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Researcher
- Andrzej Nycz
- Beth L Armstrong
- Chris Masuo
- Peter Wang
- Alex Walters
- Amit Shyam
- Jun Qu
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- Corson Cramer
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- Hsin Wang
- J.R. R Matheson
- Jaydeep Karandikar
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- Jeff Brookins
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- Jennifer M Pyles
- Jesse Heineman
- John Lindahl
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- Jordan Wright
- Jovid Rakhmonov
- Justin Griswold
- Khryslyn G Araño
- Kuntal De
- Laetitia H Delmau
- Luke Sadergaski
- Marm Dixit
- Matthew S Chambers
- Michael Kirka
- Nancy Dudney
- Nedim Cinbiz
- Nicholas Richter
- Padhraic L Mulligan
- Peeyush Nandwana
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- Riley Wallace
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- Shajjad Chowdhury
- Sunyong Kwon
- Tolga Aytug
- Tony Beard
- Trevor Aguirre
- Vincent Paquit
- Vladimir Orlyanchik
- Xiaohan Yang
- Ying Yang
- Yiyu Wang

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.

System and method for part porosity monitoring of additively manufactured components using machining
In additive manufacturing, choice of process parameters for a given material and geometry can result in porosities in the build volume, which can result in scrap.

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.

We present the design, assembly and demonstration of functionality for a new custom integrated robotics-based automated soil sampling technology as part of a larger vision for future edge computing- and AI- enabled bioenergy field monitoring and management technologies called

Creating a framework (method) for bots (agents) to autonomously, in real time, dynamically divide and execute a complex manufacturing (or any suitable) task in a collaborative, parallel-sequential way without required human interaction.

Materials produced via additive manufacturing, or 3D printing, can experience significant residual stress, distortion and cracking, negatively impacting the manufacturing process.

Using all polymer formulations, the PIP densification is improved almost 70% over traditional preceramic polymers and PIP material leading to cost and times saving for densifying ceramic composites made from powder or fibers.