An electronic nose is an intelligent system consists of a sensor network and a pattern recognition system able to know simple and complex odors. As the human nose, the artificial nose must learn to recognize different odors: the learning phase. There are several types of sensors such as fiber optic sensors, piezoelectric sensors, sensor type MOSFET. The performance of the sensor network is discussed by using pattern recognition methods. In this article, we tested Principal Component Analysis (PCA) to evaluate the ability of our sensor array to distinguish between different groups of target gases according to their nature: only in binary mixture and ternary mixture.
A two-dimensional (2D) analytical model based on the Green’s function method is applied to an n+-p thin film polycrystalline solar cell that allows us to calculate the conversion efficiency. This model considers the effective Gaussian doping profile in the p region in order to improve cell efficiency. The dependence of mobility and lifetime on grain doping is also investigated. This model is implemented through a simulation program in order to optimize conversion efficiency while varying thickness and doping profile in the base region of the cell. Compared with back surface field (BSF) technology, our proposed structure shows a 31% improvement in conversion efficiency for a polycrystalline solar cell. For a monocrystalline solar cell, the BSF technology becomes more efficient.
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