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1. Principal Component Analysis for STEM-EDS images: Optimal collection parameters
Kandidat-uppsats, Lunds universitet/Centrum för analys och syntesSammanfattning : STEM-EDS is a common technique used for compositional mapping in materials. The vasts amounts of raw data produced by STEM EDS is suitable for advanced analysis using methods in unsupervised machine learning. One powerful method is Principal Component Analysis (PCA) which can automatically discover significant chemical correlations in a sample. LÄS MER
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