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Modern TEM instrumentation can probe a wide range of structural, optical, andchemical properties with unprecedented resolution. However, each of theseproperties must be recorded in independent datasets using different detectormodes with no unifying framework currently available for quantitatively mappingtheir relationships onto chemically distinct features, particularly in complexmorphologies. Here, we tackle this challenge by proposing a data acquisitionand analysis workflow called "hypermodal data fusion," describing how todirectly couple an arbitrary number of highly disparate detector modesincluding spectroscopy and scanning diffraction and jointly analyze them forcorrelations. We demonstrate this concept on a random collection of anatase andrutile nanoparticles, first detailing how to use core-loss EELS to unmix thedifferent polymorphs despite three-dimensional overlap along the beam directionand then showing how this can be used to extract polymorph-specificcomposition, bandgaps, and crystal structure. We conclude with a discussion onthe applicability of this workflow for a broad range of materials systems.

Feature-specific correlation of structural, optical, and chemical properties in the transmission electron microscope with hypermodal data fusion