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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.
High coercive fields prevalent in wurtzite ferroelectrics present a significant challenge, as they hinder efficient polarization switching, which is essential for microelectronic applications.
The technologies provide a system and method of needling of veiled AS4 fabric tape.
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.
ORNL will develop an advanced high-performing RTG using a novel radioisotope heat source.
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.
Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.
Aromas play a significant role in the quality and safety of food, beverages, and even manufactured products. The ability to detect and interpret these aromas accurately can enhance product safety and consumer satisfaction.