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
- Adam Siekmann
- Alexander I Wiechert
- Costas Tsouris
- Debangshu Mukherjee
- Diana E Hun
- Easwaran Krishnan
- Gs Jung
- Gyoung Gug Jang
- Hong Wang
- Hyeonsup Lim
- James Manley
- Jamieson Brechtl
- Joe Rendall
- Karen Cortes Guzman
- Kashif Nawaz
- Kuma Sumathipala
- Md Inzamam Ul Haque
- Mengjia Tang
- Muneeshwaran Murugan
- Olga S Ovchinnikova
- Radu Custelcean
- Tomonori Saito
- Vivek Sujan
- Zoriana Demchuk

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.

Estimates based on the U.S. Department of Energy (DOE) test procedure for water heaters indicate that the equivalent of 350 billion kWh worth of hot water is discarded annually through drains, and a large portion of this energy is, in fact, recoverable.

The incorporation of low embodied carbon building materials in the enclosure is increasing the fuel load for fire, increasing the demand for fire/flame retardants.

No readily available public data exists for vehicle class and weight information that covers the entire U.S. highway network. The Travel Monitoring Analysis System, managed by the Federal Highway Administration covers only less than 1% of the US highway network.

Pairing hybrid neural network modeling techniques with artificial intelligence, or AI, controls has resulted in a unique hybrid system that creates a smart solution for traffic-signal timing.

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