Research Topics

nD STEM (multi-dimensional Scanning Transmission Electron Microscopy) is an advanced technique that builds upon the capabilities of traditional STEM by integrating multiple modalities of electron microscopy and expanding on 4D STEM. It captures and analyzes multi-dimensional datasets, allowing for a more comprehensive understanding of a material's structure, composition, and properties. By leveraging tools like direct electron detectors, nD-STEM provides unprecedented detail and insights, making it a powerful method for exploring complex materials. This innovation is beneficial in fields where understanding the intricate relationships between different data types is crucial.

MINDSHIFT (Multimodal Integration for Nanoparticle Data Screening using High-throughput Frameworks in Transmission electron microscopy) is a cutting-edge research project at Northwestern University. It aims to revolutionize materials characterization using advanced AI and machine learning techniques.

MINDSHIFT has the potential to transform materials characterization, enabling more efficient and precise analysis of complex nanoparticle systems. This could lead to breakthroughs in various applications of nanotechnology across multiple industries.

Quantum algorithms for electron microscopy (EM) analysis represent a cutting-edge approach to handling the vast and complex datasets generated by modern EM techniques. By leveraging quantum computing's unique capabilities, these algorithms can optimize data processing, enhance pattern recognition, and accelerate the extraction of critical insights from EM images. This enables researchers to tackle computationally intensive problems for classical methods, opening up new possibilities for understanding materials at unprecedented scales and speeds. Integrating quantum algorithms into EM analysis pushes the boundaries of what's possible in materials science and microscopy.