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Researcher
- Ilias Belharouak
- Jaswinder Sharma
- Singanallur Venkatakrishnan
- Alexey Serov
- Ali Abouimrane
- Amir K Ziabari
- Beth L Armstrong
- Diana E Hun
- Georgios Polyzos
- Marm Dixit
- Philip Bingham
- Philip Boudreaux
- Ruhul Amin
- Ryan Dehoff
- Sergiy Kalnaus
- Stephen M Killough
- Vincent Paquit
- Xiang Lyu
- Amit K Naskar
- Ben LaRiviere
- Bryan Maldonado Puente
- Corey Cooke
- David L Wood III
- Gabriel Veith
- Gina Accawi
- Gurneesh Jatana
- Holly Humphrey
- Hongbin Sun
- James Szybist
- Jonathan Willocks
- Junbin Choi
- Khryslyn G Araño
- Logan Kearney
- Lu Yu
- Mark M Root
- Meghan Lamm
- Michael Kirka
- Michael Toomey
- Michelle Lehmann
- Nance Ericson
- Nancy Dudney
- Nihal Kanbargi
- Nolan Hayes
- Obaid Rahman
- Paul Groth
- Peter Wang
- Pradeep Ramuhalli
- Ritu Sahore
- Ryan Kerekes
- Sally Ghanem
- Todd Toops
- Yaocai Bai
- Zhijia Du

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.

An electrochemical cell has been specifically designed to maximize CO2 release from the seawater while also not changing the pH of the seawater before returning to the sea.

We developed and incorporated two innovative mPET/Cu and mPET/Al foils as current collectors in LIBs to enhance cell energy density under XFC conditions.

The ORNL invention addresses the challenge of poor mechanical properties of dry processed electrodes, improves their electrical properties, while improving their electrochemical performance.

Hydrogen is in great demand, but production relies heavily on hydrocarbons utilization. This process contributes greenhouse gases release into the atmosphere.

The co-processing of cathode and composite electrolyte for solid state polymer batteries has been developed. A traditional uncalendared cathode of e.g.

ORNL has developed a new hybrid membrane to improve electrochemical stability in next-generation sodium metal anodes.

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