Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
Scientists at ORNL and the University of Nebraska have developed an easier way to generate electrons for nanoscale imaging and sensing, providing a useful new tool for material science, bioimaging and fundamental quantum research.
ORNL scientists have modified a single microbe to simultaneously digest five of the most abundant components of lignocellulosic biomass, a big step forward in the development of a cost-effective biochemical conversion process to turn plants into
Oak Ridge National Laboratory researchers used additive manufacturing to build a first-of-its kind smart wall called EMPOWER.
Oak Ridge National Laboratory researchers have designed and additively manufactured a first-of-its-kind aluminum device that enhances the capture of carbon dioxide emitted from fossil fuel plants and other industrial processes.
Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.
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.
Scientists at Oak Ridge National Laboratory have demonstrated a direct relationship between climate warming and carbon loss in a peatland ecosystem.
Five researchers at the Department of Energy’s Oak Ridge National Laboratory have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
Burak Ozpineci of the Electrical and Electronics Systems Research Division at Oak Ridge National Laboratory has won the 2020 IEEE Power Electronics Society Vehicle and Transportation Systems Achievement Award.
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.