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![ORNL’s Bianca Haberl and Amy Elliott hold 3D-printed collimators — an invention that has been licensed to ExOne, a leading binder jet 3D printer company. Credit: Genevieve Martin/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-02/Amy%20and%20Bianca_Small.jpg?h=c6980913&itok=hhxhYFhi)
Oak Ridge National Laboratory has licensed a novel method to 3D print components used in neutron instruments for scientific research to the ExOne Company, a leading maker of binder jet 3D printing technology.
![The agreement builds upon years of collaboration, including a 2016 effort using modeling tools developed at ORNL to predict the first six months of operations of TVA’s Watts Bar Unit 2 nuclear power plant. Credit: Andrew Godfrey/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2020-02/wb2_xenon_1.png?h=19940d61&itok=Da4pDLde)
OAK RIDGE, Tenn., Feb. 19, 2020 — The U.S. Department of Energy’s Oak Ridge National Laboratory and the Tennessee Valley Authority have signed a memorandum of understanding to evaluate a new generation of flexible, cost-effective advanced nuclear reactors.
![Using as much as 50 percent lignin by weight, a new composite material created at ORNL is well suited for use in 3D printing. Using as much as 50 percent lignin by weight, a new composite material created at ORNL is well suited for use in 3D printing.](/sites/default/files/styles/list_page_thumbnail/public/2018-P09551.jpg?itok=q7Ri01Qb)
Scientists at the Department of Energy’s Oak Ridge National Laboratory have created a recipe for a renewable 3D printing feedstock that could spur a profitable new use for an intractable biorefinery byproduct: lignin.
![ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system. ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the