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

Generating energy models from scratch is a costly and time consuming task. A detailed energy model can have thousands of input parameters that are necessary for a complete simulation of a building. A solution to this problem is the AutoBEMGen framework.

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.

This invention introduces an innovative method for upcycling waste polyalkenamers, such as polybutadiene and acrylonitrile butadiene styrene, into high-performance materials through ring-opening metathesis polymerization (ROMP).

Technologies directed to a direct rotor current measurement for transformer-fed wound rotor machine are described.

Fabrication methods are needed that are easily scalable, will enable facile manufacturing of SSEs that are < 50 µm thick to attain high energy density, and also exhibit good stability at the interface of the anode. Specifically, Wu et al.