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Media Contacts
![When an electron beam drills holes in heated graphene, single-atom vacancies, shown in purple, diffuse until they join with other vacancies to form stationary structures and chains, shown in blue. Credit: Ondrej Dyck/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-12/variation.jpg?h=bedff801&itok=9S6jmOVH)
Oak Ridge National Laboratory researchers serendipitously discovered when they automated the beam of an electron microscope to precisely drill holes in the atomically thin lattice of graphene, the drilled holes closed up.
![Eight ORNL scientists are among the world’s most highly cited researchers, Credit: Butch Newton/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-11/2021-P09536_0.png?h=82f92a78&itok=BeEG2fpP)
Eight ORNL scientists are among the world’s most highly cited researchers, according to a bibliometric analysis conducted by the scientific publication analytics firm Clarivate.
![Rama Vasudevan](/sites/default/files/styles/list_page_thumbnail/public/2022-10/2015-P066641.jpg?h=49ab6177&itok=Y-cZRl53)
Rama Vasudevan, a research scientist at the Department of Energy’s Oak Ridge National Laboratory, has been elected a Fellow of the American Physical Society, or APS. The honor recognizes members who have made significant contributions to physics and its application to science and technology.
![A team of researchers used mathematics to predict which areas of the SARS-CoV-2 spike protein are most likely to mutate. Credit: Jill Hemman/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-09/covid19_jh_0_0.png?h=252f27fa&itok=c3Qts7j0)
Researchers from ORNL, the University of Tennessee at Chattanooga and Tuskegee University used mathematics to predict which areas of the SARS-CoV-2 spike protein are most likely to mutate.
![ORNL’s RapidCure improves lithium-ion electrode production by producing electrodes faster, reducing the energy necessary for manufacturing and eliminating the need for a solvent recycling unit. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-08/RapidCure_0.jpg?h=def3cf70&itok=BFENW6Cu)
Researchers at the Department of Energy’s Oak Ridge National Laboratory and their technologies have received seven 2022 R&D 100 Awards, plus special recognition for a battery-related green technology product.
![Larry Allard](/sites/default/files/styles/list_page_thumbnail/public/2022-08/Allard.png?h=748cb951&itok=GvagcEmh)
Larry Allard, a distinguished research staff member at Oak Ridge National Laboratory, has been named a Fellow of the Microanalysis Society.
![Magnetic quantum material broadens platform for probing next-gen information technologies](/sites/default/files/styles/list_page_thumbnail/public/2022-07/2022-G00762_DataOilPaintingStill_Stone_jnd_April2022.jpg?h=d1cb525d&itok=oepl7N2Y)
Scientists at ORNL used neutron scattering to determine whether a specific material’s atomic structure could host a novel state of matter called a spiral spin liquid.
![MDF Exterior](/sites/default/files/styles/list_page_thumbnail/public/2022-06/2021-p07609.jpg?h=be3e4b3a&itok=YfKK7Wy2)
ORNL scientists will present new technologies available for licensing during the annual Technology Innovation Showcase. The event is 9 a.m. to 3 p.m. Thursday, June 16, at the Manufacturing Demonstration Facility at ORNL’s Hardin Valley campus.
![A smart approach to microscopy and imaging developed at Oak Ridge National Laboratory could drive discoveries in materials for future technologies. Credit: Adam Malin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-05/PFC%20Surface%20v3%20300dpi_1.jpg?h=9c3ba2fc&itok=s8arZbEt)
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
![Oak Ridge National Laboratory researchers used an invertible neural network, a type of artificial intelligence that mimics the human brain, to select the most suitable materials for desired properties, such as flexibility or heat resistance, with high chemical accuracy. The study could lead to more customizable materials design for industry.](/sites/default/files/styles/list_page_thumbnail/public/2022-04/CCSD_NeuralNetworkBanner.png?h=b16f811b&itok=fxqDEvs_)
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.