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Researcher
- Blane Fillingim
- Brian Post
- Hongbin Sun
- Lauren Heinrich
- Peeyush Nandwana
- Sudarsanam Babu
- Thomas Feldhausen
- Yousub Lee
- Alexander I Wiechert
- Alex Roschli
- Costas Tsouris
- Debangshu Mukherjee
- Erin Webb
- Evin Carter
- Gs Jung
- Gyoung Gug Jang
- Ilias Belharouak
- Jeremy Malmstead
- Kitty K Mccracken
- Md Inzamam Ul Haque
- Olga S Ovchinnikova
- Oluwafemi Oyedeji
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Radu Custelcean
- Ramanan Sankaran
- Ruhul Amin
- Soydan Ozcan
- Tyler Smith
- Vimal Ramanuj
- Vishaldeep Sharma
- Wenjun Ge
- Xianhui Zhao

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.

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 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.

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.

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.

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.

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