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Researcher
- Ryan Dehoff
- Venkatakrishnan Singanallur Vaidyanathan
- Vincent Paquit
- Alexey Serov
- Amir K Ziabari
- Diana E Hun
- Jaswinder Sharma
- Michael Kirka
- Philip Bingham
- Philip Boudreaux
- Stephen M Killough
- Xiang Lyu
- Adam Stevens
- Ahmed Hassen
- Alex Plotkowski
- Alice Perrin
- Amit K Naskar
- Amit Shyam
- Andres Marquez Rossy
- Beth L Armstrong
- Blane Fillingim
- Brian Post
- Bryan Maldonado Puente
- Christopher Ledford
- Clay Leach
- Corey Cooke
- David Nuttall
- Gabriel Veith
- Georgios Polyzos
- Gina Accawi
- Gurneesh Jatana
- Holly Humphrey
- James Haley
- James Szybist
- Jonathan Willocks
- Junbin Choi
- Khryslyn G Araño
- Logan Kearney
- Mark M Root
- Marm Dixit
- Meghan Lamm
- Michael Toomey
- Michelle Lehmann
- Nihal Kanbargi
- Nolan Hayes
- Obaid Rahman
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Peter Wang
- Rangasayee Kannan
- Ritu Sahore
- Roger G Miller
- Ryan Kerekes
- Sally Ghanem
- Sarah Graham
- Sudarsanam Babu
- Todd Toops
- Vipin Kumar
- Vlastimil Kunc
- William Peter
- Yan-Ru Lin
- Ying Yang
- Yukinori Yamamoto

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.

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.

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

High strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.

ORNL has developed a new hybrid membrane to improve electrochemical stability in next-generation sodium metal anodes.

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