AI-powered X-ray Microscope Reconstruction Technologies for Gamechanging Capabilities in Battery and Fuel Cell Research

About this webinar

In order to power a growing society, today’s materials scientists drive materials development in battery and fuel cell research. Increasingly, researchers leverage advanced computer simulations to understand fundamental mechanisms, analyze performance attributes, and develop materials or device architectures using “digital twin” approaches that allow for rapid iteration of material properties, arrangements, and device operation parameters without having to physically produce each variation. This approach, while powerful, requires a detailed representation of real materials and devices that both resolves the relevant features and spans across the meaningful length scales in which these devices operate. 3D microscopy techniques such as X-ray microscopy can provide these inputs but, like most microscopy approaches, suffer from the tradeoff between maintaining the needed image resolution and capturing a representative field of view.

In this webinar you will learn how 3D X-ray microscopy coupled with AI-based reconstruction technologies can help to overcome these challenges in imaging and simulation of battery and fuel cell processes by producing 3D images over device-relevant volumes (e.g., many millimeters) while simultaneously capturing features with sub-micrometer detail and help power tomorrow’s energy research breakthroughs.