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Media Contacts
Researchers at Oak Ridge National Laboratory have identified a statistical relationship between the growth of cities and the spread of paved surfaces like roads and sidewalks. These impervious surfaces impede the flow of water into the ground, affecting the water cycle and, by extension, the climate.
An Oak Ridge National Laboratory research team discovered that aspen saplings emerging after wildfire have less diverse microbiomes and more pathogens in their leaves, providing new insights about how fire affects ecosystem recovery.
Oak Ridge National Laboratory researchers have demonstrated that a new class of superalloys made of cobalt and nickel remains crack-free and defect-resistant in extreme heat, making them conducive for use in metal-based 3D printing applications.
A new Department of Energy report produced by Oak Ridge National Laboratory details national and international trends in hydropower, including the role waterpower plays in enhancing the flexibility and resilience of the power grid.
Oak Ridge National Laboratory contributed to an international study that found almost 300 novel types of microbes living near a deep sea volcano. These microbes, which could be used in biotechnology, reveal new insights about their extreme underwater environment.
A new tool from Oak Ridge National Laboratory can help planners, emergency responders and scientists visualize how flood waters will spread for any scenario and terrain.
Pauling’s Rules is the standard model used to describe atomic arrangements in ordered materials. Neutron scattering experiments at Oak Ridge National Laboratory confirmed this approach can also be used to describe highly disordered materials.
Oak Ridge National Laboratory scientists evaluating northern peatland responses to environmental change recorded extraordinary fine-root growth with increasing temperatures, indicating that this previously hidden belowground mechanism may play an important role in how carbon-rich peatlands respond to warming.
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.