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Media Contacts
![ORNL collaborator Hsiu-Wen Wang led the neutron scattering experiments at the Spallation Neutron Source to probe complex electrolyte solutions that challenge nuclear waste processing at Hanford and other sites. Credit: Genevieve Martin/Oak Ridge National Laboratory, U.S. Dept. of Energy.](/sites/default/files/styles/list_page_thumbnail/public/2019-05/2019-P01240_0.jpg?h=c6980913&itok=RLLi1M-g)
Researchers at the Department of Energy’s Oak Ridge National Laboratory, Pacific Northwest National Laboratory and Washington State University teamed up to investigate the complex dynamics of low-water liquids that challenge nuclear waste processing at federal cleanup sites.
![Using artificial intelligence, Oak Ridge National Laboratory analyzed data from published medical studies to reveal the potential of direct and indirect impacts of bullying.](/sites/default/files/styles/list_page_thumbnail/public/2019-04/bullying_img.png?h=48484608&itok=zxX54Jz1)
Oak Ridge National Laboratory is using artificial intelligence to analyze data from published medical studies associated with bullying to reveal the potential of broader impacts, such as mental illness or disease.
![Desalination diagram](/sites/default/files/styles/list_page_thumbnail/public/2019-04/DesalDiagram-_0.jpg?h=d4f5ec8a&itok=-yhECJ4V)
A team of scientists led by Oak Ridge National Laboratory used carbon nanotubes to improve a desalination process that attracts and removes ionic compounds such as salt from water using charged electrodes.
![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.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/Slide1_0.png?h=c855054e&itok=aNbgxXsc)
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.
![ORNL researcher Karren More has been elected fellow of the Microscopy Society of America.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/K_More_th.jpg?h=655057a4&itok=53tPHa-r)
OAK RIDGE, Tenn., March 22, 2019 – Karren Leslie More, a researcher at the Department of Energy’s Oak Ridge National Laboratory, has been elected fellow of the Microscopy Society of America (MSA) professional organization.
![(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.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/TourassiWH%5B1%5D.png?h=26b5064d&itok=HUC2iYmE)
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.
![To develop complex materials with superior properties, Vera Bocharova uses diverse methods including broadband dielectric spectroscopy. Credit: Oak Ridge National Laboratory, U.S. Dept. of Energy; photographer Jason Richards](/sites/default/files/styles/list_page_thumbnail/public/2019-02/2016-p05202.jpg?h=b6236d98&itok=w-Sd8giq)
Vera Bocharova at the Department of Energy’s Oak Ridge National Laboratory investigates the structure and dynamics of soft materials—polymer nanocomposites, polymer electrolytes and biological macromolecules—to advance materials and technologies for energy, medicine and other applications.
![As part of a preliminary study, ORNL scientists used critical location data collected from Twitter to map the location of certain power outages across the United States.](/sites/default/files/styles/list_page_thumbnail/public/2019-02/PowerOutageTweets_map_0.png?h=6448fdc1&itok=AUit-O2Y)
Gleaning valuable data from social platforms such as Twitter—particularly to map out critical location information during emergencies— has become more effective and efficient thanks to Oak Ridge National Laboratory.
OAK RIDGE, Tenn., Feb. 12, 2019—A team of researchers from the Department of Energy’s Oak Ridge and Los Alamos National Laboratories has partnered with EPB, a Chattanooga utility and telecommunications company, to demonstrate the effectiveness of metro-scale quantum key distribution (QKD).
![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.