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)
- Isotope Science and Enrichment Directorate (6)
- National Security Sciences Directorate (17)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (128)
- User Facilities (27)
- (-) Information Technology Services Directorate (2)
Researcher
- Brian Post
- Blane Fillingim
- Lauren Heinrich
- Peeyush Nandwana
- Sudarsanam Babu
- Thomas Feldhausen
- Yousub Lee
- Alexander I Wiechert
- Alex Roschli
- Cameron Adkins
- Costas Tsouris
- Debangshu Mukherjee
- Diana E Hun
- Gina Accawi
- Gs Jung
- Gurneesh Jatana
- Gyoung Gug Jang
- Isha Bhandari
- Jason Jarnagin
- Kevin Spakes
- Liam White
- Lilian V Swann
- Mark M Root
- Mark Provo II
- Md Inzamam Ul Haque
- Michael Borish
- Olga S Ovchinnikova
- Philip Boudreaux
- Radu Custelcean
- Ramanan Sankaran
- Rob Root
- Sam Hollifield
- Singanallur Venkatakrishnan
- Vimal Ramanuj
- Wenjun Ge

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

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.

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