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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.

Mechanism-Based Trait Inference in Plants Using Multiplex Networks, AI Agents, and Translation Tools
This system enables the modular design and optimization of complex plant traits by organizing genes and regulatory mechanisms into interpretable clades.

Mechanism-Based Biological Inference via Multiplex Networks, AI Agents and Cross-Species Translation
This invention provides a platform that uses AI agents and biological networks to uncover and interpret disease-relevant biological mechanisms.

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.

This invention utilizes a custom-synthesized vinyl trifluoromethanesulfonimide (VTFSI) salt and an alcohol containing small molecule or polymer for the synthesis of novel single-ion conducting polymer electrolytes for the use in Li-ion and beyond Li-ion batteries, fuel cells,

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.

PET is used in many commercial products, but only a fraction is mechanically recycled, and even less is chemically recycled.

Developed a novel energy efficient, cost-effective, environmentally friendly process for separation of lithium from end-of-life lithium-ion batteries.

This work presents a novel method for upcycling polyethylene terephthalate (PET) waste into sustainable vitrimer materials. By combining bio-based crosslinkers with our PET-based macromonomer, we developed dynamically bonded plastics that are renewably sourced.