1 - 10 of 14 Results
ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
The use of biomass fiber reinforcement for polymer composite applications, like those in buildings or automotive, has expanded rapidly due to the low cost, high stiffness, and inherent renewability of these materials. Biomass are commonly disposed of as waste.
High coercive fields prevalent in wurtzite ferroelectrics present a significant challenge, as they hinder efficient polarization switching, which is essential for microelectronic applications.
This technology is a laser-based heating unit that offers rapid heating profiles on a research scale with minimal incidental heating of materials processing environments.
We have developed an aerosol sampling technique to enable collection of trace materials such as actinides in the atmosphere.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.
A novel molecular sorbent system for low energy CO2 regeneration is developed by employing CO2-responsive molecules and salt in aqueous media where a precipitating CO2--salt fractal network is formed, resulting in solid-phase formation and sedimentation.
Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).