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Researcher
- Ali Passian
- 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
- Christopher Rouleau
- Claire Marvinney
- Costas Tsouris
- Debangshu Mukherjee
- Gerd Duscher
- Gs Jung
- Gyoung Gug Jang
- Harper Jordan
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- Jamieson Brechtl
- Jewook Park
- Joel Asiamah
- Joel Dawson
- Jong K Keum
- Kai Li
- Kyle Gluesenkamp
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Md Inzamam Ul Haque
- Mina Yoon
- Nance Ericson
- Neus Domingo Marimon
- Nickolay Lavrik
- Ondrej Dyck
- Radu Custelcean
- Saban Hus
- Sai Mani Prudhvi Valleti
- Srikanth Yoginath
- Steven Randolph
- Sumner Harris
- Utkarsh Pratiush
- Varisara Tansakul
- Zhiming Gao

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.

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.

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.

Technologies directed quantum spectroscopy and imaging with Raman and surface-enhanced Raman scattering are described.

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.