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Media Contacts
![Testing with ORNL tribology equipment found that new ionic liquid-based lubricant additives developed for water turbines significantly reduced friction and equipment wear. Credit: Genevieve Martin, ORNL/U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2024-05/MicrosoftTeams-image%20%286%29.png?h=c6980913&itok=aecfEFxo)
Scientists at the Department of Energy’s Oak Ridge National Laboratory have developed lubricant additives that protect both water turbine equipment and the surrounding environment.
![Chuck Greenfield, former assistant director of the DII-D National Fusion Program at General Atomics, has joined ORNL as ITER R&D Lead.](/sites/default/files/styles/list_page_thumbnail/public/2024-02/2024-P00545.jpg?h=8f9cfe54&itok=J_v6I5YE)
Chuck Greenfield, former assistant director of the DIII-D National Fusion Program at General Atomics, has joined ORNL as ITER R&D Lead.
![From left, Cable-Dunlap, Chi, Smith and Thornton have been named ORNL Corporate Fellows. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/corpfellow_nov23_2.jpg?h=d1cb525d&itok=G_PduE-d)
Four researchers at the Department of Energy’s Oak Ridge National Laboratory have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
![2023 Battelle Distinguished Inventors](/sites/default/files/styles/list_page_thumbnail/public/2023-11/23-G07641-Battelle-Distinguished-Inventor-graphic-pcg_0.jpg?h=d1cb525d&itok=uhmqAKgT)
Four scientists affiliated with ORNL were named Battelle Distinguished Inventors during the lab’s annual Innovation Awards on Dec. 1 in recognition of being granted 14 or more United States patents.
![Conceptual art depicts machine learning finding an ideal material for capacitive energy storage. Its carbon framework (black) has functional groups with oxygen (pink) and nitrogen (turquoise). Credit: Tao Wang/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-11/Press%20release%20image_0.jpg?h=706c9a24&itok=zX1lC5ud)
Guided by machine learning, chemists at ORNL designed a record-setting carbonaceous supercapacitor material that stores four times more energy than the best commercial material.
![Connecting wires to the interface of the topological insulator and superconductor enables probing of novel electronic properties. Researchers aim for qubits based on theorized Majorana particles. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-09/2023-P04516.jpg?h=c6980913&itok=BoCZtfwR)
Quantum computers process information using quantum bits, or qubits, based on fragile, short-lived quantum mechanical states. To make qubits robust and tailor them for applications, researchers from the Department of Energy’s Oak Ridge National Laboratory sought to create a new material system.
![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.
![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.
![Andrew Lupini](/sites/default/files/styles/list_page_thumbnail/public/2023-04/lupini.png?h=181bc054&itok=c-ov-WoV)
Andrew Lupini, a scientist and inventor at ORNL, has been elected Fellow of the Microscopy Society of America.
![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.