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Media Contacts
![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
![Cody Lloyd stands in front of images of historical nuclear field testing. The green and red dots are the machine learning algorithm recognizing features in the image. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/2023-P05797_0.jpg?h=4a7d1ed4&itok=S8h_wvJX)
Cody Lloyd became a nuclear engineer because of his interest in the Manhattan Project, the United States’ mission to advance nuclear science to end World War II. As a research associate in nuclear forensics at ORNL, Lloyd now teaches computers to interpret data from imagery of nuclear weapons tests from the 1950s and early 1960s, bringing his childhood fascination into his career
![ORNL-developed software tools for identifying and quantifying energy efficiency will be demonstrated to participants during an Energy Bootcamp sponsored by DOE’s Industrial Efficiency and Decarbonization Office. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/betterplants_0.png?h=8f9cfe54&itok=jR_fxVWk)
ORNL researchers have developed a training camp to help manufacturing industries reduce energy-related carbon dioxide emissions and improve cost savings.
![Jonathan Harter, a technical professional in ORNL’s Engineering Science and Technology Directorate, uses a robot and other automated methods to disassemble electric vehicle batteries for recycling or reuse in the electric grid. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/2021-P06328_0.jpg?h=036a71b7&itok=elZeutaQ)
After being stabilized in an ambulance as he struggled to breathe, Jonathan Harter hit a low point. It was 2020, he was very sick with COVID-19, and his job as a lab technician at ORNL was ending along with his research funding.
![ORNL will collaborate with Fairbanks Morse Defense on decarbonization efforts to develop alternative fuel technologies for marine engines. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/fairbanksimage_0.png?h=8f9cfe54&itok=LlS1hP5_)
ORNL, the Department of Energy’s largest multidisciplinary laboratory, and Fairbanks Morse Defense, a portfolio company of Arcline Investment Management, have entered into a Memorandum of Understanding to collaborate on the development and integration of alternative fuel technologies aimed at reducing the marine engine’s reliance on fossil fuels.
![AIRES 4 attendees hailing from seven national laboratories and from academia met to discuss robust engineering for digital twins. Credit: Pradeep Ramuhalli/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/aires_attendees_0.jpg?h=9fc2b970&itok=20wrmyuA)
ORNL hosted its fourth Artificial Intelligence for Robust Engineering and Science, or AIRES, workshop from April 18-20. Over 100 attendees from government, academia and industry convened to identify research challenges and investment areas, carving the future of the discipline.
![ORNL’s Travis Humble, Quantum Science Center director, addresses students during a working lunch. Credit: Teresa Hurt/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/travis_talking_2.jpg?h=827069f2&itok=QVTcVS8z)
Quantum computing sits on the cutting edge of scientific discovery. Given its novelty, the next generation of researchers will contribute significantly to the advancement of the field. However, this new crop of scientists must first be cultivated.
![Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/2023-P06111_0.jpg?h=c6980913&itok=dgI-yVRh)
After completing a bachelor’s degree in biology, Toya Beiswenger didn’t intend to go into forensics. But almost two decades later, the nuclear security scientist at ORNL has found a way to appreciate the art of nuclear forensics.
![Clouds of gray smoke in the lower left are funneled northward from wildfires in Western Canada, reaching the edge of the sea ice covering the Arctic Ocean. A second path of thick smoke is visible at the top center of the image, emanating from wildfires in the boreal areas of Russia’s Far East, in this image captured on July 13, 2023. Credit: NASA MODIS](/sites/default/files/styles/list_page_thumbnail/public/2023-07/NASA%20Arctic%20Circle%20wildfire%20smoke_image07182023_1km_1.jpg?h=dbdc3f84&itok=oHQVs6Bn)
Wildfires have shaped the environment for millennia, but they are increasing in frequency, range and intensity in response to a hotter climate. The phenomenon is being incorporated into high-resolution simulations of the Earth’s climate by scientists at the Department of Energy’s Oak Ridge National Laboratory, with a mission to better understand and predict environmental change.
![Two researchers standing back to back in a grassy area](/sites/default/files/styles/list_page_thumbnail/public/2023-07/CSJ_1716_updated.jpg?h=2dfa0735&itok=U-3yNm3M)
When geoinformatics engineering researchers at the Department of Energy’s Oak Ridge National Laboratory wanted to better understand changes in land areas and points of interest around the world, they turned to the locals — their data, at least.