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- Amir K Ziabari
- Ilias Belharouak
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- Aaron Werth
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- Shajjad Chowdhury
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- Stephen M Killough
- Tyler Smith
- Vilmos Kertesz
- Xianhui Zhao
- Yaocai Bai
- Yarom Polsky

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

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 technology identifies enzymatic routes to synthesize amide oligomers with defined sequence to improve polymerization of existing materials or enable polymerization of new materials. Polymers are generally composed of one (e.g. Nylon 6) or two (e.g.

Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches.

ORNL has developed a new hydrothermal synthesis route to generate high quality battery cathode precursors. The new route offers excellent compositional control, homogenous spherical morphologies, and an ammonia-free co-precipitation process.

This technology creates a light and metalless current collector for battery application. Cathodes coated on this new current collector demonstrated similar contact resistance, lower charge transfer resistance and similar or high rate performance.

Additively manufacturing of the windings with a conductor distributed in the cross-section according to the Hilbert curve provides many benefits as it allows for the reduction of the high-frequency losses due to the reduction of the effective winding conductor size.