Katy Bradford: Cassette approach offers compelling construction solution
In a game-changing study, ORNL scientists developed a deep learning model — a type of artificial intelligence that mimics human brain function — to analyze high-speed videos of plasma plumes during a process called pulsed laser deposition.
For the first time, ORNL will run equipment developed at its research facilities on a commercially available quantum network at EPB Quantum Network powered by Qubitekk to help validate the technology's commercial viability.
ORNL’s Matthew Loyd will receive a Department of Energy Office of Science Early Career Research award.
Scientists using high-resolution aerial scans and computational modeling concluded that wildfires, storms and selective logging have become key drivers behind rainforest carbon emissions, outpacing clear-cutting practices.
A new convergent manufacturing platform, developed in only five months at the Department of Energy’s Oak Ridge National Laboratory, is debuting at the International Manufacturing Technology Show, or IMTS, in Chicago, Sept. 9–12, 2024.
The "SNS LEGO Model Middle School Outreach Program" invites middle school students to build a LEGO model of the SNS, a major scientific accelerator. This engaging initiative aims to teach students about STEM careers and the inner workings of the SNS through hands-on construction and interactive lessons. Launching next spring, the program seeks to inspire interest in science and engineering among young learners.
A team led by scientists at ORNL identified and demonstrated a method to process a plant-based material called nanocellulose that reduced energy needs by a whopping 21%, using simulations on the lab’s supercomputers and follow-on analysis.
A group of scientists at the Department of Energy’s Oak Ridge National Laboratory have conducted neutron scattering research to reveal key information about fungus cell membranes that could aid in developing new antifungal treatments.
ORNL is working with industry partners to develop a technique that combines 3D printing and conventional machining to produce large metal parts for clean energy applications. The project, known as Rapid Research on Universal Near Net Shape Fabrication Strategies for Expedited Runner Systems, or Rapid RUNNERS, recently received $15 million in funding from DOE.
As a mechanical engineer in building envelope materials research at ORNL, Bryan Maldonado sees opportunities to apply his scientific expertise virtually everywhere he goes, from coast to coast. As an expert in understanding how complex systems operate, he’s using machine learning methods to control the process and ultimately optimize performance.