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New research predicts peak groundwater extraction for key basins around the globe by the year 2050. The map indicates groundwater storage trends for Earth’s 37 largest aquifers using data from the NASA Jet Propulsion Laboratory GRACE satellite. Credit: NASA.

Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds. 

This graphic shows an unconventional approach to making widely used composite materials stronger and tougher. Thermoplastic fibers are deposited like cobwebs on top of rigid fibers to chemically form a supportive network with a surrounding matrix, or binder substance. Credit: Adam Malin/ORNL, U.S. Dept. of Energy

Scientists at ORNL have developed a method that demonstrates how fiber-reinforced polymer composite materials used in the automotive, aerospace and renewable energy industries can be made stronger and tougher to better withstand mechanical or structural stresses over time.

SOS26 attendees standing in front of the Kennedy Space Center on Merrit Island, Florida the night of their dinner reception provided by the conference sponsors. The keynote speaker was Rupak Biswas from NASA. Credit: Judy Potok/ORNL, U.S. Dept. of Energy

Held in Cocoa Beach, Florida from March 11 to 14, researchers across the computing and data spectra participated in sessions developed by staff members from the Department of Energy’s Oak Ridge National Laboratory, or ORNL, Sandia National Laboratories and the Swiss National Supercomputing Centre. 

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. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

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. 

ORNL’s Suhas Sreehari explains the algebraic and topological foundations of representation systems, used in generative AI technology such as large language models. Credit: Lena Shoemaker/ORNL, U.S. Dept. of Energy

In the age of easy access to generative AI software, user can take steps to stay safe. Suhas Sreehari, an applied mathematician, identifies misconceptions of generative AI that could lead to unintentionally bad outcomes for a user. 
 

Credit: Tyler Spano/ORNL, U.S. Dept. of Energy

Nuclear nonproliferation scientists at ORNL have published the Compendium of Uranium Raman and Infrared Experimental Spectra, a public database and analysis of structure-spectral relationships for uranium minerals. This first-of-its-kind dataset and corresponding analysis fill a key gap in the existing body of knowledge for mineralogists and actinide scientists. 

3D printed “Frankenstein design” collimator show the “scars” where the individual parts are joined

Scientists at ORNL have developed 3D-printed collimator techniques that can be used to custom design collimators that better filter out noise during different types of neutron scattering experiments

Assaf Anyamba Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

ORNL’s Assaf Anyamba has spent his career using satellite images to determine where extreme weather may lead to vector-borne disease outbreaks. His work has helped the U.S. government better prepare for outbreaks that happen during periods of extended weather events such as El Niño and La Niña, climate patterns in the Pacific Ocean that can affect weather worldwide. 

ORNL

ORNL took home the top honors in three categories at the second annual DOE Geospatial Science Poster competition, held on National GIS Day. For the second year in a row, DOE awarded ORNL top prize as Best Geospatial Program. Additionally, ORNL geospatial researchers took home first place prizes for their posters in the Best Departmental Element Alignment and Best Cartography categories.

ORNL researchers developed a long-sequenced AI transformer capable of processing millions of pathology reports to provide experts researching cancer diagnoses and management with more accurate information on cancer reporting.

In partnership with the National Cancer Institute, researchers from ORNL and Louisiana State University developed a long-sequenced AI transformer capable of processing millions of pathology reports to provide experts researching cancer diagnoses and management with exponentially more accurate information on cancer reporting.