Phase recognition in SEM-EDX chemical maps using positive matrix factorization
Journal article, 2023

Images from scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDX) are informative and useful to understand the chemical composition and mixing state of solid materials. Positive matrix factorization (PMF) is a multivariate factor analysis technique that has been used in many applications, and the method is here applied to identify factors that can describe common features between elemental SEM-EDX maps. The procedures of converting both graphics and digital images to PMF input files are introduced, and the PMF analysis is exemplified with an open-access PMF program. A case study of oxygen carrier materials from oxygen carrier aided combustion is presented, and the results show that PMF successfully groups elements into factors, and the maps of these factors are visualized. The produced images provide further information on ash interactions and composition of distinct chemical layers. The method can handle all types of chemical maps and the method is not limited solely to SEM-EDX although these images have been used as an example. The main characteristics of the method are: • Adapting graphics and digital images ready for PMF analysis. • Conversion between 1-D and 2-D datasets allows visualization of common chemical maps of elements grouped in factors. • Handles all types of chemical mappings and large data sets.

Chemical looping

Non-negative matrix factorization

EDX

PMF

SEM

Author

Xiangrui Kong

University of Gothenburg

Ivana Stanicic

Chalmers, Space, Earth and Environment, Energy Technology

Viktor Andersson

University of Gothenburg

Tobias Mattisson

Chalmers, Space, Earth and Environment, Energy Technology

Jan B. C. Pettersson

University of Gothenburg

MethodsX

2215-0161 (eISSN)

Vol. 11 102384

Förbränningskemi för biomassa med syrebärarmaterial

Swedish Research Council (VR) (2016-06023), 2017-01-01 -- 2024-12-31.

Subject Categories

Clinical Medicine

Chemical Sciences

DOI

10.1016/j.mex.2023.102384

PubMed

37822675

More information

Latest update

10/19/2023