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Researcher
- Amit Shyam
- Alex Plotkowski
- Srikanth Yoginath
- Gurneesh Jatana
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- Mark M Root
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- Melanie Moses-DeBusk Debusk
- Nance Ericson
- Nicholas Richter
- Pablo Moriano Salazar
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- Philip Boudreaux
- Roger G Miller
- Sarah Graham
- Singanallur Venkatakrishnan
- Sreshtha Sinha Majumdar
- Sudarsanam Babu
- Sunyong Kwon
- Tomas Grejtak
- Vandana Rallabandi
- Varisara Tansakul
- William Peter
- William P Partridge Jr
- Xiang Lyu
- Ying Yang
- Yiyu Wang
- Yukinori Yamamoto

High-gradient magnetic filtration (HGMF) is a non-destructive separation technique that captures magnetic constituents from a matrix containing other non-magnetic species. One characteristic that actinide metals share across much of the group is that they are magnetic.

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.

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

The invention discloses methods of using a reducing agent for catalytic oxygen reduction from CO2 streams, enabling the treated CO2 streams to meet the pipeline specifications.

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.

An electrochemical cell has been specifically designed to maximize CO2 release from the seawater while also not changing the pH of the seawater before returning to the sea.

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.

Digital twins (DTs) have emerged as essential tools for monitoring, predicting, and optimizing physical systems by using real-time data.