C1: Physics-Informed Data Fusion of 4D-STEM and Spectroscopic Signals for Functional Thin Films
Physics-Informed Data Fusion of 4D-STEM and Spectroscopic Signals for Functional Thin Films
This doctoral project aims to develop advanced data fusion algorithms for combining multi-modal 4D-STEM and spectroscopic signals to achieve comprehensive, high-fidelity characterization of functional thin films and nanostructured materials. The candidate will design physics-informed models that correlate structural, crystallographic, and chemical information from elastic and inelastic electron scattering channels. These methods will significantly enhance signal-to-noise ratios, enabling dose-efficient imaging of beam-sensitive materials. The algorithms will be implemented and tested at state-of-the-art 4D-STEM and SEM-STEM instruments equipped with direct electron detectors at CENEM. Applications include inorganic and organic semiconductor films, as well as catalytic layers in electrochemical devices. The resulting workflows will provide quantitative structure–property correlations across multiple length scales, forming the basis for automated, data-driven microscopy in next-generation materials discovery.
