Microscopy Solutions for Materials R&D

New, stronger, tough, durable, lightweight, and fracture-resistant materials open doors to new technologies. Designing and developing engineering materials for tomorrow’s innovation challenges demands an ever-better understanding of complex processes, structures, and properties.

Platinum has been widely used for catalytic chemical reactions in automobile, chemical refining, and energy industries. This application note discusses imaging techniques that accurately determine particle size and morphology, a method that can improve the catalytic analysis of platinum loading, size, dispersion, and active site determination.

Non-metallic inclusions (NMI) are present in all grades of steels and related alloys. NMI that do not fall into standard categories can be located by light microscopy combined with scanning electron microscopy (SEM) energy-dispersive X-ray spectroscopy (EDS) mapping. This allows full characterization of all inclusions in the steel to develop improved products and processes.

Routine inspection and quality control tasks can be achieved rapidly and accurately using light and electron microscopy in industrially-focused workflows. Now, multi-modal microscopy is capable of providing a vast array of analytical capabilities.

Single-phase polycrystalline materials in steel, alloys, and ceramics do not exhibit absorption contrast to reveal underlying grain microstructure. Now, advanced laboratory-based diffraction contrast tomography enables 3D characterization capabilities of these challenging structures.

To support the development of new batteries, electrode designs, and energy storage materials for the automotive industry, portable batteries, the renewable energy market, and the electronics industry calls for analytical methods that display various morphological and chemical properties.

Segmentation is the division of images into defined regions for subsequent categorization and analysis. By analyzing these regions, and the borders between them, we can obtain quantitative, actionable information. These analyses depend on the accuracy and reliability of segmentation results.

In this application note, two different segmentation methods are evaluated in the context of quantitative analysis of components of Li-ion batteries, including machine learning and thresholding.