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
- Brian Post
- Blane Fillingim
- Chad Steed
- Junghoon Chae
- Lauren Heinrich
- Peeyush Nandwana
- Sudarsanam Babu
- Thomas Feldhausen
- Travis Humble
- 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
- Liam White
- Mark M Root
- Md Inzamam Ul Haque
- Michael Borish
- Olga S Ovchinnikova
- Philip Boudreaux
- Radu Custelcean
- Ramanan Sankaran
- Samudra Dasgupta
- Singanallur Venkatakrishnan
- Vimal Ramanuj
- Wenjun Ge

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.

The QVis Quantum Device Circuit Optimization Module gives users the ability to map a circuit to a specific quantum devices based on the device specifications.

QVis is a visual analytics tool that helps uncover temporal and multivariate variations in noise properties of quantum devices.

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.