Dissecting complex nanoparticle heterostructures via multimodal data fusion with aberration-corrected STEM spectroscopy

With nanostructured materials such as catalytic heterostructures projected to play a critical role in applications ranging from water splitting to energy harvesting, tailoring their properties to specific tasks requires an increasingly comprehensive characterization of their local chemical and electronic landscape. Although aberration-corrected electron spectroscopy currently provides sufficient spatial resolution to study this space, an approach to concurrently dissect both the electronic structure and full composition of buried metal/oxide interfaces remains a considerable challenge. In this manuscript, we outline a statistical methodology to jointly analyze simultaneously-acquired STEM EELS and EDX datasets by fusing them along their shared spatial factors. We show how this procedure can be used to derive a rich descriptive model for estimating both transition metal valency and full chemical composition from encapsulated morphologies such as core-shell nanoparticles. We demonstrate this on a heterogeneous Co-P thin film catalyst, concluding that this system is best described as a multi-shell phosphide structure with a P-doped metallic Co core.