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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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Postdoctoral researcher Nischal Kafle positions a component for a portable plasma imaging diagnostic device at ORNL in February. The device, a project for ARPA-E, is built of off-the-shelf parts. Credit: Carlos Jones/ORNL

The techniques Theodore Biewer and his colleagues are using to measure whether plasma has the right conditions to create fusion have been around awhile.

Scientists created a novel polymer that is as effective as natural proteins in transporting protons through a membrane. Credit: ORNL/Jill Hemman

Biological membranes, such as the “walls” of most types of living cells, primarily consist of a double layer of lipids, or “lipid bilayer,” that forms the structure, and a variety of embedded and attached proteins with highly specialized functions, including proteins that rapidly and selectively transport ions and molecules in and out of the cell.

Starch granules

Scientists at the Department of Energy’s Oak Ridge National Laboratory have developed a new method to peer deep into the nanostructure of biomaterials without damaging the sample. This novel technique can confirm structural features in starch, a carbohydrate important in biofuel production.

Edge computing is both dependent on and greatly influencing a host of promising technologies including (clockwise from top left): quantum computing; high-performance computing; neuromorphic computing; and carbon nanotubes.

We have a data problem. Humanity is now generating more data than it can handle; more sensors, smartphones, and devices of all types are coming online every day and contributing to the ever-growing global dataset.

Examples from the ORNL Overhead Vehicle Dataset, generated with images captured by GRIDSMART cameras. Image: Thomas Karnowski/ORNL

Each year, approximately 6 billion gallons of fuel are wasted as vehicles wait at stop lights or sit in dense traffic with engines idling, according to US Department of Energy estimates.

The image visualizes how the team’s multitask convolutional neural network classifies primary cancer sites. Image credit: Hong-Jun Yoon/ORNL

As the second-leading cause of death in the United States, cancer is a public health crisis that afflicts nearly one in two people during their lifetime.

Researchers in ORNL’s Quantum Information Science group summarized their significant contributions to quantum networking and quantum computing in a special issue of Optics & Photonics News. Image credit: Christopher Tison and Michael Fanto/Air Force Research Laboratory.

A team from the ORNL has conducted a series of experiments to gain a better understanding of quantum mechanics and pursue advances in quantum networking and quantum computing, which could lead to practical applications in cybersecurity and other areas.

Liane Russell

A select group gathered on the morning of Dec. 20 at the Department of Energy’s Oak Ridge National Laboratory for a symposium in honor of Liane B. Russell, the renowned ORNL mammalian geneticist who died in July.

As part of DOE’s HPC4Mobility initiative ORNL researchers developed machine learning algorithms that can control smart traffic lights at intersections to facilitate the smooth flow of traffic and increase fuel efficiency.

A modern, healthy transportation system is vital to the nation’s economic security and the American standard of living. The U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) is engaged in a broad portfolio of scientific research for improved mobility

Molecular dynamics simulations of the Fs-peptide revealed the presence of at least eight distinct intermediate stages during the process of protein folding. The image depicts a fully folded helix (1), various transitional forms (2–8), and one misfolded state (9). By studying these protein folding pathways, scientists hope to identify underlying factors that affect human health.

Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.