News

A former intern for ORNL was selected to represent Tennessee presenting his research at the National Junior Science and Humanities Symposium. Langalibalele “Langa” Lunga, a senior at Farragut High School in Knoxville, Tennessee, interned with ORNL working on deep learning for fast scanning microscopy, a technique for capturing microscopic images more rapidly than traditional methods.

From decoding plant genomes to modeling microbial behavior, computational biologist Priya Ranjan builds computational tools that turn extensive biological datasets into real-world insights. These tools transform the way scientists ask and answer complex biological questions that advance biotechnology breakthroughs and support cultivation of better crops for energy and food security. 

Researchers at ORNL have developed a tool that gives builders a quick way to measure, correct and certify level foundations. FLAT, or the Flat and Level Analysis Tool, examines a 360-degree laser scan of a construction site using ORNL-developed segmentation algorithms and machine learning to locate uneven areas on a concrete slab. 

ORNL’s Biological Monitoring and Abatement Program, or BMAP, is marking 40 years of helping steward the DOE’s 33,476 acres of land on which some of the nation’s most powerful science and technology missions are carried out. 

As the focus on energy resiliency and competitiveness increases, the development of advanced materials for next-generation, commercial fusion reactors is gaining attention. A recent paper examines a promising candidate for these reactors: ultra-high-temperature ceramics, or UHTCs.

UT-Battelle has contributed up to $475,000 for the purchase and installation of advanced manufacturing equipment to support a program at Tennessee’s Oak Ridge High School that gives students direct experience with the AI- and robotics-assisted workplace of the future. 

The Heartbeat Detector, developed at ORNL and licensed by Geovox Security Inc., detects hidden individuals in vehicles by measuring suspension vibrations. Now using a compact black box and cloud software, the system is more affordable and easier to use, while remaining the industry standard worldwide.

Scientists at ORNL have developed a vacuum-assisted extrusion method that reduces internal porosity by up to 75% in large-scale 3D-printed polymer parts. This new technique addresses the critical issue of porosity in large-scale prints but also paves the way for stronger composites. 

Research teams at the Department of Energy’s Oak Ridge National Laboratory received computing resource awards to train and test AI foundation models for science. A total of six ORNL projects were awarded allocations from the National Artificial Intelligence Research Resource, or NAIRR, pilot and the Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program to train their AI models.