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 A group of ORNL staff standing in a long corridor with flags hanging from the ceiling

For 25 years, scientists at Oak Ridge National Laboratory have used their broad expertise in human health risk assessment, ecology, radiation protection, toxicology and information management to develop widely used tools and data for the U.S. Environmental Protection Agency as part of the agency’s Superfund program.

Attendees of SMC23 pose for their annual group photo in downtown Knoxville, TN.

ORNL hosted its annual Smoky Mountains Computational Sciences and Engineering Conference in person for the first time since the COVID-19 pandemic.

Director of ORNL’s AI Initiative Prasanna Balaprakash addresses attendees at the Generative AI for ORNL Science Workshop. Credit: ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory hosted its Smoky Mountains Computational Science and Engineering Conference for the first time in person since the COVID pandemic broke in 2020. The conference, which celebrated its 20th consecutive year, took place at the Crowne Plaza Hotel in downtown Knoxville, Tenn., in late August.

Steve Nolan, left, who manages many ORNL facilities for United Cleanup Oak Ridge, and Carl Dukes worked closely together to accommodate bringing members of the public into the Oak Ridge Reservation to collect distant images from overhead for the BRIAR biometric recognition project. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Carl Dukes’ career as an adept communicator got off to a slow start: He was about 5 years old when he spoke for the first time. “I’ve been making up for lost time ever since,” joked Dukes, a technical professional at the Department of Energy’s Oak Ridge National Laboratory.

Plutonium oxide is loaded onto a truck for shipping. Adam Parkison/ORNL, U.S. Dept. of Energy

In June, ORNL hit a milestone not seen in more than three decades: producing a production-quality amount of plutonium-238

Steven Hamilton, an R&D scientist in the HPC Methods for Nuclear Applications group at ORNL, leads the ExaSMR project. ExaSMR was developed to run on the Oak Ridge Leadership Computing Facility’s exascale-class supercomputer, Frontier. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

The Exascale Small Modular Reactor effort, or ExaSMR, is a software stack developed over seven years under the Department of Energy’s Exascale Computing Project to produce the highest-resolution simulations of nuclear reactor systems to date. Now, ExaSMR has been nominated for a 2023 Gordon Bell Prize by the Association for Computing Machinery and is one of six finalists for the annual award, which honors outstanding achievements in high-performance computing from a variety of scientific domains.  

ORNL researchers are demonstrating an automation system for this portable system, currently based in Colorado, for treatment of non-traditional water sources to drinking water standards. Credit: Tzahi Cath/Colorado School of Mines

Researchers at ORNL are developing advanced automation techniques for desalination and water treatment plants, enabling them to save energy while providing affordable drinking water to small, parched communities without high-quality water supplies.

Cadet Elyse Wages, Mike Shaffer and Amanda Sandifer pose with a collected sample of air. Credit: Liz Neunsinger/ORNL, U.S. Dept. of Energy

Cadet Elyse Wages, a rising junior at the United States Air Force Academy, visited ORNL with one goal in mind: collect air.

Tom Karnowski (left) and Jordan Johnson (right). Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Tom Karnowski and Jordan Johnson of ORNL have been named chair and vice chair, respectively, of the East Tennessee section of the Institute of Electrical and Electronics Engineers, or IEEE.

The DEMAND single crystal diffractometer at the High Flux Isotope Reactor, or HFIR, is the latest neutron instrument at the Department of Energy’s Oak Ridge National Laboratory to be equipped with machine learning-assisted software, called ReTIA. Credit: Jeremy Rumsey/ORNL, U.S. Dept. of Energy

Neutron experiments can take days to complete, requiring researchers to work long shifts to monitor progress and make necessary adjustments. But thanks to advances in artificial intelligence and machine learning, experiments can now be done remotely and in half the time.