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Researcher
- Amit Shyam
- Beth L Armstrong
- Peeyush Nandwana
- Alex Plotkowski
- Brian Post
- Jun Qu
- Rangasayee Kannan
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- Yong Chae Lim
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- Debjani Pal
- Debraj De
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- Govindarajan Muralidharan
- Hongbin Sun
- Hsin Wang
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- Jesse McGaha
- Jian Chen
- Jiheon Jun
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- Jordan Wright
- Jovid Rakhmonov
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- Khryslyn G Araño
- Kuntal De
- Laetitia H Delmau
- Liz McBride
- Luke Sadergaski
- Marm Dixit
- Matthew S Chambers
- Michael Kirka
- Nancy Dudney
- Nate See
- Nedim Cinbiz
- Nicholas Richter
- Padhraic L Mulligan
- Peter Wang
- Prashant Jain
- Priyanshi Agrawal
- Roger G Miller
- Rose Montgomery
- Sandra Davern
- Sarah Graham
- Sergiy Kalnaus
- Shajjad Chowdhury
- Steven J Zinkle
- Sunyong Kwon
- Thien D. Nguyen
- Thomas R Muth
- Tim Graening Seibert
- Todd Thomas
- Tolga Aytug
- Tony Beard
- Trevor Aguirre
- Venugopal K Varma
- Weicheng Zhong
- Wei Tang
- Wei Zhang
- William Peter
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- Xiuling Nie
- Yanli Wang
- Yiyu Wang
- Yukinori Yamamoto
- Yutai Kato

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.

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

A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.

Ruthenium is recovered from used nuclear fuel in an oxidizing environment by depositing the volatile RuO4 species onto a polymeric substrate.

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.

A novel approach is presented herein to improve time to onset of natural convection stemming from fuel element porosity during a failure mode of a nuclear reactor.