News

Photo of glowing, pink diamond-shaped figure. This is illuminated with light, encircled with a wreath of around 70 blue tube-like shapes.
Promethium’s secrets exposed
Male student wearing clear safety goggles peering through a 3D-printed item.
tnAchieves students learn about lab careers
Photo of a man in a black jacket suit with a blue shirt crossing his arms outside. He is smiling.
Wagner receives ASME Medal

Filter News

Area of Research

News Type

Date

News Topics

Media Contacts

Connect with ORNL

Chathuddasie Amarasinghe explains her research poster, “Using Microfluidic Mother Machine Devices to Study the Correlated Dynamics of Ribosomes and Chromosomes in Escherichia Coli.” Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Annual meeting for world-class nanophase materials sciences users earns praise for exciting program

Speakers, scientific workshops, speed networking, a student poster showcase and more energized the Annual User Meeting of the Department of Energy’s Center for Nanophase Materials Sciences, or CNMS, Aug. 7-10, near Market Square in downtown Knoxville, Tennessee.

Yue Yuan, Weinberg Distinguished Staff Fellow at ORNL, is researching ways to create new materials to help the environment. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Yue Yuan, Weinberg Research Fellow, uses nature to create sustainable materials

Growing up in China, Yue Yuan stood beneath the world’s largest hydroelectric dam, built to harness the world’s third-longest river. Her father brought her to Three Gorges Dam every year as it was being constructed across the Yangtze River so she could witness its progress.

Andrea Delgado, Distinguished Staff Fellow at Oak Ridge National Laboratory, uses quantum computing to help elucidate the fundamental particles of the universe. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Andrea Delgado unites fundamental, high energy physics with quantum computing

Andrea Delgado is looking for elementary particles that seem so abstract, there appears to be no obvious short-term benefit to her research.

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.

ORNL neural network study harnesses made-to-order design to pair properties to materials

A study led by researchers at ORNL could help make materials design as customizable as point-and-click.

Using quantum Monte Carlo methods, the researchers simulated bulk VO2. Yellow and turquoise represent changes in electron density between the excited and ground states of a compound composed of oxygen, in red, and vanadium, in blue, which allowed them to evaluate how an oxygen vacancy, in white, can alter the compound’s properties. Credit: Panchapakesan Ganesh/ORNL, U.S. Dept. of Energy

Artificially altered material could accelerate neuromorphic device development

Neuromorphic devices — which emulate the decision-making processes of the human brain — show great promise for solving pressing scientific problems, but building physical systems to realize this potential presents researchers with a significant

As part of the Next-Generation Ecosystem Experiments Arctic project, scientists are gathering and incorporating new data about the Alaskan tundra into global models that predict the future of our planet. Credit: ORNL/U.S. Dept. of Energy

ORNL expertise supports latest IPCC report and efforts to understand, address climate change

Improved data, models and analyses from ORNL scientists and many other researchers in the latest global climate assessment report provide new levels of certainty about what the future holds for the planet 

ORNL’s Sergei Kalinin and Rama Vasudevan (foreground) use scanning probe microscopy to study bulk ferroelectricity and surface electrochemistry -- and generate a lot of data. Credit: Jason Richards/ORNL, U.S. Dept. of Energy

ORNL’s superb materials expertise, data and AI tools propel progress

At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.

Heavy-duty vehicles contribute 23% of transportation emissions of greenhouse gases and account for almost one-quarter of the fuel consumed annually in the U.S. Credit: Chris Bair/Unsplash

Heavy-duty vehicles an ideal entry into hydrogen fuel cell use

Through a consortium of Department of Energy national laboratories, ORNL scientists are applying their expertise to provide solutions that enable the commercialization of emission-free hydrogen fuel cell technology for heavy-duty

Verónica Melesse Vergara speaks with third and fourth graders at East Side Intermediate School in Brownsville. Credit: ORNL, U.S. Dept. of Energy

ORNL scientists share excitement for engineering with students statewide

Twenty-seven ORNL researchers Zoomed into 11 middle schools across Tennessee during the annual Engineers Week in February. East Tennessee schools throughout Oak Ridge and Roane, Sevier, Blount and Loudon counties participated, with three West Tennessee schools joining in.

The CrossVis application includes a parallel coordinates plot (left), a tiled image view (right) and other interactive data views. Credit: Chad Steed/Oak Ridge National Laboratory, U.S. Dept. of Energy

Visual analytics tool plucks elusive patterns from elaborate datasets

From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.