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ORNL nuclear engineer Chris Petrie

Oak Ridge National Laboratory is using ultrasonic additive manufacturing to embed highly accurate fiber optic sensors in heat- and radiation-resistant materials, allowing for real-time monitoring that could lead to greater insights and safer reactors.

(From left) ORNL Associate Laboratory Director for Computing and Computational Sciences Jeff Nichols; ORNL Health Data Sciences Institute Director Gina Tourassi; DOE Deputy Under Secretary for Science Thomas Cubbage; ORNL Task Lead for Biostatistics Blair Christian; and ORNL Research Scientist Ioana Danciu were invited to the White House to showcase an ORNL-developed digital tool aimed at better matching cancer patients with clinical trials.

OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.

carbon nanospikes

OAK RIDGE, Tenn., March 1, 2019—ReactWell, LLC, has licensed a novel waste-to-fuel technology from the Department of Energy’s Oak Ridge National Laboratory to improve energy conversion methods for cleaner, more efficient oil and gas, chemical and

ORNL will use state-of-the-art R&D tools at the Battery Manufacturing Facility to develop new methods for separating and reclaiming valuable materials from spent EV batteries.

The use of lithium-ion batteries has surged in recent years, starting with electronics and expanding into many applications, including the growing electric and hybrid vehicle industry. But the technologies to optimize recycling of these batteries have not kept pace.

Using neutrons from the TOPAZ beamline, which is optimal for locating hydrogen atoms in materials, ORNL researchers observed a single-crystal neutron diffraction structure of the insoluble carbonate salt formed by absorption of carbon dioxide from the air.

Researchers used neutron scattering at Oak Ridge National Laboratory’s Spallation Neutron Source to investigate the effectiveness of a novel crystallization method to capture carbon dioxide directly from the air.

An ORNL-developed graphite foam, which could be used in plasma-facing components in fusion reactors, performed well during testing at the Wendlestein 7-X stellarator in Germany.

Scientists have tested a novel heat-shielding graphite foam, originally created at Oak Ridge National Laboratory, at Germany’s Wendelstein 7-X stellarator with promising results for use in plasma-facing components of fusion reactors.

CO2 release by mild heating of the BIG-bicarbonate solid. The released CO2 gas is trapped in the orange balloon, while the released water vapors are trapped by condensation in the ice-cooled U-shaped tube. Credit: Neil J. Williams and Erick Holguin.

Scientists at the Department of Energy’s Oak Ridge National Laboratory (ORNL) have developed a process that could remove CO2 from coal-burning power plant emissions in a way that is similar to how soda lime works in scuba diving rebreathers. Their research, published January 31 in...

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.

Researchers analyzed the oxygen structure (highlighted in red) found in a perovskite’s crystal structure at room temperature, 500°C and 900°C using neutron scattering at ORNL’s Spallation Neutron Source. Analyzing how these structures impact solid oxide f

A University of South Carolina research team is investigating the oxygen reduction performance of energy conversion materials called perovskites by using neutron diffraction at Oak Ridge National Laboratory’s Spallation Neutron Source.

ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La

Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.