@misc{10498/33841,
year = {2024},
url = {http://hdl.handle.net/10498/33841},
abstract = {At present, transmission electron microscopy is regarded as the main option when dealing with phase characterization for materials at a nanometric scale. The development and improvement of complementary techniques such as energy-dispersive X-ray spectroscopy (EDS), electron energy loss spectroscopy (EELS), imaging detectors and associated computational methods provide a huge variety of choices to determine the composition and crystal structure at any region of a specimen. Despite all these advancements, phase identification for some specific materials having phases with similar structure or similar composition might still be a difficult procedure to perform. To overcome this difficulty, a new method is proposed which combines the acquisition of low-loss EELS spectra with a subsequent analysis driven by machine learning based algorithms (clustering). In the present research, this new approach is applied to a set of industrial duplex stainless steels having different compositions and it is contrasted to other characterization alternatives. As opposed to the other options, this method proved to be effective, reliable and time-saving.},
organization = {Ministerio de Ciencia e Innovación (Spain)
Ministerio de Economía y Competitividad (Spain)
European Commission (European Union)
Universidad de Cádiz (Spain)},
publisher = {Elsevier Inc.},
keywords = {Clustering},
keywords = {Duplex stainless steels},
keywords = {Low-loss EELS},
keywords = {Phase identification},
keywords = {Plasmon},
title = {Combining low-loss EELS experiments with machine learning-based algorithms to automate the phases separation imaging in industrial duplex stainless steels},
doi = {10.1016/j.matchar.2024.113924},
author = {Castro Riglos, V. and Amaya Dolores, Beatriz and Ramasubramaniam, A. and González Souto, Lorena and Sanchez, R. and Botana Pedemonte, Francisco Javier and Almagro, J.F. and Calvino Gámez, José Juan and Lajaunie, Luc Cyrille Jacques},
}