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A new method is proposed to analyze doubly-resonant infrared-visible sum-frequency (DR-SFG) spectra. Based on the transform technique, this approach is free from assumptions about vibronic modes, energies, or line widths, and accurately captures through the overlap spectral function all required aspects of the vibronic structure from simple experimental linear absorption spectra. Details and implementation of the method are provided, along with three examples treating rhodamine thin films about one monolayer thick. The technique leads to a perfect agreement between experiment and simulations of the visible DR-SFG lineshapes, even in the case of complex intermolecular interactions resulting from J-aggregated chromophores in heterogeneous films. For films with mixed H-and J-aggregates, separation of their responses shows that the J-aggregate DR-SFG response is dominant. Our analysis also accounts for the unexplained results published in the early times of DR-SFG experiments.

This project studies the conditions at which the maximum power point of a photovoltaic (PV) panel is obtained. It shows that the maximum power point is very sensitive to external disturbances such as temperature and irradiation. It introduces a novel method for maximizing the output power of a PV panel when connected to a DC/DC boost converter under variable load conditions. The main contribution of this work is to predict the optimum reference voltage of the PV panel at all-weather conditions using machine learning strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. Evaluations of the proposed system, which uses an experimental photovoltaic dataset gathered from Spain, prove that it is robust against internal and external disturbances. They also show that the system performs better when using support vector machines as the machine learning strategy compared to the case when using general regression neural networks.

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