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Media Contacts
Scientists have discovered a way to alter heat transport in thermoelectric materials, a finding that may ultimately improve energy efficiency as the materials
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.
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.
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.
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.
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.