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Media Contacts
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.
Scientists at Oak Ridge National Laboratory have developed a low-cost, printed, flexible sensor that can wrap around power cables to precisely monitor electrical loads from household appliances to support grid operations.
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.
As the rise of antibiotic-resistant bacteria known as superbugs threatens public health, Oak Ridge National Laboratory’s Shuo Qian and Veerendra Sharma from the Bhaba Atomic Research Centre in India are using neutron scattering to study how an antibacterial peptide interacts with and fights harmful bacteria.
Higher carbon dioxide levels caused 30 percent more wood growth in young forest stands across the temperate United States over a decade, according to an analysis led by Oak Ridge National Laboratory.
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.
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.
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.
Oak Ridge National Laboratory scientists analyzed more than 50 years of data showing puzzlingly inconsistent trends about corrosion of structural alloys in molten salts and found one factor mattered most—salt purity.
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.