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Media Contacts
![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.
![CFM’s RISE open fan engine architecture. Image: GE Aerospace](/sites/default/files/styles/list_page_thumbnail/public/2023-06/02-CFM_RISE_Program_Open_Fan%5B1%5D_0.jpg?h=790be497&itok=Ulzp5W_p)
To support the development of a revolutionary new open fan engine architecture for the future of flight, GE Aerospace has run simulations using the world’s fastest supercomputer capable of crunching data in excess of exascale speed, or more than a quintillion calculations per second.
![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.
![ORNL researchers have developed a new pressing method, shown as blue circle on right, that produces a more uniform solid electrolyte than the traditionally processed material with more voids, shown as gray circle on left. The material can be integrated into a battery system, center, for improved stability and rate performance. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-06/Electrolyte.StoryTip_0.png?h=b6717701&itok=PIYcf5iS)
ORNL scientists found that a small tweak created big performance improvements in a type of solid-state battery, a technology considered vital to broader electric vehicle adoption.
![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.
![The Fuel Pellet Fueling Laboratory at ORNL is part of a suite of fusion energy R&D capabilities and provides test equipment and related diagnostics for carrying out experiments to develop pellet injectors for plasma fueling applications. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-06/2021-P02876_0.jpg?h=c6980913&itok=8fqWlX5k)
ORNL will team up with six of eight companies that are advancing designs and research and development for fusion power plants with the mission to achieve a pilot-scale demonstration of fusion within a decade.
![A study led by ORNL researchers examines the causes behind ordering of cations, the positive ions that help make double perovskite oxides look promising as an energy source. Credit: Getty Images](/sites/default/files/styles/list_page_thumbnail/public/2023-05/CationBanner.png?h=ae114f5c&itok=czF5YUhD)
A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.
![Tomonori Saito, Oak Ridge National Laboratory’s Inventor of the Year, was honored at Battelle’s Celebration of Solvers. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-05/2022-P11389.jpg?h=04df159e&itok=ptAD9rQS)
Tomonori Saito, a distinguished innovator in the field of polymer science and senior R&D staff member at ORNL, was honored on May 11 in Columbus, Ohio, at Battelle’s Celebration of Solvers.
![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.