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![QLAN submit - A team from the U.S. Department of Energy’s Oak Ridge National Laboratory, Stanford University and Purdue University developed and demonstrated a novel, fully functional quantum local area network, or QLAN, to enable real-time adjustments to information shared with geographically isolated systems at ORNL using entangled photons passing through optical fiber. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/QLAN%20submit_0.jpg?h=cd715a88&itok=JV1MjQHH)
A rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.
![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.
![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.