Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy.

ABSTRACT: Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner. One of the persisting limitations of EELS is the requirement for manual identification of core-loss edges and their corresponding elements. This can be especially bothersome in spectrum imaging, where a large amount of spectra are recorded when spatially scanning over a sample area. This paper introduces a synthetic dataset with 736,000 labeled EELS spectra, computed from available generalized oscillator strength tables, that represents 107 K, L, M or N core-loss edges and 80 chemical elements. Generic lifetime broadened peaks are used to mimic the fine structure due to band structure effects present in experim

SUBMITTER: Annys A 

PROVIDER: S-EPMC10444881 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature