Optimization in Dye Sensitized Solar Cells: a multiparametric approach

In recent years the research on Dye-sensitized Solar Cells (DSCs) has focused on the understanding of the photovoltaic processes and the improvement of the photoconversion efficiencies, currently around 12-13%. Despite these continuous advances, DSCs are not yet commercialized on large-scale, because they suffer of troubles in long-term stability, especially with organic and NIR sensitizers. In fact, the cells are subjected to undesirable phenomena, i.e. photodegradation of the dye anchored on semiconductor, leakage of the electrolyte, diffusion of pollutants from the outside and corrosion of some components. Another problem is due to the difficulty in realizing devices able to guarantee high photovoltaic performances with reliable reproducibility. The reason is that the cells are assembled with different and heterogeneous layers, each one affected by intrinsic variability; moreover the layers influence each other and this increases exponentially in cells with large active area, like modules and panels for commercial applications. The idea of the present work started from the need to identify all the factors by which the photoconversion processes may be influenced. For this reason, the research has been conducted with a chemometric multivariate approach (Design of Experiment, DoE) that allows to simultaneously evaluate multiple variables by reducing the number of the needed experiments, in order to understand and possible predict the synergistic/antagonistic effects, due to the interactions between the variables themselves. Starting from some case studies recently published here we will describe in specific the development of a method that enabled us to study the photostability and the photovoltaic efficiency of a series of NIR sensitizers, by correlating the results both to the structural characteristic of the molecule and to the dipping condition. We are firmly convinced that this approach will make possible to find the optimal experimental conditions to achieve, within a good reproducibility, optimized performances, both in term of efficiency and long term stability.