Phase recognition in SEM-EDX chemical maps using positive matrix factorization

• Images from scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spec- troscopy (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 tech- nique 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 con- verting 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:center dot Adapting graphics and digital images ready for PMF analysis.center dot Conversion between 1-D and 2-D datasets allows visualization of common chemical maps of elements grouped in factors.center dot Handles all types of chemical mappings and large data sets.

Subject headings

NATURVETENSKAP  -- Kemi (hsv//swe)

NATURAL SCIENCES  -- Chemical Sciences (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine (hsv//eng)

Keyword

PMF

SEM

EDX

Chemical looping

Non-negative matrix factorization

Science & Technology - Other Topics

Chemical looping

Publication and Content Type

ref (subject category)

art (subject category)