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Researcher
- Adam M Guss
- Josh Michener
- Liangyu Qian
- Ryan Dehoff
- Venkatakrishnan Singanallur Vaidyanathan
- Vincent Paquit
- Yong Chae Lim
- Amir K Ziabari
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- Kuntal De
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- Rangasayee Kannan
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- Xiaohan Yang
- Zhili Feng
- Adam Stevens
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- Joanna Tannous
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- Mark M Root
- Michael Kirka
- Obaid Rahman
- Paul Abraham
- Peeyush Nandwana
- Philip Boudreaux
- Priyanshi Agrawal
- Roger G Miller
- Sarah Graham
- Sudarsanam Babu
- Tomas Grejtak
- Vilmos Kertesz
- Wei Zhang
- William Peter
- Yang Liu
- Yiyu Wang
- Yukinori Yamamoto

ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.

Enzymes for synthesis of sequenced oligoamide triads and tetrads that can be polymerized into sequenced copolyamides.
Contact
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.

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.

A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.

By engineering the Serine Integrase Assisted Genome Engineering (SAGE) genetic toolkit in an industrial strain of Aspergillus niger, we have established its proof of principle for applicability in Eukaryotes.

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

We present a comprehensive muti-technique approach for systematic investigation of enzymes generated by wastewater Comamonas species with hitherto unknown functionality to wards the depolymerization of plastics into bioaccessible products for bacterial metabolism.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

We present the design, assembly and demonstration of functionality for a new custom integrated robotics-based automated soil sampling technology as part of a larger vision for future edge computing- and AI- enabled bioenergy field monitoring and management technologies called

Detection of gene expression in plants is critical for understanding the molecular basis of plant physiology and plant responses to drought, stress, climate change, microbes, insects and other factors.