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ORNL researcher Priya Ranjan standing outside in front of brick pillars

From decoding plant genomes to modeling microbial behavior, computational biologist Priya Ranjan builds computational tools that turn extensive biological datasets into real-world insights. These tools transform the way scientists ask and answer complex biological questions that advance biotechnology breakthroughs and support cultivation of better crops for energy and food security. 

Close up image of researcher's hands showing a PAN nanofiber next to a strand of human hair.

Stronger than steel and lighter than aluminum, carbon fiber is a staple in aerospace and high-performance vehicles — and now, scientists at ORNL have found a way to make it even stronger.

Two ORNL researchers inspect carbon fiber materials - one black rectangular sheet and one see-through sheet of film.

Researchers at ORNL have developed an innovative new technique using carbon nanofibers to enhance binding in carbon fiber and other fiber-reinforced polymer composites – an advance likely to improve structural materials for automobiles, airplanes and other applications that require lightweight and strong materials. 

Three profile photos of ORNL researchers are cut out into one image representing three new members of leadership for the Center for Bioenergy Innovation

The Center for Bioenergy Innovation, or CBI, at the Department of Energy’s Oak Ridge National Laboratory has promoted Melissa Cregger and Carrie Eckert to serve as chief science officers, advancing the center’s mission of innovations for new domestic biofuels, chemicals and materials.

Five scientists and one in a boat are conducting fish sampling for the biological monitoring program on the DOE Oak Ridge Reservation.

ORNL’s Biological Monitoring and Abatement Program, or BMAP, is marking 40 years of helping steward the DOE’s 33,476 acres of land on which some of the nation’s most powerful science and technology missions are carried out. 

Illustration of the GRETA detector, a spherical array of metal cylinders. The detector is divided into two halves to show the inside of the machine. Both halves are attached to metal harnesses, displayed against a black and green cyber-themed background.

Analyzing massive datasets from nuclear physics experiments can take hours or days to process, but researchers are working to radically reduce that time to mere seconds using special software being developed at the Department of Energy’s Lawrence Berkeley and Oak Ridge national laboratories.  

Artist's rendering depicts a cantilever's sharp tip in an atomic force microscope scanning a material's surface to measure domain wall movement

As demand for energy-intensive computing grows, researchers at ORNL have developed a new technique that lets scientists see how interfaces move in promising materials for computing and other applications. The method, now available to users at the Center for Nanophase Materials Sciences at ORNL, could help design dramatically more energy-efficient technologies.

A 3D printing nozzle wrapped in insulation extrudes black composite material into a small square mold on a green and white flat surface in a lab setting. Inset shows a close-up of a pressure gauge connected to brass valves and tubing.

Scientists at ORNL have developed a vacuum-assisted extrusion method that reduces internal porosity by up to 75% in large-scale 3D-printed polymer parts. This new technique addresses the critical issue of porosity in large-scale prints but also paves the way for stronger composites. 

ORNL researcher Jesse Labbe is working with plants in a greenhouse. He is framed on all sides with bright green leaves

Jesse Labbé aims to leverage biology, computation and engineering to address societal challenges related to energy, national security and health, while enhancing U.S. competitiveness. Labbé emphasizes the importance of translating groundbreaking research into practical applications that have real-world impact.

Illustration of a glowing black box emitting digital particles that form into a 3D model of an electrical grid infrastructure, set against a background of binary code and data visualizations.

Researchers at Oak Ridge National Laboratory have developed a modeling method that uses machine learning to accurately simulate electric grid behavior while protecting proprietary equipment details. The approach overcomes a key barrier to accurate grid modeling, helping utilities plan for future demand and prevent blackouts.