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Researcher
- Ryan Dehoff
- Kyle Kelley
- Rama K Vasudevan
- Hongbin Sun
- Michael Kirka
- Sergei V Kalinin
- Vincent Paquit
- Adam Stevens
- Ahmed Hassen
- Alex Plotkowski
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- Andres Marquez Rossy
- Anton Ievlev
- Blane Fillingim
- Bogdan Dryzhakov
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- Clay Leach
- David Nuttall
- Ilias Belharouak
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- Kevin M Roccapriore
- Liam Collins
- Marti Checa Nualart
- Maxim A Ziatdinov
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Philip Bingham
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Rangasayee Kannan
- Roger G Miller
- Ruhul Amin
- Sarah Graham
- Singanallur Venkatakrishnan
- Stephen Jesse
- Steven Randolph
- Sudarsanam Babu
- Vipin Kumar
- Vishaldeep Sharma
- Vlastimil Kunc
- William Peter
- Yan-Ru Lin
- Ying Yang
- Yongtao Liu
- Yukinori Yamamoto

The invention presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.

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.

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

Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.

In manufacturing parts for industry using traditional molds and dies, about 70 percent to 80 percent of the time it takes to create a part is a result of a relatively slow cooling process.

This technology combines 3D printing and compression molding to produce high-strength, low-porosity composite articles.

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

Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.