Quantitative Microstructural Analysis of State-of-the-art Lithium-ion Battery Cathodes
Using ZEISS ZEN Intellesis
Apps Development Specialist
Faculty of Mechanical Engineering
Application Consultant Light Microscopy
Dr. Timo Bernthaler
Materials Engineering and Microscopy Solutions
Prof. Dr. rer. Nat. Gerhard Schneider
Former material scientist and headmaster
Quantitative Microstructural Analysis of State-of-the-art Lithium-ion Battery Cathodes - Using ZEISS ZEN Intellesis
In this application note two different segmentation methods – classic thresholding and machine learning-based – are evaluated in the context of quantitative analysis of constituents of state-of-the-art lithium-ion batteries.
Both methods are compared against reference measurements using a laboratory balance. A detailed description of the fundamental functionality of the machine learning based approach is given. It has the potential to be more robust against image variations and thus can provide a more accurate segmentation result.