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Currently there is no capability to test materials, sensors, and nuclear fuels at extremely high temperatures and under radiation conditions for nuclear thermal rocket propulsion or advanced reactors.
A human-in-the-loop machine learning (hML) technology potentially enhances experimental workflows by integrating human expertise with AI automation.
Recent advances in magnetic fusion (tokamak) technology have attracted billions of dollars of investments in startups from venture capitals and corporations to develop devices demonstrating net energy gain in a self-heated burning plasma, such as SPARC (under construction) and
The scanning transmission electron microscope (STEM) provides unprecedented spatial resolution and is critical for many applications, primarily for imaging matter at the atomic and nanoscales and obtaining spectroscopic information at similar length scales.
In order to avoid the limitations and costs due to the use of monolithic components for chemical vapor deposition, we developed a modular system in which the reaction chamber can be composed of a top and bottom cone, nozzle, and in-situ reaction chambers.
ORNL will develop an advanced high-performing RTG using a novel radioisotope heat source.
Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.
The use of Fluidized Bed Chemical Vapor Deposition to coat particles or fibers is inherently slow and capital intensive, as it requires constant modifications to the equipment to account for changes in the characteristics of the substrates to be coated.
Real-time tracking and monitoring of radioactive/nuclear materials during transportation is a critical need to ensure safety and security. Current technologies rely on simple tagging, using sensors attached to transport containers, but they have limitations.