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Researcher
- Alex Plotkowski
- Amit Shyam
- Hongbin Sun
- Peeyush Nandwana
- Blane Fillingim
- Brian Post
- James A Haynes
- Lauren Heinrich
- Sudarsanam Babu
- Sumit Bahl
- Thomas Feldhausen
- Yousub Lee
- Alexander I Wiechert
- Alice Perrin
- Andres Marquez Rossy
- Costas Tsouris
- Debangshu Mukherjee
- Gerry Knapp
- Gs Jung
- Gyoung Gug Jang
- Ilias Belharouak
- Jovid Rakhmonov
- Md Inzamam Ul Haque
- Nicholas Richter
- Olga S Ovchinnikova
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Radu Custelcean
- Ramanan Sankaran
- Ruhul Amin
- Ryan Dehoff
- Sunyong Kwon
- Thien D. Nguyen
- Vimal Ramanuj
- Vishaldeep Sharma
- Wenjun Ge
- Ying Yang

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.

Currently available cast Al alloys are not suitable for various high-performance conductor applications, such as rotor, inverter, windings, busbar, heat exchangers/sinks, etc.

The invented alloys are a new family of Al-Mg alloys. This new family of Al-based alloys demonstrate an excellent ductility (10 ± 2 % elongation) despite the high content of impurities commonly observed in recycled aluminum.

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.

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.

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.