolution on the mechanical properties has been mostly disregarded. We thus propose a new model aimed at providing the spatially dependent mechanical properties of the axon during its growth. Our in-house finite difference solver Neurite is used to describe the guanosine triphosphate (GTP) transport through the axon, its dephosphorylation in guanosine diphosphate (GDP), and thus the microtubules polymerization. The model is calibrated against experimental results and the tensile and bending mechanical stiffnesses are ultimately inferred from the spatially dependent microtubule occupancy. Such additional information is believed to be of drastic relevance in the growth cone vicinity, where biomechanical mechanisms are driving axonal growth and pathfinding. More specifically, the confirmation of a lower stiffness in the distal axon ultimately participates in explaining the controversy associated to the tensile role of the growth cone.

Knowledge representation tool for cognitiveprocesses modeling

In the last decades, neuropsychological theories tend to consider cognitive functions as a result of the whole brainwork and not as individual local areas of its cortex. Studies based on neuroimaging techniques have increased in the last years, promoting an exponential growth of the body of knowledge about relations between cognitive functions and brain structures [1]. However, so fast evolution make complicated to integrate them in verifiable theories and, even more, translated in to cognitive rehabilitation. The aim of this research work is to develop a cognitive process-modeling tool. The purpose of this system is, in the first term, to represent multidimensional data, from structural and functional connectivity, neuroimaging, data from lesion studies and derived data from clinical intervention [2][3]. This will allow to identify consolidated knowledge, hypothesis, experimental designs, new data from ongoing studies and emerging results from clinical interventions. In the second term, we pursuit to use Artificial Intelligence to assist in decision making allowing to advance towards evidence based and personalized treatments in cognitive rehabilitation. This work presents the knowledge base design of the knowledge representation tool. It is compound of two different taxonomies (structure and function) and a set of tags linking both taxonomies at different levels of structural and functional organization. The remainder of the abstract is organized as follows: Section 2 presents the web application used for gathering necessary information for generating the knowledge base, Section 3 describes knowledge base structure and finally Section 4 expounds reached conclusions.

On the analytical approach for modeling photovoltaic systems behavior

The 1-diode/2-resistors electric circuit equivalent to a photovoltaic system is analyzed. The equations at particular points of the I–V curve are studied considering the maximum number of terms. The maximum power point as a boundary condition is given special attention. A new analytical method is developed based on a reduced amount of information, consisting in the normal manufacturer data. Results indicate that this new method is faster than numerical methods and has similar (or better) accuracy than other existing methods, numerical or analytical.

Large scale multiphysics modeling of neurites

Situado en el límite entre Ingeniería, Informática y Biología, la mecánica computacional de las neuronas aparece como un nuevo campo interdisciplinar que potencialmente puede ser capaz de abordar problemas clínicos desde una perspectiva diferente. Este campo es multiescala por naturaleza, yendo desde la nanoescala (como, por ejemplo, los dímeros de tubulina) a la macroescala (como, por ejemplo, el tejido cerebral), y tiene como objetivo abordar problemas que son complejos, y algunas veces imposibles, de estudiar con medios experimentales. La modelización computacional ha sido ampliamente empleada en aplicaciones Neurocientíficas tan diversas como el crecimiento neuronal o la propagación de los potenciales de acción compuestos. Sin embargo, en la mayoría de los enfoques de modelización hechos hasta ahora, la interacción entre la célula y el medio/estímulo que la rodea ha sido muy poco explorada. A pesar de la tremenda importancia de esa relación en algunos desafíos médicos—como, por ejemplo, lesiones traumáticas en el cerebro, cáncer, la enfermedad del Alzheimer—un puente que relacione las propiedades electrofisiológicas-químicas y mecánicas desde la escala molecular al nivel celular todavía no existe. Con ese objetivo, esta investigación propone un marco computacional multiescala particularizado para dos escenarios respresentativos: el crecimiento del axón y el acomplamiento electrofisiológicomecánico de las neuritas. En el primer caso, se explora la relación entre los constituyentes moleculares del axón durante su crecimiento y sus propiedades mecánicas resultantes, mientras que en el último, un estímulo mecánico provoca deficiencias funcionales a nivel celular como consecuencia de sus alteraciones electrofisiológicas-químicas. La modelización computacional empleada en este trabajo es el método de las diferencias finitas, y es implementada en un nuevo programa llamado Neurite. Aunque el método de los elementos finitos es también explorado en parte de esta investigación, el método de las diferencias finitas tiene la flexibilidad y versatilidad necesaria para implementar mode los biológicos, así como la simplicidad matemática para extenderlos a simulaciones a gran escala con un coste computacional bajo. Centrándose primero en el efecto de las propiedades electrofisiológicas-químicas sobre las propiedades mecánicas, una versión adaptada de Neurite es desarrollada para simular la polimerización de los microtúbulos en el crecimiento del axón y proporcionar las propiedades mecánicas como función de la ocupación de los microtúbulos. Después de calibrar el modelo de crecimiento del axón frente a resultados experimentales disponibles en la literatura, las características mecánicas pueden ser evaluadas durante la simulación. Las propiedades mecánicas del axón muestran variaciones dramáticas en la punta de éste, donde el cono de crecimiento soporta las señales químicas y mecánicas. Bansándose en el conocimiento ganado con el modelo de diferencias finitas, y con el objetivo de ir de 1D a 3D, este esquema preliminar pero de una naturaleza innovadora allana el camino a futuros estudios con el método de los elementos finitos. Centrándose finalmente en el efecto de las propiedades mecánicas sobre las propiedades electrofisiológicas- químicas, Neurite es empleado para relacionar las cargas mecánicas macroscópicas con las deformaciones y velocidades de deformación a escala microscópica, y simular la propagación de la señal eléctrica en las neuritas bajo carga mecánica. Las simulaciones fueron calibradas con resultados experimentales publicados en la literatura, proporcionando, por tanto, un modelo capaz de predecir las alteraciones de las funciones electrofisiológicas neuronales bajo cargas externas dañinas, y uniendo lesiones mecánicas con las correspondientes deficiencias funcionales. Para abordar simulaciones a gran escala, aunque otras arquitecturas avanzadas basadas en muchos núcleos integrados (MICs) fueron consideradas, los solvers explícito e implícito se implementaron en unidades de procesamiento central (CPU) y unidades de procesamiento gráfico (GPUs). Estudios de escalabilidad fueron llevados acabo para ambas implementaciones mostrando resultados prometedores para casos de simulaciones extremadamente grandes con GPUs. Esta tesis abre la vía para futuros modelos mecánicos con el objetivo de unir las propiedades electrofisiológicas-químicas con las propiedades mecánicas. El objetivo general es mejorar el conocimiento de las comunidades médicas y de bioingeniería sobre la mecánica de las neuronas y las deficiencias funcionales que aparecen de los daños producidos por traumatismos mecánicos, como lesiones traumáticas en el cerebro, o enfermedades neurodegenerativas como la enfermedad del Alzheimer. ABSTRACT Sitting at the interface between Engineering, Computer Science and Biology, Computational Neuron Mechanics appears as a new interdisciplinary field potentially able to tackle clinical problems from a new perspective. This field is multiscale by nature, ranging from the nanoscale (e.g., tubulin dimers) to the macroscale (e.g., brain tissue), and aims at tackling problems that are complex, and sometime impossible, to study through experimental means. Computational modeling has been widely used in different Neuroscience applications as diverse as neuronal growth or compound action potential propagation. However, in the majority of the modeling approaches done in this field to date, the interactions between the cell and its surrounding media/stimulus have been rarely explored. Despite of the tremendous importance of such relationship in several medical challenges—e.g., traumatic brain injury (TBI), cancer, Alzheimer’s disease (AD)—a bridge between electrophysiological-chemical and mechanical properties of neurons from the molecular scale to the cell level is still lacking. To this end, this research proposes a multiscale computational framework particularized for two representative scenarios: axon growth and electrophysiological-mechanical coupling of neurites. In the former case, the relation between the molecular constituents of the axon during its growth and its resulting mechanical properties is explored, whereas in the latter, a mechanical stimulus provokes functional deficits at cell level as a consequence of its electrophysiological-chemical alterations. The computational modeling approach chosen in this work is the finite difference method (FDM), and was implemented in a new program called Neurite. Although the finite element method (FEM) is also explored as part of this research, the FDM provides the necessary flexibility and versatility to implement biological models, as well as the mathematical simplicity to extend them to large scale simulations with a low computational cost. Focusing first on the effect of electrophysiological-chemical properties on the mechanical proper ties, an adaptation of Neurite was developed to simulate microtubule polymerization in axonal growth and provide the axon mechanical properties as a function of microtubule occupancy. After calibrating the axon growth model against experimental results available in the literature, the mechanical characteristics can be tracked during the simulation. The axon mechanical properties show dramatic variations at the tip of the axon, where the growth cone supports the chemical and mechanical signaling. Based on the knowledge gained from the FDM scheme, and in order to go from 1D to 3D, this preliminary yet novel scheme paves the road for future studies with FEM. Focusing then on the effect of mechanical properties on the electrophysiological-chemical properties, Neurite was used to relate macroscopic mechanical loading to microscopic strains and strain rates, and simulate the electrical signal propagation along neurites under mechanical loading. The simulations were calibrated against experimental results published in the literature, thus providing a model able to predict the alteration of neuronal electrophysiological function under external damaging load, and linking mechanical injuries to subsequent acute functional deficits. To undertake large scale simulations, although other state-of-the-art architectures based on many integrated cores (MICs) were considered, the explicit and implicit solvers were implemented for central processing units (CPUs) and graphics processing units (GPUs). Scalability studies were done for both implementations showing promising results for extremely large scale simulations with GPUs. This thesis opens the avenue for future mechanical modeling approaches aimed at linking electrophysiological- chemical properties to mechanical properties. Its overarching goal is to enhance the bioengineering and medical communities knowledge on neuronal mechanics and functional deficits arising from damages produced by direct mechanical insults, such as TBI, or neurodegenerative evolving illness, such as AD.

Linear latent force models using Gaussian Processes

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Air quality modeling and mortality impact of fine particles reduction policies in Spain

Background: In recent years, Spain has implemented a number of air quality control measures that are expected to lead to a future reduction in fine particle concentrations and an ensuing positive impact on public health. Objectives: We aimed to assess the impact on mortality attributable to a reduction in fine particle levels in Spain in 2014 in relation to the estimated level for 2007. Methods: To estimate exposure, we constructed fine particle distribution models for Spain for 2007 (reference scenario) and 2014 (projected scenario) with a spatial resolution of 16x16 km2. In a second step, we used the concentration-response functions proposed by cohort studies carried out in Europe (European Study of Cohorts for Air Pollution Effects and Rome longitudinal cohort) and North America (American Cancer Society cohort, Harvard Six Cities study and Canadian national cohort) to calculate the number of attributable annual deaths corresponding to all causes, all non-accidental causes, ischemic heart disease and lung cancer among persons aged over 25 years (2005-2007 mortality rate data). We examined the effect of the Spanish demographic shift in our analysis using 2007 and 2012 population figures. Results: Our model suggested that there would be a mean overall reduction in fine particle levels of 1mg/m3 by 2014. Taking into account 2007 population data, between 8 and 15 all-cause deaths per 100,000 population could be postponed annually by the expected reduction in fine particle levels. For specific subgroups, estimates varied from 10 to 30 deaths for all non-accidental causes, from 1 to 5 for lung cancer, and from 2 to 6 for ischemic heart disease. The expected burden of preventable mortality would be even higher in the future due to the Spanish population growth. Taking into account the population older than 30 years in 2012, the absolute mortality impact estimate would increase approximately by 18%. Conclusions: Effective implementation of air quality measures in Spain, in a scenario with a short-term projection, would amount to an appreciable decline infine particle concentrations, and this, in turn, would lead to notable health-related benefits. Recent European cohort studies strengthen the evidence of an association between long-term exposure to fine particles and health effects, and could enhance the health impact quantification in Europe. Air quality models can contribute to improved assessment of air pollution health impact estimates, particularly in study areas without air pollution monitoring data.

MOVPE growth of III-V semiconductors on silicon for third generation solar cells

Esta Tesis trata sobre el desarrollo y crecimiento -mediante tecnología MOVPE (del inglés: MetalOrganic Vapor Phase Epitaxy)- de células solares híbridas de semiconductores III-V sobre substratos de silicio. Esta integración pretende ofrecer una alternativa a las células actuales de III-V, que, si bien ostentan el récord de eficiencia en dispositivos fotovoltaicos, su coste es, a día de hoy, demasiado elevado para ser económicamente competitivo frente a las células convencionales de silicio. De este modo, este proyecto trata de conjugar el potencial de alta eficiencia ya demostrado por los semiconductores III-V en arquitecturas de células fotovoltaicas multiunión con el bajo coste, la disponibilidad y la abundancia del silicio. La integración de semiconductores III-V sobre substratos de silicio puede afrontarse a través de diferentes aproximaciones. En esta Tesis se ha optado por el desarrollo de células solares metamórficas de doble unión de GaAsP/Si. Mediante esta técnica, la transición entre los parámetros de red de ambos materiales se consigue por medio de la formación de defectos cristalográficos (mayoritariamente dislocaciones). La idea es confinar estos defectos durante el crecimiento de sucesivas capas graduales en composición para que la superficie final tenga, por un lado, una buena calidad estructural, y por otro, un parámetro de red adecuado. Numerosos grupos de investigación han dirigido sus esfuerzos en los últimos años en desarrollar una estructura similar a la que aquí proponemos. La mayoría de éstos se han centrado en entender los retos asociados al crecimiento de materiales III-V, con el fin de conseguir un material de alta calidad cristalográfica. Sin embargo, prácticamente ninguno de estos grupos ha prestado especial atención al desarrollo y optimización de la célula inferior de silicio, cuyo papel va a ser de gran relevancia en el funcionamiento de la célula completa. De esta forma, y con el fin de completar el trabajo hecho hasta el momento en el desarrollo de células de III-V sobre silicio, la presente Tesis se centra, fundamentalmente, en el diseño y optimización de la célula inferior de silicio, para extraer su máximo potencial. Este trabajo se ha estructurado en seis capítulos, ordenados de acuerdo al desarrollo natural de la célula inferior. Tras un capítulo de introducción al crecimiento de semiconductores III-V sobre Si, en el que se describen las diferentes alternativas para su integración; nos ocupamos de la parte experimental, comenzando con una extensa descripción y caracterización de los substratos de silicio. De este modo, en el Capítulo 2 se analizan con exhaustividad los diferentes tratamientos (tanto químicos como térmicos) que deben seguir éstos para garantizar una superficie óptima sobre la que crecer epitaxialmente el resto de la estructura. Ya centrados en el diseño de la célula inferior, el Capítulo 3 aborda la formación de la unión p-n. En primer lugar se analiza qué configuración de emisor (en términos de dopaje y espesor) es la más adecuada para sacar el máximo rendimiento de la célula inferior. En este primer estudio se compara entre las diferentes alternativas existentes para la creación del emisor, evaluando las ventajas e inconvenientes que cada aproximación ofrece frente al resto. Tras ello, se presenta un modelo teórico capaz de simular el proceso de difusión de fosforo en silicio en un entorno MOVPE por medio del software Silvaco. Mediante este modelo teórico podemos determinar qué condiciones experimentales son necesarias para conseguir un emisor con el diseño seleccionado. Finalmente, estos modelos serán validados y constatados experimentalmente mediante la caracterización por técnicas analíticas (i.e. ECV o SIMS) de uniones p-n con emisores difundidos. Uno de los principales problemas asociados a la formación del emisor por difusión de fósforo, es la degradación superficial del substrato como consecuencia de su exposición a grandes concentraciones de fosfina (fuente de fósforo). En efecto, la rugosidad del silicio debe ser minuciosamente controlada, puesto que éste servirá de base para el posterior crecimiento epitaxial y por tanto debe presentar una superficie prístina para evitar una degradación morfológica y cristalográfica de las capas superiores. En este sentido, el Capítulo 4 incluye un análisis exhaustivo sobre la degradación morfológica de los substratos de silicio durante la formación del emisor. Además, se proponen diferentes alternativas para la recuperación de la superficie con el fin de conseguir rugosidades sub-nanométricas, que no comprometan la calidad del crecimiento epitaxial. Finalmente, a través de desarrollos teóricos, se establecerá una correlación entre la degradación morfológica (observada experimentalmente) con el perfil de difusión del fósforo en el silicio y por tanto, con las características del emisor. Una vez concluida la formación de la unión p-n propiamente dicha, se abordan los problemas relacionados con el crecimiento de la capa de nucleación de GaP. Por un lado, esta capa será la encargada de pasivar la subcélula de silicio, por lo que su crecimiento debe ser regular y homogéneo para que la superficie de silicio quede totalmente pasivada, de tal forma que la velocidad de recombinación superficial en la interfaz GaP/Si sea mínima. Por otro lado, su crecimiento debe ser tal que minimice la aparición de los defectos típicos de una heteroepitaxia de una capa polar sobre un substrato no polar -denominados dominios de antifase-. En el Capítulo 5 se exploran diferentes rutinas de nucleación, dentro del gran abanico de posibilidades existentes, para conseguir una capa de GaP con una buena calidad morfológica y estructural, que será analizada mediante diversas técnicas de caracterización microscópicas. La última parte de esta Tesis está dedicada al estudio de las propiedades fotovoltaicas de la célula inferior. En ella se analiza la evolución de los tiempos de vida de portadores minoritarios de la base durante dos etapas claves en el desarrollo de la estructura Ill-V/Si: la formación de la célula inferior y el crecimiento de las capas III-V. Este estudio se ha llevado a cabo en colaboración con la Universidad de Ohio, que cuentan con una gran experiencia en el crecimiento de materiales III-V sobre silicio. Esta tesis concluye destacando las conclusiones globales del trabajo realizado y proponiendo diversas líneas de trabajo a emprender en el futuro. ABSTRACT This thesis pursues the development and growth of hybrid solar cells -through Metal Organic Vapor Phase Epitaxy (MOVPE)- formed by III-V semiconductors on silicon substrates. This integration aims to provide an alternative to current III-V cells, which, despite hold the efficiency record for photovoltaic devices, their cost is, today, too high to be economically competitive to conventional silicon cells. Accordingly, the target of this project is to link the already demonstrated efficiency potential of III-V semiconductor multijunction solar cell architectures with the low cost and unconstrained availability of silicon substrates. Within the existing alternatives for the integration of III-V semiconductors on silicon substrates, this thesis is based on the metamorphic approach for the development of GaAsP/Si dual-junction solar cells. In this approach, the accommodation of the lattice mismatch is handle through the appearance of crystallographic defects (namely dislocations), which will be confined through the incorporation of a graded buffer layer. The resulting surface will have, on the one hand a good structural quality; and on the other hand the desired lattice parameter. Different research groups have been working in the last years in a structure similar to the one here described, being most of their efforts directed towards the optimization of the heteroepitaxial growth of III-V compounds on Si, with the primary goal of minimizing the appearance of crystal defects. However, none of these groups has paid much attention to the development and optimization of the bottom silicon cell, which, indeed, will play an important role on the overall solar cell performance. In this respect, the idea of this thesis is to complete the work done so far in this field by focusing on the design and optimization of the bottom silicon cell, to harness its efficiency. This work is divided into six chapters, organized according to the natural progress of the bottom cell development. After a brief introduction to the growth of III-V semiconductors on Si substrates, pointing out the different alternatives for their integration; we move to the experimental part, which is initiated by an extensive description and characterization of silicon substrates -the base of the III-V structure-. In this chapter, a comprehensive analysis of the different treatments (chemical and thermal) required for preparing silicon surfaces for subsequent epitaxial growth is presented. Next step on the development of the bottom cell is the formation of the p-n junction itself, which is faced in Chapter 3. Firstly, the optimization of the emitter configuration (in terms of doping and thickness) is handling by analytic models. This study includes a comparison between the different alternatives for the emitter formation, evaluating the advantages and disadvantages of each approach. After the theoretical design of the emitter, it is defined (through the modeling of the P-in-Si diffusion process) a practical parameter space for the experimental implementation of this emitter configuration. The characterization of these emitters through different analytical tools (i.e. ECV or SIMS) will validate and provide experimental support for the theoretical models. A side effect of the formation of the emitter by P diffusion is the roughening of the Si surface. Accordingly, once the p-n junction is formed, it is necessary to ensure that the Si surface is smooth enough and clean for subsequent phases. Indeed, the roughness of the Si must be carefully controlled since it will be the basis for the epitaxial growth. Accordingly, after quantifying (experimentally and by theoretical models) the impact of the phosphorus on the silicon surface morphology, different alternatives for the recovery of the surface are proposed in order to achieve a sub-nanometer roughness which does not endanger the quality of the incoming III-V layers. Moving a step further in the development of the Ill-V/Si structure implies to address the challenges associated to the GaP on Si nucleation. On the one hand, this layer will provide surface passivation to the emitter. In this sense, the growth of the III-V layer must be homogeneous and continuous so the Si emitter gets fully passivated, providing a minimal surface recombination velocity at the interface. On the other hand, the growth should be such that the appearance of typical defects related to the growth of a polar layer on a non-polar substrate is minimized. Chapter 5 includes an exhaustive study of the GaP on Si nucleation process, exploring different nucleation routines for achieving a high morphological and structural quality, which will be characterized by means of different microscopy techniques. Finally, an extensive study of the photovoltaic properties of the bottom cell and its evolution during key phases in the fabrication of a MOCVD-grown III-V-on-Si epitaxial structure (i.e. the formation of the bottom cell; and the growth of III-V layers) will be presented in the last part of this thesis. This study was conducted in collaboration with The Ohio State University, who has extensive experience in the growth of III-V materials on silicon. This thesis concludes by highlighting the overall conclusions of the presented work and proposing different lines of work to be undertaken in the future.