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![ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.](/sites/default/files/styles/list_page_thumbnail/public/2023-08/ZEISS%20signing%20handshake_0.jpg?h=c6980913&itok=4J8nVrPc)
A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine
![The OpeN-AM experimental platform, installed at the VULCAN instrument, features a robotic arm that prints layers of molten metal to create complex shapes. Credit: Jill Hemman/ORNL, U.S Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/Picture2.jpg?h=3c75dc16&itok=_NLdJ0Po)
Technologies developed by researchers at ORNL have received six 2023 R&D 100 Awards.
![Innovation Crossroads cohort 7](/sites/default/files/styles/list_page_thumbnail/public/2023-08/IC-cohort7-1000px.png?h=b6717701&itok=dHzO-FYD)
Seven entrepreneurs will embark on a two-year fellowship as the seventh cohort of Innovation Crossroads kicks off this month at ORNL. Representing a range of transformative energy technologies, Cohort 7 is a diverse class of innovators with promising new companies.
![ORNL’s Fernanda Santos examines a soil sample at an NGEE Arctic field site in the Alaskan tundra in June 2022. Credit: Amy Breen, University of Alaska Fairbanks.](/sites/default/files/styles/list_page_thumbnail/public/2023-08/Fernanda_Nome_June2022.jpg?h=06de31ac&itok=VGxKV_uY)
Wildfires are an ancient force shaping the environment, but they have grown in frequency, range and intensity in response to a changing climate. At ORNL, scientists are working on several fronts to better understand and predict these events and what they mean for the carbon cycle and biodiversity.
![TIP graphic](/sites/default/files/styles/list_page_thumbnail/public/2023-06/TIPbg_1200.png?h=da33fe38&itok=y7ggwHLV)
Scientist-inventors from ORNL will present seven new technologies during the Technology Innovation Showcase on Friday, July 14, from 8 a.m.–4 p.m. at the Joint Institute for Computational Sciences on ORNL’s campus.
![Frontier supercomputer](/sites/default/files/styles/list_page_thumbnail/public/2023-06/Frontier-logos_0.jpg?h=c6980913&itok=yuF5A0wj)
Innovations in artificial intelligence are rapidly shaping our world, from virtual assistants and chatbots to self-driving cars and automated manufacturing.
![Rigoberto Advincula](/sites/default/files/styles/list_page_thumbnail/public/2023-06/2020-P08153.jpg?h=8f9cfe54&itok=J1Xib1hr)
Rigoberto Advincula, a renowned scientist at ORNL and professor of Chemical and Biomolecular Engineering at the University of Tennessee, has won the Netzsch North American Thermal Analysis Society Fellows Award for 2023.
![ytterbium](/sites/default/files/styles/list_page_thumbnail/public/2023-05/Ytterbium-176%20approved%20crop_0.jpg?h=1f8bb2ae&itok=lTsZ7UjW)
ORNL’s electromagnetic isotope separator, or EMIS, made history in 2018 when it produced 500 milligrams of the rare isotope ruthenium-96, unavailable anywhere else in the world.
![Computing pioneer Jack Dongarra has been elected to the National Academy of Sciences.](/sites/default/files/styles/list_page_thumbnail/public/2023-05/Dongarra%20thumbnail.png?h=560fc75e&itok=ByoO7Sli)
Computing pioneer Jack Dongarra has been elected to the National Academy of Sciences in recognition of his distinguished and continuing achievements in original research.
![An AI-generated image representing atoms and artificial neural networks. Credit: Maxim Ziatdinov, ORNL](/sites/default/files/styles/list_page_thumbnail/public/2023-04/atoms3.jpg?h=ab622562&itok=dNMzrFw8)
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.