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Media Contacts
![Researchers at Oak Ridge National Laboratory upcycled a common plastic to develop a novel reusable adhesive with exceptional strength and toughness.Carlos Jones/ORNL; U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/2022-P00031_0.jpg?h=8f9cfe54&itok=7MuPCQ8s)
Researchers at ORNL used polymer chemistry to transform a common household plastic into a reusable adhesive with a rare combination of strength and ductility, making it one of the toughest materials ever reported.
![The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/E3SM_0.jpg?h=d5571230&itok=lKS66vCl)
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
![Ultra Safe Nuclear Corporation has licensed a novel method to 3D print highly resistant components for use in nuclear reactor designs. USNC Executive Vice President Kurt Terrani, formerly of ORNL, said the novel method will allow the company to make parts with desired complex shapes more efficiently. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/2020-P15249_0.jpg?h=245bf488&itok=Ts1prptZ)
A novel method to 3D print components for nuclear reactors, developed by the Department of Energy’s Oak Ridge National Laboratory, has been licensed by Ultra Safe Nuclear Corporation.
![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.
![Using quantum Monte Carlo methods, the researchers simulated bulk VO2. Yellow and turquoise represent changes in electron density between the excited and ground states of a compound composed of oxygen, in red, and vanadium, in blue, which allowed them to evaluate how an oxygen vacancy, in white, can alter the compound’s properties. Credit: Panchapakesan Ganesh/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/image001_0.png?h=11d99c73&itok=sdREw4na)
Neuromorphic devices — which emulate the decision-making processes of the human brain — show great promise for solving pressing scientific problems, but building physical systems to realize this potential presents researchers with a significant