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Media Contacts
![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.](/sites/default/files/styles/list_page_thumbnail/public/2019-02/Carbon_capture_neutrons_0.jpg?h=4137a28c&itok=ZBLNFjNc)
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.
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 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](/sites/default/files/styles/list_page_thumbnail/public/rs2019_highlight_plot_3d.png?itok=5bROV_ys)
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.
![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 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](/sites/default/files/styles/list_page_thumbnail/public/study_area_one_dest_2.jpg?itok=2cWFkQvW)
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.
![Natalie Griffiths kneading in watershed at ORNL](/sites/default/files/styles/list_page_thumbnail/public/2018-P08986.jpg?itok=KTY95MCD)
Growing up, Natalie Griffiths dreamed of playing shortstop for the Toronto Blue Jays. With a stint on the Canadian national women’s baseball team under her belt, Griffiths has retired her glove and now fields scientific questions about carbon and nutrient cycling and water quality ...
Scientists studying a valuable, but vulnerable, species of poplar have identified the genetic mechanism responsible for the species’ inability to resist a pervasive and deadly disease. Their finding, published in the Proceedings of the National Academy of Sciences, could lead to more successful hybrid poplar varieties for increased biofuels and forestry production and protect native trees against infection.
![Methanogen_mercury_study3.jpg Methanogen_mercury_study3.jpg](/sites/default/files/styles/list_page_thumbnail/public/Methanogen_mercury_study3.jpg?itok=a79hsOOv)
Biologists from Oak Ridge National Laboratory and the Smithsonian Environmental Research Center have confirmed that microorganisms called methanogens can transform mercury into the neurotoxin methylmercury with varying efficiency across species.
![Researchers analyzed 15 years of data across 16 neighborhoods, shown in orange, in the Las Vegas Valley Water District to determine whether one home’s participation in the utility’s water conservation program had a measureable effect on their neighbors’ l Researchers analyzed 15 years of data across 16 neighborhoods, shown in orange, in the Las Vegas Valley Water District to determine whether one home’s participation in the utility’s water conservation program had a measureable effect on their neighbors’ l](/sites/default/files/styles/list_page_thumbnail/public/news/images/01%20-%20PeerEffectsMap.png?itok=LA4lx3bu)
A team led by Oak Ridge National Laboratory has discovered that residents living in arid environments share a desire for water security, which can ultimately benefit entire neighborhoods. Las Vegas, Nevada’s water utility was the first utility in the United States to implement ...
![ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system. ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the
![As hurricanes formed in the Gulf Coast, ORNL activated a computing technique to quickly gather building structure data from Texas’ coastal counties. Credit: Mark Tuttle/Oak Ridge National Laboratory, U.S. Dept. of Energy As hurricanes formed in the Gulf Coast, ORNL activated a computing technique to quickly gather building structure data from Texas’ coastal counties. Credit: Mark Tuttle/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/01%201%20-%20Impacts%20r1.jpg?itok=D1FzgK0y)
Geospatial scientists at Oak Ridge National Laboratory have developed a novel method to quickly gather building structure datasets that support emergency response teams assessing properties damaged by Hurricanes Harvey and Irma. By coupling deep learning with high-performance comp...