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Researcher
- Ryan Dehoff
- Kyle Kelley
- Rama K Vasudevan
- Michael Kirka
- Sergei V Kalinin
- Vincent Paquit
- Adam Stevens
- Ahmed Hassen
- Alex Plotkowski
- Alex Roschli
- Alice Perrin
- Amir K Ziabari
- Amit Shyam
- Andres Marquez Rossy
- Anton Ievlev
- Blane Fillingim
- Bogdan Dryzhakov
- Brian Post
- Christopher Ledford
- Clay Leach
- David Nuttall
- Erin Webb
- Evin Carter
- James Haley
- Jeremy Malmstead
- Kevin M Roccapriore
- Kitty K Mccracken
- Liam Collins
- Marti Checa Nualart
- Maxim A Ziatdinov
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Oluwafemi Oyedeji
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Philip Bingham
- Rangasayee Kannan
- Roger G Miller
- Sarah Graham
- Singanallur Venkatakrishnan
- Soydan Ozcan
- Stephen Jesse
- Steven Randolph
- Sudarsanam Babu
- Tyler Smith
- Vipin Kumar
- Vlastimil Kunc
- William Peter
- Xianhui Zhao
- Yan-Ru Lin
- Ying Yang
- Yongtao Liu
- Yukinori Yamamoto

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

The use of biomass fiber reinforcement for polymer composite applications, like those in buildings or automotive, has expanded rapidly due to the low cost, high stiffness, and inherent renewability of these materials. Biomass are commonly disposed of as waste.

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.

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.

This invention introduces a system for microscopy called pan-sharpening, enabling the generation of images with both full-spatial and full-spectral resolution without needing to capture the entire dataset, significantly reducing data acquisition time.