Summary¶

Here, we presented a tutorial to introduce scientists to fused multi-modal electron microscopy; a technique that takes two separate signal types from the electron microscope and combines them to leverage helpful information embedded in each. By improving conventional spectroscopic elemental imaging techniques, crucial information on material chemistry, from performance to structure, can be elucidated for various applications. In improving elemental SNR, we also have opened the possibility of lower dose elemental imaging of radiation-sensitive materials such as polymers and biomaterials. This tutorial lowers the barrier to entry for scientists without coding, optimization, or mixed modality backgrounds by streamlining the learning process. This tutorial and the accompanying fused multi-modal code are easily customizable with minimal user input for fine-tuning the algorithm for optimal convergence.

Future Outlook¶

Despite the validation in this work on X-EDS and EELS datasets, this data fusion approach can extend to ptychography, 4D-STEM, low-loss EELS, valence EELS, and other advanced modalities. As “advanced” techniques become commonplace alongside standard imaging, we expect more signals to be integrated into the fused multi-modal pipeline, allowing for an unprecedented understanding of nano- and atomic-scale materials. Additionally, the recent advancements in liquid helium cooling capabilities provide additional dose protection for current radiation-sensitive materials so that a cryo-fused multi-modal approach opens up the possibility of high-resolution chemical imaging in soft matter and other biological systems.