RT journal article
T1 Combining low-loss EELS experiments with machine learning-based algorithms to automate the phases separation imaging in industrial duplex stainless steels
A1 Castro Riglos, V.
A1 Amaya Dolores, Beatriz
A1 Ramasubramaniam, A.
A1 González Souto, Lorena
A1 Sanchez, R.
A1 Botana Pedemonte, Francisco Javier
A1 Almagro, J.F.
A1 Calvino Gámez, José Juan
A1 Lajaunie, Luc Cyrille Jacques
A2 Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica
K1 Clustering
K1 Duplex stainless steels
K1 Low-loss EELS
K1 Phase identification
K1 Plasmon
AB 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.
PB Elsevier Inc.
SN 1044-5803
YR 2024
FD 2024
LK http://hdl.handle.net/10498/33841
UL http://hdl.handle.net/10498/33841
LA eng
NO Ministerio de Ciencia e Innovación (Spain)Ministerio de Economía y Competitividad (Spain)European Commission (European Union)Universidad de Cádiz (Spain)
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026