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Researcher
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Diana E Hun
- Hongbin Sun
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Vincent Paquit
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- Charlie Cook
- Christopher Hershey
- Corey Cooke
- Craig Blue
- Daniel Rasmussen
- Gina Accawi
- Gurneesh Jatana
- Hsin Wang
- Ilias Belharouak
- James Klett
- John Lindahl
- Mark M Root
- Michael Kirka
- Mike Zach
- Nedim Cinbiz
- Nolan Hayes
- Obaid Rahman
- Peter Wang
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Ruhul Amin
- Ryan Kerekes
- Sally Ghanem
- Tony Beard
- Vishaldeep Sharma

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

The invention presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.

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

The technologies provide a system and method of needling of veiled AS4 fabric tape.

ORNL will develop an advanced high-performing RTG using a novel radioisotope heat source.

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

Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.

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).

Technologies for optimizing prefab retrofit panel installation using a real-time evaluator is described.