Resumen de Desenvolupament de cel·les solars orgàniques per mètodes combinatorials
Organic solar cells are complex stratified devices that convert light into electricity. Therein, the photovoltaic effect takes place in the active layer, which is formed by at least two organic semiconducting materials with dissimilar electronic character: the donor or hole transporting material, and the acceptor or electron transporting material, which appear mixed forming a bulk heterojunction. Device parameters such as the donor:acceptor mixing ratio and the active layer thickness must be tailored to maximize the photovoltaic efficiency, a time- and resources-consuming process usually performed through the systematic fabrication of tens of discrete devices.
In this thesis we develop high-throughput experimental approaches to accelerate the screening of materials and the optimization of organic solar cells. The methodology is based on the realization of lateral solid-state gradients on the parameters of interest, which are thought to circumvent the limitations of traditional sampling approaches by combining in a single sample the parametric variations explored by hundreds of them in discrete sampling experimentation. The photovoltaic performance is imaged throughout the gradients and spatially correlated with their parametric variations, thus moving from fabrication- to measuring-intensive screening scenarios that offer significantly higher materials screening and device optimization rates.
First, we demonstrate the use of Raman spectroscopy imaging to map quantitatively the thickness and mixing ratio variations of the gradients, while light-beam induced current (LBIC) mapping is exploited to measure the photocurrent in functional devices containing the aforementioned gradients. Their joint use is demonstrated in this work to be a truly efficient screening approach to optimize organic solar cells of distinct types.
On the one hand, we study bilayer heterostructures formed by orthogonal thickness gradients and use Raman spectroscopy and LBIC to identify the thickness of each layer that results in the highest photovoltaic performance. Then, we investigate bulk heterojunction devices made of low band gap donor polymers and fullerene acceptors and identify the optimal active layer thickness and mixing ratio of several state-of-the-art donor:acceptor binaries. Afterwards, the work is extended to novel non-fullerene acceptors including small molecules and n-type polymers. For this latter case and in order to accommodate the particular rheology of polymeric inks, a novel deposition technique based on microfluidic dispensers and blade coating is introduced. Next, bulk heterojunctions of ternary organic photovoltaic blends are effectively screened following high-throughput combinatorial methods. Finally, we perform systematic combinatorial material screening studies on polymer:small molecule binary blends to feed artificial intelligence algorithms. As a result, these are able to retrieve predictive models for the photocurrent space that might accelerate even further the device optimization and materials screening rate already enabled by the development of combinatorial methods in organic photovoltaics.
