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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Alexey Serov
- Jaswinder Sharma
- Kyle Kelley
- Vincent Paquit
- Xiang Lyu
- Akash Jag Prasad
- Amit K Naskar
- Anton Ievlev
- Arpan Biswas
- Beth L Armstrong
- Calen Kimmell
- Canhai Lai
- Chris Tyler
- Clay Leach
- Costas Tsouris
- Gabriel Veith
- Georgios Polyzos
- Gerd Duscher
- Holly Humphrey
- James Haley
- James Parks II
- James Szybist
- Jaydeep Karandikar
- Jonathan Willocks
- Junbin Choi
- Khryslyn G Araño
- Liam Collins
- Logan Kearney
- Mahshid Ahmadi-Kalinina
- Marm Dixit
- Marti Checa Nualart
- Meghan Lamm
- Michael Toomey
- Michelle Lehmann
- Neus Domingo Marimon
- Nihal Kanbargi
- Olga S Ovchinnikova
- Ritu Sahore
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Stephen Jesse
- Sumner Harris
- Todd Toops
- Utkarsh Pratiush
- Vladimir Orlyanchik
- Zackary Snow

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

System and method for part porosity monitoring of additively manufactured components using machining
In additive manufacturing, choice of process parameters for a given material and geometry can result in porosities in the build volume, which can result in scrap.

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.

Sensing of additive manufacturing processes promises to facilitate detailed quality inspection at scales that have seldom been seen in traditional manufacturing processes.

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

The scanning transmission electron microscope (STEM) provides unprecedented spatial resolution and is critical for many applications, primarily for imaging matter at the atomic and nanoscales and obtaining spectroscopic information at similar length scales.