# Automated approaches for band gap mapping in STEM-EELS

## TL;DR

This paper introduces automated methods for high-resolution, accurate band gap mapping in STEM-EELS data, addressing the challenge of analyzing large datasets with minimal user intervention.

## Contribution

The authors develop automated techniques for extracting band gap maps from STEM-EELS data, improving efficiency and accuracy over manual analysis methods.

## Key findings

- Automated methods enable high-resolution band gap mapping.
- The approach reduces user involvement in data analysis.
- High accuracy and precision are maintained in large datasets.

## Abstract

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.