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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 a nitrogen dioxide molecule (depicted in blue and purple) captured in a nano-size pore of an MFM-520 metal-organic framework material as observed using neutron vibrational spectroscopy at Oak Ridge National Laboratory. Image credit: ORNL/Jill Hemman

An international team of scientists, led by the University of Manchester, has developed a metal-organic framework, or MOF, material

The students analyzed diatom images like this one to compare wild and genetically modified strains of these organisms. Credit: Alison Pawlicki/Oak Ridge National Laboratory, US Department of Energy.

Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.

CellSight allows for rapid mass spectrometry of individual cells. Credit: John Cahill, Oak Ridge National Laboratory/U.S. Dept of Energy

Researchers at the Department of Energy’s Oak Ridge National Laboratory have received five 2019 R&D 100 Awards, increasing the lab’s total to 221 since the award’s inception in 1963.

The configurational ensemble (a collection of 3D structures) of an intrinsically disordered protein, the N-terminal of c-Src kinase, which is a major signaling protein in humans. Credit: Oak Ridge National Laboratory, U.S. Dept. of Energy.

Using the Titan supercomputer and the Spallation Neutron Source at the Department of Energy’s Oak Ridge National Laboratory, scientists have created the most accurate 3D model yet of an intrinsically disordered protein, revealing the ensemble of its atomic-level structures.

early prototype of the optical array developed by Oak Ridge National Laboratory.

IDEMIA Identity & Security USA has licensed an advanced optical array developed at Oak Ridge National Laboratory. The portable technology can be used to help identify individuals in challenging outdoor conditions.

Heat impact map

A detailed study by Oak Ridge National Laboratory estimated how much more—or less—energy United States residents might consume by 2050 relative to predicted shifts in seasonal weather patterns 

The illustrations show how the correlation between lattice distortion and proton binding energy in a material affects proton conduction in different environments. Mitigating this interaction could help researchers improve the ionic conductivity of solid materials.

Ionic conduction involves the movement of ions from one location to another inside a material. The ions travel through point defects, which are irregularities in the otherwise consistent arrangement of atoms known as the crystal lattice. This sometimes sluggish process can limit the performance and efficiency of fuel cells, batteries, and other energy storage technologies.

Low-cost, compact, printed sensor that can collect and transmit data on electrical appliances for better load monitoring

Scientists at Oak Ridge National Laboratory have developed a low-cost, printed, flexible sensor that can wrap around power cables to precisely monitor electrical loads from household appliances to support grid operations.

 

Laminations such as these are compiled to form the core of modern electric vehicle motors. ORNL has developed a software toolkit to speed the development of new motor designs and to improve the accuracy of their real-world performance.

Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.

Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w

A team of scientists led by Oak Ridge National Laboratory used machine learning methods to generate a high-resolution map of vegetation growing in the remote reaches of the Alaskan tundra.