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Media Contacts
ORNL’s Erin Webb is co-leading a new Circular Bioeconomy Systems Convergent Research Initiative focused on advancing production and use of renewable carbon from Tennessee to meet societal needs.
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.
An experiment by researchers at the Department of Energy’s Oak Ridge National Laboratory demonstrated advanced quantum-based cybersecurity can be realized in a deployed fiber link.
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.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
Technology Transfer staff from Department of Energy’s Oak Ridge National Laboratory attended the 2024 Consumer Electronics Show, or CES, in Las Vegas, Jan. 8–12.
The 2023 top science achievements from HFIR and SNS feature a broad range of materials research published in high impact journals such as Nature and Advanced Materials.
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.
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.