Skip to main content
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

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.

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.

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

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.

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

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

Andrew Lupini, a scientist and inventor at ORNL, has been elected Fellow of the Microscopy Society of America.

ORNL scientists mutated amino acids in a receptor protein, shown in green, which diminished interaction with the SARS-CoV-2 virus spike protein, shown in red. Mutating the receptor protein hampered the virus’s ability to infect host cells. Credit: ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory scientists exploring bioenergy plant genetics have made a surprising discovery: a protein domain that could lead to new COVID-19 treatments.

Oak Ridge National Laboratory led a team of scientists to design a molecule that disrupts the infection mechanism of the SARS-CoV-2 coronavirus and could be used to develop new treatments for COVID-19 and future virus outbreaks. Credit: Michelle Lehman/ORNL, U.S. Dept. of Energy

A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory designed a molecule that disrupts the infection mechanism of the SARS-CoV-2 coronavirus and could be used to develop new treatments for COVID-19 and other viral diseases.

Computational systems biologists at ORNL worked with the U.S. Department of Veterans Affairs and other institutions to identify 139 locations across the human genome tied to risk factors for varicose veins, marking a first step in the development of new treatments. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

As part of a multi-institutional research project, scientists at ORNL leveraged their computational systems biology expertise and the largest, most diverse set of health data to date to explore the genetic basis of varicose veins.

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

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.

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

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.