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Media Contacts
ORNL scientists have determined how to avoid costly and potentially irreparable damage to large metallic parts fabricated through additive manufacturing, also known as 3D printing, that is caused by residual stress in the material.
A team of researchers at ORNL demonstrated that a light-duty passenger electric vehicle can be wirelessly charged at 100-kW with 96% efficiency using polyphase electromagnetic coupling coils with rotating magnetic fields.
Two different teams that included Oak Ridge National Laboratory employees were honored Feb. 20 with Secretary’s Honor Achievement Awards from the Department of Energy. This is DOE's highest form of employee recognition.
Scientists at ORNL are looking for a happy medium to enable the grid of the future, filling a gap between high and low voltages for power electronics technology that underpins the modern U.S. electric grid.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
Electric vehicles can drive longer distances if their lithium-ion batteries deliver more energy in a lighter package. A prime weight-loss candidate is the current collector, a component that often adds 10% to the weight of a battery cell without contributing energy.
Oak Ridge National Laboratory researchers have identified the most energy-efficient 2024 model year vehicles available in the United States, including electric and hybrids, in the latest edition of the Department of Energy’s Fuel Economy Guide.
Researchers at ORNL became the first to 3D-print large rotating steam turbine blades for generating energy in power plants.
Scientists from more than a dozen institutions have completed a first-of-its-kind high-resolution assessment of carbon dioxide removal potential in the United States, charting a path to achieve a net-zero greenhouse gas economy by 2050.
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.