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Researcher
- Isabelle Snyder
- Venkatakrishnan Singanallur Vaidyanathan
- Adam Siekmann
- Alexey Serov
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- Emilio Piesciorovsky
- Jaswinder Sharma
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- Nolan Hayes
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- Ozgur Alaca
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- Sally Ghanem
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- Viswadeep Lebakula
- Yarom Polsky

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.

An electrochemical cell has been specifically designed to maximize CO2 release from the seawater while also not changing the pH of the seawater before returning to the sea.

The ORNL invention addresses the challenge of poor mechanical properties of dry processed electrodes, improves their electrical properties, while improving their electrochemical performance.

Faults in the power grid cause many problems that can result in catastrophic failures. Real-time fault detection in the power grid system is crucial to sustain the power systems' reliability, stability, and quality.

Hydrogen is in great demand, but production relies heavily on hydrocarbons utilization. This process contributes greenhouse gases release into the atmosphere.

Water heaters and heating, ventilation, and air conditioning (HVAC) systems collectively consume about 58% of home energy use.

This disclosure introduces an innovative tool that capitalizes on historical data concerning the carbon intensity of the grid, distinct to each electric zone.

This disclosure introduces an innovative tool that capitalizes on historical data concerning the carbon intensity of the grid, distinct to each electric zone.