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Media Contacts
![Andrew Sutton joined ORNL in 2020 to guide a newly formed team that focuses on chemical process scale-up in advanced manufacturing. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-04/2022-P01204.jpg?h=8f9cfe54&itok=n2ca9jZz)
When Andrew Sutton arrived at ORNL in late 2020, he knew the move would be significant in more ways than just a change in location.
![ORNL research scientist Christa Brelsford explained a mathematical framework she developed in 2018, which showed increased availability of infrastructure didn’t necessarily reduce inequality in its access. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-04/image001.jpg?h=1579b22a&itok=XzIna_Uc)
Unequal access to modern infrastructure is a feature of growing cities, according to a study published this week in the Proceedings of the National Academy of Sciences
![ORNL’s Marie Kurz examines the many factors affecting the health of streams and watersheds. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-02/2022-P00380--_1.jpg?h=918d9ab1&itok=3Fxfv-4i)
Spanning no less than three disciplines, Marie Kurz’s title — hydrogeochemist — already gives you a sense of the collaborative, interdisciplinary nature of her research at ORNL.
![An ORNL-led team studied the SARS-CoV-2 spike protein in the trimer state, shown here, to pinpoint structural transitions that could be disrupted to destabilize the protein and negate its harmful effects. Credit: Debsindhu Bhowmik/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/sars_cov_2_bk.png?h=05c2797f&itok=jQ2D9aTr)
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
![This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography. Credit: Ada Sedova/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/thermosipho_collabfold2_0.jpg?h=3432ff3c&itok=4xhLbjKZ)
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.