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
- Andrzej Nycz
- Blane Fillingim
- Brian Post
- Chris Masuo
- Lauren Heinrich
- Luke Meyer
- Peeyush Nandwana
- Sudarsanam Babu
- Thomas Feldhausen
- William Carter
- Yousub Lee
- Alexander I Wiechert
- Alex Walters
- Bruce Hannan
- Costas Tsouris
- Debangshu Mukherjee
- Eve Tsybina
- Gs Jung
- Gyoung Gug Jang
- Joshua Vaughan
- Loren L Funk
- Md Inzamam Ul Haque
- Olga S Ovchinnikova
- Peter Wang
- Polad Shikhaliev
- Radu Custelcean
- Ramanan Sankaran
- Theodore Visscher
- Vimal Ramanuj
- Viswadeep Lebakula
- Vladislav N Sedov
- Wenjun Ge
- Yacouba Diawara

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.

ORNL has developed a large area thermal neutron detector based on 6LiF/ZnS(Ag) scintillator coupled with wavelength shifting fibers. The detector uses resistive charge divider-based position encoding.

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

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