  Band gap variations in thin film structures, across grain boundaries, and in
embedded nanoparticles are of increasing interest in the materials science
community. As many common experimental techniques for measuring band gaps do
not have the spatial resolution needed to observe these variations directly,
probe-corrected Scanning Transmission Electron Microscope (STEM) with
monochromated Electron Energy-Loss Spectroscopy (EELS) is a promising method
for studying band gaps of such features. However, extraction of band gaps from
EELS data sets usually requires heavy user involvement, and makes the analysis
of large data sets challenging. Here we develop and present methods for
automated extraction of band gap maps from large STEM-EELS data sets with high
spatial resolution while preserving high accuracy and precision.
