Clustering analysis strategies for electron energy loss spectroscopy (EELS).
In this work, the use of cluster analysis algorithms, widely applied in the field of big data, is proposed to explore and analyse electron energy loss spectroscopy (EELS) data sets. Three different data clustering approaches have been tested both with simulated and experimental data from Fe3O4/Mn3O4...
| Authors: | , , , , , |
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| Format: | article |
| Status: | Versión aceptada para publicación |
| Publication Date: | 2018 |
| Country: | España |
| Institution: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repository: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/162057 |
| Online Access: | https://hdl.handle.net/2445/162057 |
| Access Level: | Open access |
| Keyword: | Espectroscòpia de pèrdua d'energia d'electrons Electron energy loss spectroscopy |
| Summary: | In this work, the use of cluster analysis algorithms, widely applied in the field of big data, is proposed to explore and analyse electron energy loss spectroscopy (EELS) data sets. Three different data clustering approaches have been tested both with simulated and experimental data from Fe3O4/Mn3O4 core/shell nanoparticles. The first method consists on applying data clustering directly to the acquired spectra. A second approach is to analyse spectral variance with principal component analysis (PCA) within a given data cluster. Lastly, data clustering on PCA score maps is discussed. The advantages and requirements of each approach are studied. Results demonstrate how clustering is able to recover compositional and oxidation state information from EELS data with minimal user input, giving great prospects for its usage in EEL spectroscopy. |
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