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Researcher
- Ying Yang
- Alex Plotkowski
- Amit Shyam
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- Femi Omitaomu
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- Gautam Malviya Thakur
- Georgios Polyzos
- Gerry Knapp
- Haowen Xu
- Harper Jordan
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- Jiheon Jun
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- Liz McBride
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- Marie Romedenne
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- Patxi Fernandez-Zelaia
- Priyanshi Agrawal
- Rangasayee Kannan
- Samudra Dasgupta
- Shajjad Chowdhury
- Sheng Dai
- Sunyong Kwon
- Tim Graening Seibert
- Todd Thomas
- Tolga Aytug
- Tomas Grejtak
- Varisara Tansakul
- Weicheng Zhong
- Wei Tang
- Xiang Chen
- Xiuling Nie
- Yan-Ru Lin
- Yiyu Wang
- Yong Chae Lim
- Zhili Feng

The eDICEML digital twin is proposed which emulates networks and hosts of an instrument-computing ecosystem. It runs natively on an ecosystem’s host or as a portable virtual machine.

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

Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

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.

V-Cr-Ti alloys have been proposed as candidate structural materials in fusion reactor blanket concepts with operation temperatures greater than that for reduced activation ferritic martensitic steels (RAFMs).

A novel method that prevents detachment of an optical fiber from a metal/alloy tube and allows strain measurement up to higher temperatures, about 800 C has been developed. Standard commercial adhesives typically only survive up to about 400 C.

We developed and incorporated two innovative mPET/Cu and mPET/Al foils as current collectors in LIBs to enhance cell energy density under XFC conditions.

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.

The microreactor design addresses the need to understand molten salt-assisted electrochemical processes at a controlled scale, enabling real-time observation of structural changes and kinetics.