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Researcher
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Diana E Hun
- Gurneesh Jatana
- Jonathan Willocks
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
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- Costas Tsouris
- Dave Willis
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- Michael Kirka
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- Obaid Rahman
- Peter Wang
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- Sally Ghanem
- Sreshtha Sinha Majumdar
- Sydney Murray III
- Vandana Rallabandi
- Vasilis Tzoganis
- Vasiliy Morozov
- William P Partridge Jr
- Xiang Lyu
- Yun Liu

ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.

High-gradient magnetic filtration (HGMF) is a non-destructive separation technique that captures magnetic constituents from a matrix containing other non-magnetic species. One characteristic that actinide metals share across much of the group is that they are magnetic.

We presented a novel apparatus and method for laser beam position detection and pointing stabilization using analog position-sensitive diodes (PSDs).

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

The invention discloses methods of using a reducing agent for catalytic oxygen reduction from CO2 streams, enabling the treated CO2 streams to meet the pipeline specifications.

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.

Lean-burn natural gas (NG) engines are a preferred choice for the hard-to-electrify sectors for higher efficiency and lower NOx emissions, but methane slip can be a challenge.

High and ultra-high vacuum applications require seals that do not allow leaks. O-rings can break down over time, due to aging and exposure to radiation. Metallic seals can damage sealing surfaces, making replacement of the original seal very difficult.

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.