Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks.

ABSTRACT: The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal-noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-lo

SUBMITTER: Kong L 

PROVIDER: S-EPMC9789080 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature