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ORNL's Communications team works with news media seeking information about the laboratory. Media may use the resources listed below or send questions to news@ornl.gov.

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A deep look inside a cell membrane showing the production of materials from plant biomass, shown with shapes that consist of four green balls connected with a red ball on one end, dotted with smaller white balls on each surface.

Scientists at ORNL and the University of Cincinnati achieved a breakthrough in understanding the vulnerability of microbes to the butanol they produce during fermentation of plant biomass. The discovery could pave the way for more efficient production of domestic fuels, chemicals and materials.

Animated graphic with a plant on the right, blue sphere on the left and blue glowing dots scattered throughout.

To help reduce the likelihood of losing future cultivated crops to drought and other seasonal hardships, researchers from ORNL, Budapest and Hungary are using neutrons, light microscopy and transmission electron microscopy to study the 'Never Never' plant, known for its ability to endure periods of little to no rain. 

Secretary Wright leans over red computer door, signing with silver sharpie as ORNL Director Stephen Streiffer looks on

During his first visit to 91°µÍø, Energy Secretary Chris Wright compared the urgency of the Lab’s World War II beginnings to today’s global race to lead in artificial intelligence, calling for a “Manhattan Project 2.â€

Six images fanned out across the right side of the page with the first page showcasing the report cover. To the right hand side is a green oak leaf.

A workshop led by scientists at ORNL sketched a road map toward a longtime goal: development of autonomous, or self-driving, next-generation research laboratories. 

Hugh O'Neil, director or ORNL's Center for Structural Molecular Biology is sitting in the lab on a stool, hand on desk with glasses on. There are lab related items blurred in the foreground.

Hugh O’Neill’s lifelong fascination with the complexities of the natural world drives his research at ORNL, where he’s using powerful neutron beams to dive deep into the microscopic realm of biological materials and unlock secrets for better production of domestic biofuels and bioproducts.

Photo is a graphical representation of lithium ions (glowing orbs) move through a diffusion gate (gold triangle) in a solid-state electrolyte

A team of scientists led by a professor from Duke University discovered a way to help make batteries safer, charge faster and last longer. They relied on neutrons at ORNL to understand at the atomic scale how lithium moves in lithium phosphorus sulfur chloride, a promising new type of solid-state battery material known as a superionic compound. 

Autonomous Configurable Component Evaluation Power Test platform, called ACCEPT, enabling automated characterization of semiconductor devices.

Researchers at 91°µÍø have developed a new automated testing capability for semiconductor devices, which is newly available to researchers and industry partners in the Grid Research Integration and Deployment Center.

Emrullah Aydin, an ORNL research is posing for a headshot in a black suit with black tie
Emrullah Aydin, an R&D staff associate in the Vehicle Power Electronics Research group at the Department of Energy’s 91°µÍø, has been elevated to senior member status in the Institute of Electrical and Electronics Engineers.
Vivek Sujan is posing for a photo in a suit jacket with brown stairs and grey and brown background

Vivek Sujan, a distinguished R&D scientist in the Applied Research for Mobility Systems group at ORNL, has been named a 2024 National Association of Inventors Fellow for his numerous transportation-related patents. 

Man is flying drone in hurricane aftermath, holding the controller

During Hurricanes Helene and Milton, ORNL deployed drone teams and the Mapster platform to gather and share geospatial data, aiding recovery and damage assessments. ORNL's EAGLE-I platform tracked utility outages, helping prioritize recovery efforts. Drone data will train machine learning models for faster damage detection in future disasters.