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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Kyle Kelley
- Maxim A Ziatdinov
- Olga S Ovchinnikova
- Kashif Nawaz
- Stephen Jesse
- An-Ping Li
- Andrew Lupini
- Anton Ievlev
- Arpan Biswas
- Bogdan Dryzhakov
- Brian Fricke
- Bruce Moyer
- Christopher Rouleau
- Costas Tsouris
- Debangshu Mukherjee
- Debjani Pal
- Gerd Duscher
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- Jamieson Brechtl
- Jeffrey Einkauf
- Jennifer M Pyles
- Jewook Park
- Jong K Keum
- Justin Griswold
- Kai Li
- Kuntal De
- Kyle Gluesenkamp
- Laetitia H Delmau
- Liam Collins
- Luke Sadergaski
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Md Inzamam Ul Haque
- Mike Zach
- Mina Yoon
- Neus Domingo Marimon
- Nickolay Lavrik
- Ondrej Dyck
- Padhraic L Mulligan
- Radu Custelcean
- Saban Hus
- Sai Mani Prudhvi Valleti
- Sandra Davern
- Steven Randolph
- Sumner Harris
- Utkarsh Pratiush
- Zhiming Gao

Spherical powders applied to nuclear targetry for isotope production will allow for enhanced heat transfer properties, tailored thermal conductivity and minimize time required for target fabrication and post processing.

Biocompatible nanoparticles have been developed that can trap and retain therapeutic radionuclides and their byproducts at the cancer site. This is important to maximize the therapeutic effect of this treatment and minimize associated side effects.

A novel molecular sorbent system for low energy CO2 regeneration is developed by employing CO2-responsive molecules and salt in aqueous media where a precipitating CO2--salt fractal network is formed, resulting in solid-phase formation and sedimentation.

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.

In scientific research and industrial applications, selecting the most accurate model to describe a relationship between input parameters and target characteristics of experiments is crucial.

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.