  FIB/SEM tomography represents an indispensable tool for the characterization
of three-dimensional nanostructures in battery research and many other fields.
However, contrast and 3D classification/reconstruction problems occur in many
cases, which strongly limits the applicability of the technique especially on
porous materials, like those used for electrode materials in batteries or fuel
cells. Distinguishing the different components like active Li storage particles
and carbon/binder materials is difficult and often prevents a reliable
quantitative analysis of image data, or may even lead to wrong conclusions
about structure-property relationships. In this contribution, we present a
novel approach for data classification in three-dimensional image data obtained
by FIB/SEM tomography and its applications to NMC battery electrode materials.
We use two different image signals, namely the signal of the angled SE2 chamber
detector and the Inlens detector signal, combine both signals and train a
random forest, i.e. a particular machine learning algorithm. We demonstrate
that this approach can overcome current limitations of existing techniques
suitable for multi-phase measurements and that it allows for quantitative data
reconstruction even where current state-of the art techniques fail, or demand
for large training sets. This approach may yield as guideline for future
research using FIB/SEM tomography.
