Work

Revealing the Latent Atomic World Through Data-Driven Microscopy

Year: 2024

Type: article

Abstract: Many emerging technologies depend on the precise design of materials structure, chemistry, and defects. As devices shrink, manufacturing tolerances tighten, and performance envelopes improve, we must increasingly measure and manipulate materials at or near the single atom level. Here we describe how transmission electron microscopy (TEM) underpins our ability to see and direct the latent atomic world. We review a selection of our recent high-resolution TEM studies of the synthesis of oxide-based nanomaterials and their evolution in extreme environments. We then discuss powerful new artificial intelligence (AI) and machine learning (ML) approaches we have developed for rich, reproducible, and scalable experimentation. Furthermore, we conclude by discussing future developments that will enable new materials for breakthrough technologies.

Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)

Author: Spurgeon, Steven R. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); University of Washington, Seattle, WA (United States)]; Yano, Kayla H. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]; Doty, Christina M. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]; Akers, Sarah M. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]; Olszta, Matthew J. [Pacific Northwest National Laboratory (PNNL

Institutions: Pacific Northwest National Laboratory, University of Washington

Language: English

Topic: Advanced Electron Microscopy Techniques and Applications

Subfield: Structural Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: U.S. Department of Energy, Battelle, Basic Energy Sciences, Biological and Environmental Research, Laboratory Directed Research and Development, Pacific Northwest National Laboratory

Awards: G125460123, G138090931, G1851334662, G2270264153, G2988778103, G3090238903, G391136020, G4837814365, G5269527469, G5726405315

OpenAlex ID: W4390422715

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