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Researcher
- Soydan Ozcan
- Xianhui Zhao
- Alex Roschli
- Dali Wang
- Dave Willis
- Debangshu Mukherjee
- Erin Webb
- Evin Carter
- Halil Tekinalp
- Jeremy Malmstead
- Jian Chen
- Josh Michener
- Kitty K Mccracken
- Liangyu Qian
- Luke Chapman
- Md Inzamam Ul Haque
- Mengdawn Cheng
- Olga S Ovchinnikova
- Oluwafemi Oyedeji
- Paula Cable-Dunlap
- Sanjita Wasti
- Serena Chen
- Sydney Murray III
- Tyler Smith
- Vasilis Tzoganis
- Vasiliy Morozov
- Wei Zhang
- Yun Liu
- Zhili Feng

We have developed a novel extrusion-based 3D printing technique that can achieve a resolution of 0.51 mm layer thickness, and catalyst loading of 44% and 90.5% before and after drying, respectively.

We tested 48 diverse homologs of SfaB and identified several enzyme variants that were more active than SfaB at synthesizing the nylon-6,6 monomer.

We presented a novel apparatus and method for laser beam position detection and pointing stabilization using analog position-sensitive diodes (PSDs).

This invention is directed to a machine leaning methodology to quantify the association of a set of input variables to a set of output variables, specifically for the one-to-many scenarios in which the output exhibits a range of variations under the same replicated input condi

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 and ultra-high vacuum applications require seals that do not allow leaks. O-rings can break down over time, due to aging and exposure to radiation. Metallic seals can damage sealing surfaces, making replacement of the original seal very difficult.

We have developed an aerosol sampling technique to enable collection of trace materials such as actinides in the atmosphere.

The technology describes an electron beam in a storage ring as a quantum computer.

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