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Researcher
- Peeyush Nandwana
- Anees Alnajjar
- Alexey Serov
- Amit Shyam
- Blane Fillingim
- Brian Post
- Jaswinder Sharma
- Lauren Heinrich
- Nageswara Rao
- Rangasayee Kannan
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- Thomas Feldhausen
- Xiang Lyu
- Yousub Lee
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- Andres Marquez Rossy
- Beth L Armstrong
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- Khryslyn G Araño
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- Mariam Kiran
- Marm Dixit
- Meghan Lamm
- Michael Toomey
- Michelle Lehmann
- Nihal Kanbargi
- Peter Wang
- Ritu Sahore
- Ryan Dehoff
- Sheng Dai
- Steven J Zinkle
- Tim Graening Seibert
- Todd Toops
- Tomas Grejtak
- Weicheng Zhong
- Wei Tang
- Xiang Chen
- Yanli Wang
- Ying Yang
- Yiyu Wang
- Yutai Kato

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.

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

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.

The ORNL invention addresses the challenge of poor mechanical properties of dry processed electrodes, improves their electrical properties, while improving their electrochemical performance.

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.

Hydrogen is in great demand, but production relies heavily on hydrocarbons utilization. This process contributes greenhouse gases release into the atmosphere.

Electrochemistry synthesis and characterization testing typically occurs manually at a research facility.

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.