High-throughput characterization of multi-component thin films

We propose a novel approach to the compositional optimization of complex two- and multi-component materials. The sample preparation chamber developed in our group allows deposition of multi-component organic thin films with lateral gradient distribution of the components. Spatially-resolved X-ray scattering measurements along the gradient axis of such samples will provide compositionally-resolved information about the crystalline structure, degree of intermixing and morphology of the blends. Exploiting machine learning (ML) tools for X-ray scattering data analysis, we will extract the relevant structural information from the measured data in close to real time. These results will be correlated with the optical properties of the same samples measured prior to the X-ray experiment. This study will not only contribute to a better understanding of structure-property correlations in organic blends, but also demonstrate a novel approach to high-throughput studies of multi-component systems.