Research Area
We develop and apply advanced electron microscopy to understand the structure, chemistry, and dynamics of materials across scales.
Battery Materials
Battery performance is measured at the cell level, but the origins of degradation hide at the atomic scale. We investigate capacity fade and impedance rise in next-generation energy storage materials, including all-solid-state batteries, Na/K-ion batteries, and Ni-rich cathodes. Advanced electron microscopy reveals buried interfaces, ion transport pathways, and the structural changes that govern electrode lifetime. By connecting these observations with electrochemical data, we uncover process–structure–performance relationships and identify routes to better materials.
Hydrogen & Semiconductor Materials
The performance of a catalyst or semiconductor can be shaped by changes confined to a few atomic layers. We extend our microscopy expertise to hydrogen production and fuel-cell catalysts, as well as semiconductor interfaces where defects, strain, and local electric fields govern function. Imaging, diffraction, and spectroscopy connect atomic arrangements with chemical and electronic structure. Through gas-cell experiments, 4D-STEM, and monochromated EELS, we are developing ways to reveal why materials activate, degrade, or change their properties under working conditions.
In Situ / Operando Reaction Imaging & Analysis
Most electron microscopy captures a frozen snapshot of a material. In situ and operando techniques move beyond that limit, allowing us to follow reactions as atoms, phases, and interfaces reorganize under electrical, thermal, or chemical stimuli. Our real-time studies of alloying anodes connect ion diffusion, reaction-front migration, and volume expansion with the mechanisms that govern material behavior. Building on this experience, we are extending dynamic microscopy to gas-cell experiments and correlative 4D-STEM / EELS analysis of hydrogen and catalyst materials.
AI Data Segmentation & Quantitative Electron Microscopy
A single microscopy image can contain thousands of particles and interfaces, while 4D-STEM and EELS generate vast multidimensional datasets. Turning these observations into meaningful measurements requires reliable identification of the regions we want to analyze. AEML develops AI-assisted segmentation workflows that separate particles, coatings, pores, and other regions of interest, then quantify their size, shape, and spatial distribution. By connecting these regions with diffraction and spectroscopy, we extend local observations into statistically meaningful materials analysis.
3D Structure & Correlative Electron Microscopy
Materials are inherently three-dimensional, yet a two-dimensional projection cannot fully reveal pore connectivity, crack pathways, or buried interfaces. AEML uses STEM tomography and FIB-SEM serial sectioning to reconstruct complex microstructures in three dimensions. We connect these volumes with imaging, diffraction, and chemical analysis of the same regions, linking electrode-scale architecture to local structure and composition. This correlative approach supports the design of transport pathways and the understanding of failure in functional materials.