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Researcher
- Diana E Hun
- Ilias Belharouak
- Rama K Vasudevan
- Philip Boudreaux
- Sergei V Kalinin
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- Kevin M Roccapriore
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- Holly Humphrey
- Hongbin Sun
- James Szybist
- Jonathan Willocks
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- Khryslyn G Araño
- Kuma Sumathipala
- Liam Collins
- Logan Kearney
- Lu Yu
- Mahshid Ahmadi-Kalinina
- Mark M Root
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- Neus Domingo Marimon
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- Olga S Ovchinnikova
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- Sai Mani Prudhvi Valleti
- Shiwanka Vidarshi Wanasinghe Wanasinghe Mudiyanselage
- Singanallur Venkatakrishnan
- Stephen Jesse
- Stephen M Killough
- Sumner Harris
- Todd Toops
- Utkarsh Pratiush
- Yaocai Bai
- Zhenglai Shen
- Zhijia Du

Dual-GP addresses limitations in traditional GPBO-driven autonomous experimentation by incorporating an additional surrogate observer and allowing human oversight, this technique improves optimization efficiency via data quality assessment and adaptability to unanticipated exp

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.

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

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

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

Scanning transmission electron microscopes are useful for a variety of applications. Atomic defects in materials are critical for areas such as quantum photonics, magnetic storage, and catalysis.

The incorporation of low embodied carbon building materials in the enclosure is increasing the fuel load for fire, increasing the demand for fire/flame retardants.

A human-in-the-loop machine learning (hML) technology potentially enhances experimental workflows by integrating human expertise with AI automation.