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Researcher
- Blane Fillingim
- Brian Post
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- Peeyush Nandwana
- Sudarsanam Babu
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- Yousub Lee
- Alexander I Wiechert
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- Debangshu Mukherjee
- Debjani Pal
- Gs Jung
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- Kuntal De
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- Tim Graening Seibert
- Tolga Aytug
- Vimal Ramanuj
- Weicheng Zhong
- Wei Tang
- Wenjun Ge
- Xiang Chen
- Yanli Wang
- Ying Yang
- Yutai Kato

Ruthenium is recovered from used nuclear fuel in an oxidizing environment by depositing the volatile RuO4 species onto a polymeric substrate.

Among the methods for point source carbon capture, the absorption of CO2 using aqueous amines (namely MEA) from the post-combustion gas stream is currently considered the most promising.

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.

New demands in electric vehicles have resulted in design changes for the power electronic components such as the capacitor to incur lower volume, higher operating temperatures, and dielectric properties (high dielectric permittivity and high electrical breakdown strengths).

Ceramic matrix composites are used in several industries, such as aerospace, for lightweight, high quality and high strength materials. But producing them is time consuming and often low quality.

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.

Spherical powders applied to nuclear targetry for isotope production will allow for enhanced heat transfer properties, tailored thermal conductivity and minimize time required for target fabrication and post processing.

Biocompatible nanoparticles have been developed that can trap and retain therapeutic radionuclides and their byproducts at the cancer site. This is important to maximize the therapeutic effect of this treatment and minimize associated side effects.

This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.