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Researcher
- Ali Passian
- Kyle Kelley
- Rama K Vasudevan
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Diana E Hun
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- Philip Boudreaux
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- Neus Domingo Marimon
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- Peter Wang
- Ryan Kerekes
- Sally Ghanem
- Srikanth Yoginath
- Stephen Jesse
- Steven Randolph
- Varisara Tansakul
- Yongtao Liu

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

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

The invention introduces a novel, customizable method to create, manipulate, and erase polar topological structures in ferroelectric materials using atomic force microscopy.

High coercive fields prevalent in wurtzite ferroelectrics present a significant challenge, as they hinder efficient polarization switching, which is essential for microelectronic applications.

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.

Technologies directed quantum spectroscopy and imaging with Raman and surface-enhanced Raman scattering are described.

Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).