Canan Karakaya, a R&D Staff member in the Chemical Process Scale-Up group at ORNL, was inspired to become a chemical engineer after she experienced a magical transformation that turned ammonia gas into ammonium nitrate, turning a liquid into white flakes gently floating through the air.
Although he built his career around buildings, Fengqi “Frank” Li likes to break down walls. Li was trained as an architect, but he doesn’t box himself in. Currently he is working as a computational developer at ORNL. But Li considers himself a designer. To him, that’s less a box than a plane – a landscape scattered with ideas, like destinations on a map that can be connected in different ways.
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.
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.
The American Society of Heating, Refrigeration and Air-Conditioning Engineers, or ASHRAE, selected Jason DeGraw, a researcher with ORNL, as one of 23 members elevated to Fellow during its 2024 winter conference.
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.
Four ORNL teams and one researcher were recognized for excellence in technology transfer and technology transfer innovation.
Louise Stevenson uses her expertise as an environmental toxicologist to evaluate the effects of stressors such as chemicals and other contaminants on aquatic systems.
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.
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.