Optimal 3D chemical imaging with multimodal electron tomography

Manassa, Jason; Millsaps, William; Schwartz, Jonathan; Hovden, Robert

doi:10.1038/s41524-025-01750-y

Title: Optimal 3D chemical imaging with multimodal electron tomography

Journal Article · Mon Aug 25 00:00:00 UTC 2025 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-025-01750-y · OSTI ID:3023337

Manassa, Jason ^[1]; Millsaps, William ^[2]; Schwartz, Jonathan ^[3]; Hovden, Robert ^[1]

Univ. of Michigan, Ann Arbor, MI (United States)
Univ. of Michigan, Ann Arbor, MI (United States); Stanford Univ., CA (United States)
Univ. of Michigan, Ann Arbor, MI (United States); Chan Zuckerberg Imaging Inst., Redwood City, CA (United States)

Accurate mapping of nanoscale chemistry in three dimensions (3D) has been a longstanding challenge. Modern electron microscopy provides chemical images by electron energy loss spectroscopy (EELS) and energy dispersive x-ray spectrometry (EDX) but requires high fluences that damage specimens. In 3D, the requirements are worse; electron tomography demands many high-fluence chemical maps for reconstruction, creating a tradeoff between resolution, accuracy, and sample survival. Fused multimodal electron tomography (MM-ET) alleviates this requirement by leveraging lower-fluence high-angle annular dark-field (HAADF) images alongside a few chemical maps to dramatically improve chemical resolution. Here, experimental and computational parameter space is systematically explored to determine when MM-ET performs best. Ideal imaging conditions balance sample survival with resolution and chemical specificity; we recommend a tilt range of at least ± 70°, acquiring 40 equally spaced HAADF projections (signal-to-noise > 10), and 7 EELS/EDX maps of each chemistry (signal-to-noise > 4).

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Research Organization:: Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0024147

OSTI ID:: 3023337

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 11; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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