About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Nanotechnology for Energy, Environment, Electronics, Healthcare and Industry
|
| Presentation Title |
Novel sweet spot techniques in imaging and analyzing battery materials in the FE-SEM enhanced by AI-based particle characterization |
| Author(s) |
Andy Holwell, Ria Mitchell |
| On-Site Speaker (Planned) |
Andy Holwell |
| Abstract Scope |
Cathode materials are a varied class of multi-component materials of differing morphology and chemistry, that can be challenging to image in electron microscopy and are prone to electron damage, hence often imaging poorly. Imaging quality is extremely sensitive to accelerating voltage, working distance, detector choice and other parameters. This work examines our ability to find “sweet spots” in imaging and EDS conditions for optimal insight for cathode foils, as well as anode and separator materials. We determine optimal combinations, resulting from the electron optics, reveal differing morphology and composition.
We present novel techniques for imaging battery materials, and show images that provide new insight and understanding. We explain how AI-based advances in image analysis, specifically instance segmentation and machine learning algorithms, have enabled the use of these images for particle analysis including particle size distribution, even from complex images containing multiple materials and features. |