Filter Results
Related Organization
- Biological and Environmental Systems Science Directorate (26)
- Computing and Computational Sciences Directorate (38)
- Energy Science and Technology Directorate (223)
- Fusion and Fission Energy and Science Directorate
(24)
- Information Technology Services Directorate (3)
- Isotope Science and Enrichment Directorate (7)
- National Security Sciences Directorate (20)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (135)
- User Facilities (27)
Researcher
- Hongbin Sun
- Andrzej Nycz
- Chris Masuo
- Luke Meyer
- William Carter
- Alexander I Wiechert
- Alex Walters
- Bruce Hannan
- Costas Tsouris
- Debangshu Mukherjee
- Gs Jung
- Gyoung Gug Jang
- Ilias Belharouak
- Joshua Vaughan
- Loren L Funk
- Md Inzamam Ul Haque
- Olga S Ovchinnikova
- Peter Wang
- Polad Shikhaliev
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Radu Custelcean
- Ruhul Amin
- Theodore Visscher
- Thien D. Nguyen
- Vishaldeep Sharma
- Vladislav N Sedov
- Yacouba Diawara

In nuclear and industrial facilities, fine particles, including radioactive residues—can accumulate on the interior surfaces of ventilation ducts and equipment, posing serious safety and operational risks.

The invention presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.

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.

Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.

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