Thin films of Cu, In and Sb chalcogenides as photovoltaic absorbers

Many different photovoltaic technologies are being developed for large-scale solar energy conversion such as crystalline silicon solar cells, thin film solar cells based on a-Si:H, CIGS and CdTe. As the demand for photovoltaics rapidly increases, there is a pressing need for the identification of new visible light absorbing materials for thin-film solar cells. Nowadays there are a wide range of earth-abundant absorber materials that have been studied around the world by different research groups. The current thin film photovoltaic market is dominated by technologies based on the use of CdTe and CIGS, these solar cells have been made with laboratory efficiencies up to 19.6% and 20.8% respectively. However, the scarcity and high cost of In, Ga and Te can limit in the long-term the production in large scale of photovoltaic devices. On the other hand, quaternary CZTSSe which contain abundant and inexpensive elements like Cu, Zn, Sn, S and Se has been a potential candidate for PV technology having solar cell efficiency up to 12.6%, however, there are still some challenges that must be accomplished for this material. Therefore, it is evident the need to find the alternative inexpensive and earth abundant materials for thin film solar cells. One of these alternatives is copper antimony sulfide(CuSbS2) which contains abundant and non-toxic elements which has a direct optical band gap of 1.5 eV, the optimum value for an absorber material in solar cells, suggesting this material as one among the new photovoltaic materials. This thesis work focuses on the preparation and characterization of In6Se7, CuSbS2 and CuSb(S1-xSex)2 thin films for their application as absorber material in photovoltaic structures using two stage process by the combination of chemical bath deposition and thermal evaporation.

Exact solutions for the agricultural and the two-dimensional packing problems

Objectives and study method: The objective of this study is to develop exact algorithms that can be used as management tools for the agricultural production planning and to obtain exact solutions for two of the most well known twodimensional packing problems: the strip packing problem and the bin packing problem. For the agricultural production planning problem we propose a new hierarchical scheme of three stages to improve the current agricultural practices. The objective of the first stage is to delineate rectangular and homogeneous management zones into the farmer’s plots considering the physical and chemical soil properties. This is an important task because the soil properties directly affect the agricultural production planning. The methodology for this stage is based on a new method called “Positions and Covering” that first generates all the possible positions in which the plot can be delineated. Then, we use a mathematical model of linear programming to obtain the optimal physical and chemical management zone delineation of the plot. In the second stage the objective is to determine the optimal crop pattern that maximizes the farmer’s profit taken into account the previous management zones delineation. In this case, the crop pattern is affected by both management zones delineation, physical and chemical. A mixed integer linear programming is used to solve this stage. The objective of the last stage is to determine in real-time the amount of water to irrigate in each crop. This stage takes as input the solution of the crop planning stage, the atmospheric conditions (temperature, radiation, etc.), the humidity level in plots, and the physical management zones of plots, just to name a few. This procedure is made in real-time during each irrigation period. A linear programming is used to solve this problem. A breakthrough happen when we realize that we could propose some adaptations of the P&C methodology to obtain optimal solutions for the two-dimensional packing problem and the strip packing. We empirically show that our methodologies are efficient on instances based on real data for both problems: agricultural and two-dimensional packing problems. Contributions and conclusions: The exact algorithms showed in this study can be used in the making-decision support for agricultural planning and twodimensional packing problems. For the agricultural planning problem, we show that the implementation of the new hierarchical approach can improve the farmer profit between 5.27% until 8.21% through the optimization of the natural resources. An important characteristic of this problem is that the soil properties (physical and chemical) and the real-time factors (climate, humidity level, evapotranspiration, etc.) are incorporated. With respect to the two-dimensional packing problems, one of the main contributions of this study is the fact that we have demonstrate that many of the best solutions founded in literature by others approaches (heuristics approaches) are the optimal solutions. This is very important because some of these solutions were up to now not guarantee to be the optimal solutions.