1 - 10 of 18 Results
ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
Efficient thermal management in polymers is essential for developing lightweight, high-strength materials with multifunctional capabilities.
The disclosure is directed to optimized fiber geometries for use in carbon fiber reinforced polymers with increased compressive strength per unit cost. The disclosed fiber geometries reduce the material processing costs as well as increase the compressive strength.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
A novel and cost-effective process for the activation of carbon fibers was established.
Contact
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.
ORNL has developed a large area thermal neutron detector based on 6LiF/ZnS(Ag) scintillator coupled with wavelength shifting fibers. The detector uses resistive charge divider-based position encoding.
ORNL contributes to developing the concept of passive CO2 DAC by designing and testing a hybrid sorption system. This design aims to leverage the advantages of CO2 solubility and selectivity offered by materials with selective sorption of adsorbents.
This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.