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- Peeyush Nandwana
- Amit Shyam
- Blane Fillingim
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- Yanli Wang
- Ying Yang
- Yiyu Wang
- Yutai Kato

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.

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.

The first wall and blanket of a fusion energy reactor must maintain structural integrity and performance over long operational periods under neutron irradiation and minimize long-lived radioactive waste.

Real-time tracking and monitoring of radioactive/nuclear materials during transportation is a critical need to ensure safety and security. Current technologies rely on simple tagging, using sensors attached to transport containers, but they have limitations.

A high-strength, heat-resistant Al-Ce-Ni alloy optimized for additive manufacturing in industrial applications.

An ORNL team has developed a method for screening for an immunoregulatory protein, which includes assessing the sequence of a candidate protein to determine if it is an immunoregulatory protein when at least one plasminogen-apple-nematode (PAN) domain with a consensus sequence