1 - 10 of 19 Results
ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
How fast is a vehicle traveling? For different reasons, this basic question is of interest to other motorists, insurance companies, law enforcement, traffic planners, and security personnel. Solutions to this measurement problem suffer from a number of constraints.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
A novel method that prevents detachment of an optical fiber from a metal/alloy tube and allows strain measurement up to higher temperatures, about 800 C has been developed. Standard commercial adhesives typically only survive up to about 400 C.
The ORNL invention addresses the challenge of poor mechanical properties of dry processed electrodes, improves their electrical properties, while improving their electrochemical performance.
Test facilities to evaluate materials compatibility in hydrogen are abundant for high pressure and low temperature (<100C).
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
ORNL has developed a new hydrothermal synthesis route to generate high quality battery cathode precursors. The new route offers excellent compositional control, homogenous spherical morphologies, and an ammonia-free co-precipitation process.
The technologies provide a coating method to produce corrosion resistant and electrically conductive coating layer on metallic bipolar plates for hydrogen fuel cell and hydrogen electrolyzer applications.
Sodium-ion batteries are a promising candidate to replace lithium-ion batteries for large-scale energy storage system because of their cost and safety benefits.