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Researcher
- Kyle Kelley
- Rama K Vasudevan
- Olga S Ovchinnikova
- Sergei V Kalinin
- Stephen Jesse
- An-Ping Li
- Andrew F May
- Andrew Lupini
- Anton Ievlev
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- Bogdan Dryzhakov
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- Christopher Hershey
- Craig Blue
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- Debangshu Mukherjee
- Hoyeon Jeon
- Hsin Wang
- Huixin (anna) Jiang
- James Klett
- Jamieson Brechtl
- Jewook Park
- John Lindahl
- Kai Li
- Kashif Nawaz
- Kevin M Roccapriore
- Liam Collins
- Marti Checa Nualart
- Maxim A Ziatdinov
- Md Inzamam Ul Haque
- Mike Zach
- Nedim Cinbiz
- Neus Domingo Marimon
- Ondrej Dyck
- Saban Hus
- Steven Randolph
- Tony Beard
- Yongtao Liu

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.

Distortion in scanning tunneling microscope (STM) images is an unavoidable problem. This technology is an algorithm to identify and correct distorted wavefronts in atomic resolution STM images.

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

Moisture management accounts for over 40% of the energy used by buildings. As such development of energy efficient and resilient dehumidification technologies are critical to decarbonize the building energy sector.

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

This technology provides a device, platform and method of fabrication of new atomically tailored materials. This “synthescope” is a scanning transmission electron microscope (STEM) transformed into an atomic-scale material manipulation platform.

This invention presents technologies for characterizing physical properties of a sample's surface by combining image processing with machine learning techniques.

This invention introduces a system for microscopy called pan-sharpening, enabling the generation of images with both full-spatial and full-spectral resolution without needing to capture the entire dataset, significantly reducing data acquisition time.

This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.