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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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Illustration of melting point of lithium chloride, which is shown with green and blue structures in two rows.

Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications. 

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. 

The heartbeat Detector is pictured here, which is a black rectangular box with a heartbeat line and wording on the top to reflect its name

The Heartbeat Detector, developed at ORNL and licensed by Geovox Security Inc., detects hidden individuals in vehicles by measuring suspension vibrations. Now using a compact black box and cloud software, the system is more affordable and easier to use, while remaining the industry standard worldwide.

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.

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. 

 

Close up photo of components for jet engines, fanned out in a spiral from the center

Inspired by a visit to ORNL’s Manufacturing Demonstration Facility, Jonaaron Jones launched a career in additive manufacturing that led to founding Volunteer Aerospace and now leading Beehive Industries’ external parts business. Through close collaboration with MDF, Jones has helped drive Âé¶¹Ó°Òô in defense, aviation and energy, while growing high-tech jobs and strengthening the U.S. manufacturing base.

Three people standing in a lab holding materials

ORNL, the Tennessee Valley Authority and the Tennessee Department of Economic and Community Development were recognized by the Federal Laboratory Consortium, or FLC, for their efforts to develop Tennessee as a national leader in fusion energy.

Research scientist Daniel Jacobson is standing with his arms crossed with a dark black backdrop

Daniel Jacobson, distinguished research scientist in the Biosciences Division at ORNL, has been elected a Fellow of the American Institute for Medical and Biological Engineering, or AIMBE, for his achievements in computational biology. 

Researcher is sitting in bio lab surrounded with plants

Dave Weston studies how microorganisms influence plant health and stress tolerance, using the Advanced Plant Phenotyping Laboratory to accelerate research on plant-microbe interactions and develop resilient crops for advanced fuels, chemicals and 

Illustration of a quantum experiment: atoms in a lattice (inset) with entanglement effects radiating from a central particle on a textured surface.

Working at nanoscale dimensions, billionths of a meter in size, a team of scientists led by ORNL revealed a new way to measure high-speed fluctuations in magnetic materials. Knowledge obtained by these new measurements could be used to advance technologies ranging from traditional computing to the emerging field of quantum computing.