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Researcher
- Peeyush Nandwana
- Amit Shyam
- Alex Plotkowski
- Blane Fillingim
- Brian Post
- James A Haynes
- Lauren Heinrich
- Rangasayee Kannan
- Sudarsanam Babu
- Sumit Bahl
- Thomas Feldhausen
- Ying Yang
- Yousub Lee
- Alice Perrin
- Andres Marquez Rossy
- Bruce A Pint
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- Christopher Fancher
- Debangshu Mukherjee
- Gerry Knapp
- Gordon Robertson
- Jay Reynolds
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- Jovid Rakhmonov
- Liangyu Qian
- Md Inzamam Ul Haque
- Nicholas Richter
- Olga S Ovchinnikova
- Peter Wang
- Ryan Dehoff
- Serena Chen
- Steven J Zinkle
- Sunyong Kwon
- Tim Graening Seibert
- Tomas Grejtak
- Weicheng Zhong
- Wei Tang
- Xiang Chen
- Yanli Wang
- Yiyu Wang
- Yutai Kato

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.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

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.

The first wall and blanket of a fusion energy reactor must maintain structural integrity and performance over long operational periods under neutron irradiation and minimize long-lived radioactive waste.

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