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
- Amit Shyam
- Alex Plotkowski
- James A Haynes
- Ryan Dehoff
- Sumit Bahl
- Adam Stevens
- Alice Perrin
- Andres Marquez Rossy
- Annetta Burger
- Brian Post
- Carter Christopher
- Chance C Brown
- Christopher Fancher
- Dean T Pierce
- Debangshu Mukherjee
- Debraj De
- Gautam Malviya Thakur
- Gerry Knapp
- Gordon Robertson
- James Gaboardi
- Jay Reynolds
- Jeff Brookins
- Jesse McGaha
- Josh Michener
- Jovid Rakhmonov
- Kevin Sparks
- Liangyu Qian
- Liz McBride
- Md Inzamam Ul Haque
- Nicholas Richter
- Olga S Ovchinnikova
- Peeyush Nandwana
- Peter Wang
- Rangasayee Kannan
- Roger G Miller
- Sarah Graham
- Serena Chen
- Sudarsanam Babu
- Sunyong Kwon
- Todd Thomas
- William Peter
- Xiuling Nie
- Ying Yang
- Yukinori Yamamoto

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

We tested 48 diverse homologs of SfaB and identified several enzyme variants that were more active than SfaB at synthesizing the nylon-6,6 monomer.

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 lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.

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

A high-strength, heat-resistant Al-Ce-Ni alloy optimized for additive manufacturing in industrial applications.