![Constructing libraries of atomic defects (such as those shown above in graphene) relied on an approach that combined experiment and theory to extract and classify defects with STEM, predict electronic structure with DFT, and compare both of these experimental and theoretical results with STM results from the same system.](/sites/default/files/styles/list_page_thumbnail/public/2020-07/Picture8_0.png?h=6e835914&itok=-rVHgfJG)
Developed a deep-learning approach to automatically create libraries of structural and electronic properties of atomic defects in 2D materials.
Developed a deep-learning approach to automatically create libraries of structural and electronic properties of atomic defects in 2D materials.
On November 26, 2018, researchers from Oak Ridge National Laboratory received the Joule Award from Barbara Hoffheins of the National Nuclear Security Administration (NNSA) Office of International Nuclear Safeguards.
Researchers utilized a roll-to-roll process to coat electrically conductive carbon fibers with semiconducting silicon carbide nanoparticles—demonstrating a scalable method to make reinforcing fibers for composite applications requiring strong
Scientists have unraveled details of the mechanism of mechanical reinforcement in glassy polymer nanocomposites.1 Measurements in the interfacial layer ~2–4 nm around nanoparticles revealed that Young’s modulus, which defines the relationship between
Misfit heterojunctions formed by van der Waals (vdW) epitaxial growth of one crystalline metal chalcogenide monolayer on another was demonstrated for the first time to form p-n junctions that exhibit a photovoltaic response.
Spatial localization of distinct photoexcited species were identified in light harvesting perovskite based materials using ultrafast transient absorption microscopy (TAM).