The influence of substrate temperature on growth of para-sexiphenyl thin films on Ir{111} supported graphene studied by LEEM

The growth of para-sexiphenyl (6P) thin films as a function of substrate temperature on Ir{111} supported graphene flakes has been studied in real-time with Low Energy Electron Microscopy (LEEM). Micro Low Energy Electron Diffraction (\mu LEED) has been used to determine the structure of the different 6P features formed on the surface. We observe the nucleation and growth of a wetting layer consisting of lying molecules in the initial stages of growth. Graphene defects -- wrinkles -- are found to be preferential sites for the nucleation of the wetting layer and of the 6P needles that grow on top of the wetting layer in the later stages of deposition. The molecular structure of the wetting layer and needles is found to be similar. As a result, only a limited number of growth directions are observed for the needles. In contrast, on the bare Ir{111} surface 6P molecules assume an upright orientation. The formation of ramified islands is observed on the bare Ir{111} surface at 320 K and 352 K, whereas at 405 K the formation of a continuous layer of upright standing molecules growing in a step flow like manner is observed.Comment: 9 pages, 7 figures, Revised Version as accepted for publication in Surface Scienc

LIFEDATA - a framework for traceable active learning projects

Active Learning has become a popular method for iteratively improving data-intensive Artificial Intelligence models. However, it often presents a significant challenge when dealing with large volumes of volatile data in projects, as with an Active Learning loop. This paper introduces LIFEDATA, a Python- based framework designed to assist developers in implementing Active Learning projects focusing on traceability. It supports seamless tracking of all artifacts, from data selection and labeling to model interpretation, thus promoting transparency throughout the entire model learning process and enhancing error debugging efficiency while ensuring experiment reproducibility. To showcase its applicability, we present two life science use cases. Moreover, the paper proposes an algorithm that combines query strategies to demonstrate LIFEDATA’s ability to reduce data labeling effort