ORNL’s Omer Onar and Mostak Mohammad will present on ORNL's wireless charging technology in DOE’s Office of Technology Transitions National Lab Discovery Series Tuesday, April 30.
Shift Thermal, a member of Innovation Crossroads’ first cohort of fellows, is commercializing advanced ice thermal energy storage for HVAC, shifting the cooling process to be more sustainable, cost-effective and resilient. Shift Thermal wants to enable a lower-cost, more-efficient thermal energy storage method to provide long-duration resilient cooling when the electric grid is down.
Three ORNL intellectual property projects with industry partners have advanced in DOE's Office of Technology Transitions Making Advanced Technology Commercialization Harmonized, or Lab MATCH, prize, which encourages entrepreneurs to find actionable pathways that bring lab-developed intellectual property to market.
Students with a focus on building science will spend 10 weeks this summer interning at ORNL, the National Renewable Energy Laboratory and Pacific Northwest Laboratory as winners of the DOE’s Office of Energy Efficiency and Renewable Energy’s Building Technologies Office sixth annual JUMP into STEM finals competition.
ORNL scientists and researchers attended the annual American Geophysical Union meeting and came away inspired for the year ahead in geospatial, earth and climate science.
A modeling analysis led by ORNL gives the first detailed look at how geothermal energy can relieve the electric power system and reduce carbon emissions if widely implemented across the United States within the next few decades.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
A 19-member team of scientists from across the national laboratory complex won the Association for Computing Machinery’s 2023 Gordon Bell Special Prize for Climate Modeling for developing a model that uses the world’s first exascale supercomputer to simulate decades’ worth of cloud formations.
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.
Karen White, who works in ORNL’s Neutron Science Directorate, has been honored with a Lifetime Achievement Award.