To meet the rapidly evolving energy needs of a growing population, energy storage and conversion devices such as batteries must rapidly evolve as well. Battery materials and other functional energy materials derive their properties not from their static state, but from how they interact with other materials in their service environment. Furthermore, the microstructure of these materials strongly dictates their performance and how they interact with other components (like electrolytes, binders, etc). In order to accelerate this development cycle, researchers are increasingly integrating computational modeling into their materials and device development efforts.

Computational analysis is progressing to the point where simulations can incorporate real material microstructures, simulate statistically equivalent structures, and predict performance changes based on changes to these microstructures. At the same time experimental techniques have emerged that can deliver representative volumes of high-quality, high-resolution 3D microstructural images from real devices and materials. The interplay between advanced computational analysis and evolving characterization capabilities means we are rapidly approaching the realization of the “materials by design” paradigm where new materials are designed and tested digitally with input and validation provided by advanced materials characterization techniques. Within this paradigm, however, it is critical that the experimental techniques provide accurate, realistic, and representative input to the computational models. In the case of microstructural information, this means generating 3-dimensional images that accurately represents the materials in question.

One key example of this is for materials, like battery materials and electrodes, which contain micro- and nano-scale porosity. Acquiring image data that can reproduce real material properties and feed accurate simulations is challenging and relies on satisfying several conditions. For example, the volume must be representative of the “bulk” material microstructure. The (2D) image resolution must capture the relevant scope of material features, and the 3D image must accurately represent the material in all 3 dimensions and at appropriate length scales. The focused-ion beam scanning electron microscope (FIB-SEM) is capable of imaging these micro- and nano-scale pore networks in 3D but care must be taken when considering these experiments, especially when the intent is to use the images to feed computational models.

We present here a review of the factors researchers must consider when tackling these demanding tasks and highlight experimental recommendations for leveraging FIB-SEM tomography to produce the most relevant imaging data in this context. We use a lithium-ion battery cathode as a test case to demonstrate this approach, but the outcome and methods can be leveraged across the materials research space and beyond.