Filter Results
Related Organization
- Biological and Environmental Systems Science Directorate (23)
- Computing and Computational Sciences Directorate (35)
- Energy Science and Technology Directorate (217)
- Fusion and Fission Energy and Science Directorate
(21)
- Information Technology Services Directorate (2)
- Isotope Science and Enrichment Directorate (6)
- National Security Sciences Directorate (17)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (128)
- User Facilities (27)
Researcher
- Blane Fillingim
- Brian Post
- Lauren Heinrich
- Peeyush Nandwana
- Sudarsanam Babu
- Thomas Feldhausen
- Yousub Lee
- Alexander I Wiechert
- Callie Goetz
- Costas Tsouris
- Debangshu Mukherjee
- Fred List III
- Gs Jung
- Gyoung Gug Jang
- Keith Carver
- Md Inzamam Ul Haque
- Olga S Ovchinnikova
- Radu Custelcean
- Ramanan Sankaran
- Richard Howard
- Thomas Butcher
- Vimal Ramanuj
- Wenjun Ge

A pressure burst feature has been designed and demonstrated for relieving potentially hazardous excess pressure within irradiation capsules used in the ORNL High Flux Isotope Reactor (HFIR).

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.

This technology is a strategy for decreasing electromagnetic interference and boosting signal fidelity for low signal-to-noise sensors transmitting over long distances in extreme environments, such as nuclear energy generation applications, particularly for particle detection.

This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.