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Researcher
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- Blane Fillingim
- Brian Post
- Lauren Heinrich
- Peeyush Nandwana
- Philip Bingham
- Ryan Dehoff
- Sudarsanam Babu
- Thomas Feldhausen
- Vincent Paquit
- Yousub Lee
- Alexander I Wiechert
- Costas Tsouris
- Dave Willis
- Debangshu Mukherjee
- Diana E Hun
- Gina Accawi
- Gs Jung
- Gurneesh Jatana
- Gyoung Gug Jang
- Luke Chapman
- Mark M Root
- Md Inzamam Ul Haque
- Michael Kirka
- Obaid Rahman
- Olga S Ovchinnikova
- Philip Boudreaux
- Radu Custelcean
- Ramanan Sankaran
- Sydney Murray III
- Vasilis Tzoganis
- Vasiliy Morozov
- Vimal Ramanuj
- Wenjun Ge
- Yun Liu

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

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.

Among the methods for point source carbon capture, the absorption of CO2 using aqueous amines (namely MEA) from the post-combustion gas stream is currently considered the most promising.

This work seeks to alter the interface condition through thermal history modification, deposition energy density, and interface surface preparation to prevent interface cracking.

Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.

Ceramic matrix composites are used in several industries, such as aerospace, for lightweight, high quality and high strength materials. But producing them is time consuming and often low quality.

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.

The technology describes an electron beam in a storage ring as a quantum computer.

Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.