The United States could triple its current bioeconomy by producing more than 1 billion tons per year of plant-based biomass for renewable fuels, while meeting projected demands for food, feed, fiber, conventional forest products and exports, according to the DOE’s latest Billion-Ton Report led by ORNL.
Chuck Greenfield, former assistant director of the DIII-D National Fusion Program at General Atomics, has joined ORNL as ITER R&D Lead.
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.
Ilenne Del Valle is merging her expertise in synthetic biology and environmental science to develop new technologies to help scientists better understand and engineer ecosystems for climate resilience.
ORNL and the University of Tennessee, Knoxville, co-hosted the 2023 National Society of Black Physicists Annual Conference with the theme "Frontiers in Physics: From Quantum to Materials to the Cosmos.” As part of the three-day conference held near UT, attendees took a 30-mile trip to the ORNL campus for facility tours, science talks and workshops.
ORNL will lead a new DOE-funded project designed to accelerate bringing fusion energy to the grid. The Accelerate award focuses on developing a fusion power plant design concept that supports remote maintenance and repair methods for the plasma-facing components in fusion power plants.
Two fusion energy leaders have joined ORNL in the Fusion and Fission Energy and Science Directorate, or FFESD.
A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules.
ORNL is leading three research collaborations with fusion industry partners through the Innovation Network for FUSion Energy, or INFUSE, program that will focus on resolving technical challenges and developing innovative solutions to make practical fusion energy a reality.
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.