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Sospedra Ramírez Joan.pdf (711.64 KB)

Document type

Bachelor thesis

Publication date

2018-06

Publication license

cc-by-nc-nd (c) Sospedra, 2018
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/125267

Big data analysis applied to EEL spectroscopy

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Abstract

In order to characterize an unknown sample, big data and machine learning methods are proposed. An electron energy loss (EEL) spectrum image obtained in the transmission electron microscope is analyzed. Applying principal component analysis (PCA) to image EEL spectra, the noise present in the raw data can be discarded, and comparing with existing datasets and alternatively through clustering analysis, the presence of vanadium and oxygen in the sample over a substrate with lanthanum and oxygen can be recognized

Description

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2018, Tutors: Sonia Estradé Albiol, Javier Blanco Portals

Subject

Espectroscòpia de pèrdua d'energia d'electrons, Dades massives, Treballs de fi de grau

Subject (English)

Electron energy loss spectroscopy, Big data, Bachelor's theses

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Treballs Finals de Grau (TFG) - Física

Citation

SOSPEDRA RAMÍREZ, Joan. Big data analysis applied to EEL spectroscopy. [consulted: 11 of June of 2026]. Available at: https://hdl.handle.net/2445/125267

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