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Researcher
- Amit Shyam
- Alex Plotkowski
- James A Haynes
- Ryan Dehoff
- Sumit Bahl
- Adam Stevens
- Alexander I Wiechert
- Alice Perrin
- Andres Marquez Rossy
- Brian Post
- Christopher Fancher
- Christopher Hobbs
- Costas Tsouris
- Dean T Pierce
- Debangshu Mukherjee
- Eddie Lopez Honorato
- Gerry Knapp
- Gordon Robertson
- Gs Jung
- Gyoung Gug Jang
- Jay Reynolds
- Jeff Brookins
- Jovid Rakhmonov
- Matt Kurley III
- Md Inzamam Ul Haque
- Nicholas Richter
- Olga S Ovchinnikova
- Peeyush Nandwana
- Peter Wang
- Radu Custelcean
- Rangasayee Kannan
- Rodney D Hunt
- Roger G Miller
- Ryan Heldt
- Sarah Graham
- Sudarsanam Babu
- Sunyong Kwon
- Tyler Gerczak
- William Peter
- Ying Yang
- Yukinori Yamamoto

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.

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 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.

Sintering additives to improve densification and microstructure control of UN provides a facile approach to producing high quality nuclear fuels.

The use of Fluidized Bed Chemical Vapor Deposition to coat particles or fibers is inherently slow and capital intensive, as it requires constant modifications to the equipment to account for changes in the characteristics of the substrates to be coated.

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