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Researcher
- Ying Yang
- Alice Perrin
- Costas Tsouris
- Gs Jung
- Gyoung Gug Jang
- Radu Custelcean
- Steven J Zinkle
- Yanli Wang
- Yutai Kato
- Alexander I Wiechert
- Alex Plotkowski
- Amit Shyam
- Bogdan Dryzhakov
- Bruce A Pint
- Christopher Ledford
- Christopher Rouleau
- David S Parker
- Debangshu Mukherjee
- Gerry Knapp
- Ilia N Ivanov
- Ivan Vlassiouk
- James A Haynes
- Jong K Keum
- Kyle Kelley
- Md Inzamam Ul Haque
- Michael Kirka
- Mina Yoon
- Nicholas Richter
- Olga S Ovchinnikova
- Patxi Fernandez-Zelaia
- Ryan Dehoff
- Steven Randolph
- Sumit Bahl
- Sunyong Kwon
- Tim Graening Seibert
- Weicheng Zhong
- Wei Tang
- Xiang Chen
- Yan-Ru Lin

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.

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.

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).

High coercive fields prevalent in wurtzite ferroelectrics present a significant challenge, as they hinder efficient polarization switching, which is essential for microelectronic applications.

This technology is a laser-based heating unit that offers rapid heating profiles on a research scale with minimal incidental heating of materials processing environments.

High strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.

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.

A novel molecular sorbent system for low energy CO2 regeneration is developed by employing CO2-responsive molecules and salt in aqueous media where a precipitating CO2--salt fractal network is formed, resulting in solid-phase formation and sedimentation.

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