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The 25th annual National School on Neutron and X-ray Scattering was held August 6–18. Each year, graduate students visit Oak Ridge and Argonne National Laboratories to learn how to use neutrons and X-rays to study energy and materials. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

In 2023, the National School on X-ray and Neutron Scattering, or NXS, marked its 25th year during its annual program, held August 6–18 at the Department of Energy’s Oak Ridge and Argonne National Laboratories.   

Construction is underway at ORNL's Spallation Neutron Source. Credit: The Spallation Neutron Source at Oak Ridge National Laboratory — already the world’s most powerful accelerator-based neutron source — will be on a planned hiatus through June 2024 as crews work to upgrade the facility. Credit: Brett Riffert/ORNL, U.S. Dept. of Energy

The Spallation Neutron Source — already the world’s most powerful accelerator-based neutron source — will be on a planned hiatus through June 2024 as crews work to upgrade the facility. Much of the work — part of the facility’s Proton Power Upgrade project — will involve building a connector between the accelerator and the planned Second Target Station.

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.

Connecting  wires to the interface of the topological insulator and superconductor enables probing of novel electronic properties. Researchers aim for qubits based on theorized Majorana particles. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Quantum computers process information using quantum bits, or qubits, based on fragile, short-lived quantum mechanical states. To make qubits robust and tailor them for applications, researchers from the Department of Energy’s Oak Ridge National Laboratory sought to create a new material system.

Scientists conducted microbial DNA sampling at a Yellowstone National Park hot spring for a study sponsored by DOE’s Biological and Environmental Research program, the National Science Foundation and NASA. Credit: Mircea Podar/ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory scientists studied hot springs on different continents and found similarities in how some microbes adapted despite their geographic diversity.

Yaoping Wang. Credit: Yaoping Wang

Yaoping Wang, postdoctoral research associate at ORNL, has received an Early Career Award from the Asian Ecology Section, or AES, of the Ecological Society of America.

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.

A new nanoscience study led by an ORNL quantum researcher takes a big-picture look at how scientists study materials at the smallest scales. Credit: Getty Images

A new nanoscience study led by a researcher at ORNL takes a big-picture look at how scientists study materials at the smallest scales.

Madhavi Martin portrait image

Madhavi Martin brings a physicist’s tools and perspective to biological and environmental research at the Department of Energy’s Oak Ridge National Laboratory, supporting advances in bioenergy, soil carbon storage and environmental monitoring, and even helping solve a murder mystery.

ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.

A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine