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ORNL chemist and YO-STEM founder Candice Halbert focuses her professional time operating the Liquids Reflectometer at ORNL’s Spallation Neutron Source. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

A chemist from Oak Ridge National Laboratory attracted national attention when her advocacy for science education made People magazine’s annual “Women Changing the World” issue.

Artist’s conceptual drawing illustrates the novel energy filtering technique using neutrons that enabled researchers at ORNL to freeze moving germanium telluride atoms in an unblurred image. The images offered key insights into how the material produces its outstanding thermoelectric performance. Credit: Jill Hemman/ORNL, U.S. Dept. of Energy

Scientists have long sought to better understand the “local structure” of materials, meaning the arrangement and activities of the neighboring particles around each atom. In crystals, which are used in electronics and many other applications, most of the atoms form highly ordered lattice patterns that repeat. But not all atoms conform to the pattern.

From left are UWindsor students Isabelle Dib, Dominik Dziura, Stuart Castillo and Maksymilian Dziura at ORNL’s Neutron Spin Echo spectrometer. Their work advances studies on a natural cancer treatment. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

A scientific instrument at ORNL could help create a noninvasive cancer treatment derived from a common tropical plant.

ORNL’s award-winning ultraclean condensing high-efficiency natural gas furnace features an affordable add-on technology that can remove more than 99.9% of acidic gases and other emissions. The technology can also be added to other natural gas-driven equipment. Credit: Jill Hemman/ORNL

Natural gas furnaces not only heat your home, they also produce a lot of pollution. Even modern high-efficiency condensing furnaces produce significant amounts of corrosive acidic condensation and unhealthy levels of nitrogen oxides

Steve Nagler

The truth is neutron scattering is not important, according to Steve Nagler. The knowledge gained from using it is what’s important

Oak Ridge National Laboratory materials scientist Zhili Feng, left, looks on as senior technician Doug Kyle operates a welding robot inside a robotic welding cell. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

The U.S. Departments of Energy and Defense teamed up to create a series of weld filler materials that could dramatically improve high-strength steel repair in vehicles, bridges and pipelines.

Oak Ridge National Laboratory’s Mitch Allmond works with the Facility for Rare Isotope Beams Decay Station initiator, which combined diverse detectors for FRIB’s first experiment. Credit: Robert Grzywacz/ORNL, U.S. Dept. of Energy

Two decades in the making, a new flagship facility for nuclear physics opened on May 2, and scientists from the Department of Energy’s Oak Ridge National Laboratory have a hand in 10 of its first 34 experiments.

The ORNL researchers’ findings may enable better detection of uranium tetrafluoride hydrate, a little-studied byproduct of the nuclear fuel cycle, and better understanding of how environmental conditions influence the chemical behavior of fuel cycle materials. Credit: Kevin Pastoor/Colorado School of Mines

ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.

ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

A team of researchers has developed a novel, machine learning–based  technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.

Oak Ridge National Laboratory researchers used an invertible neural network, a type of artificial intelligence that mimics the human brain, to select the most suitable materials for desired properties, such as flexibility or heat resistance, with high chemical accuracy. The study could lead to more customizable materials design for industry.

A study led by researchers at ORNL could help make materials design as customizable as point-and-click.