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Researcher
- Amit Shyam
- Alex Plotkowski
- Kyle Kelley
- Rama K Vasudevan
- James A Haynes
- Olga S Ovchinnikova
- Ryan Dehoff
- Sergei V Kalinin
- Sumit Bahl
- Adam Stevens
- Alexander I Wiechert
- Alice Perrin
- Andres Marquez Rossy
- Anton Ievlev
- Bogdan Dryzhakov
- Brian Post
- Christopher Fancher
- Costas Tsouris
- Dean T Pierce
- Debangshu Mukherjee
- Gerry Knapp
- Gordon Robertson
- Gs Jung
- Gyoung Gug Jang
- Jay Reynolds
- Jeff Brookins
- Jovid Rakhmonov
- Kevin M Roccapriore
- Liam Collins
- Marti Checa Nualart
- Maxim A Ziatdinov
- Md Inzamam Ul Haque
- Neus Domingo Marimon
- Nicholas Richter
- Peeyush Nandwana
- Peter Wang
- Radu Custelcean
- Rangasayee Kannan
- Roger G Miller
- Sarah Graham
- Stephen Jesse
- Steven Randolph
- Sudarsanam Babu
- Sunyong Kwon
- William Peter
- Ying Yang
- Yongtao Liu
- Yukinori Yamamoto

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.

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.

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.

The invention introduces a novel, customizable method to create, manipulate, and erase polar topological structures in ferroelectric materials using atomic force microscopy.

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

This invention presents technologies for characterizing physical properties of a sample's surface by combining image processing with machine learning techniques.