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Pictured is a map that is color-coded into purple, black, orange, pink and yellow to depict building density and color based on morphology to predict height

Researchers are using machine learning to provide a more complete picture of building geometries that include building height to within three meters of accuracy. This model not only provides building height for any building in the world, but it will also feed into LandScan and other large government datasets for planning and response. 

3D map of Washington, D.C. that is a weather model of neighborhood during heat waves. The map is red and green indicating which buildings are giving off more heat
Scientists at ORNL have developed a first-ever urban heat wave simulation that takes into account the compounding effects from building infrastructure. The method provides a more accurate picture of the impacts from excessive heat on at-risk
Big group photo standing outside of a brick building with text underneath describing the IAEA workshop on safety, security and safeguards

In early November, ORNL hosted the International Atomic Energy Agency (IAEA) Interregional Workshop on Safety, Security and Safeguards by Design in Small Modular Reactors, which welcomed 76 attendees representing 15 countries, three U.S. national labs, domestic and international industry partners, as well as IAEA officers. 

Profile photo of man in short sleeve button up shirt with blue and grey feather pattern.

Joel Brogan, who leads the Multimodal Sensor Analytics group at Oak Ridge National Laboratory, has been elevated to senior membership in the Institute of Electrical and Electronics Engineers.

Pictured is the process of converting greenhouse gases to syngas, shown with a city scape with CO2 and CH4 turning to a mountain landscape with CO and H2

A chemical reaction can convert two polluting greenhouse gases into valuable building blocks for cleaner fuels and feedstocks, but the high temperature required for the reaction also deactivates the catalyst. A team led by ORNL has found a way to thwart deactivation. The strategy may apply broadly to other catalysts.

seven scientists' headshots are listed horizontally in a graphic representing the Battelle Distingished Inventors

Seven scientists affiliated with ORNL have been named Battelle Distinguished Inventors in recognition of being granted 14 or more United States patents. Since Battelle began managing ORNL in 2000, 104 ORNL researchers have reached this milestone.

microscopic ctherm biomass

Using a best-of-nature approach developed by researchers working with the Center for Bioenergy Innovation at the Department of Energy’s Oak Ridge National Laboratory and Dartmouth University, startup company Terragia Biofuel is targeting commercial biofuels production that relies on renewable plant waste and consumes less energy. The technology can help meet the demand for billions of gallons of clean liquid fuels needed to reduce emissions from airplanes, ships and long-haul trucks.

A small sample from the Frontier simulations reveals the evolution of the expanding universe in a region containing a massive cluster of galaxies from billions of years ago to present day (left).

In early November, researchers at the Department of Energy’s Argonne National Laboratory used the fastest supercomputer on the planet to run the largest astrophysical simulation of the universe ever conducted. The achievement was made using the Frontier supercomputer at Oak Ridge National Laboratory. 

Pictured here is the The S-adenosylmethionine molecule

Researchers have identified a molecule essential for the microbial conversion of inorganic mercury into the neurotoxin methylmercury, moving closer to blocking the dangerous pollutant before it forms. 

Nine men are pictured here standing in front of a window, posing for a group photo with 5 standing and 4 sitting.

A research team led by the University of Maryland has been nominated for the Association for Computing Machinery’s Gordon Bell Prize. The team is being recognized for developing a scalable, distributed training framework called AxoNN, which leverages GPUs to rapidly train large language models.