1 - 10 of 18 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.
The ORNL invention addresses the challenge of poor mechanical properties of dry processed electrodes, improves their electrical properties, while improving their electrochemical performance.
When a magnetic field is applied to a type-II superconductor, it penetrates the superconductor in a thin cylindrical line known as a vortex line. Traditional methods to manipulate these vortices are limited in precision and affect a broad area.
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.
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.
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 proposed solid electrolyte can solve the problem of manufacturing solid electrolyte when heating and densifying the solid electrolyte powder. The material can avoid also the use of solid electrolyte additive with cathode to prepare a catholyte.