Phase recognition in SEM-EDX chemical maps using positive matrix factorization
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123003801 |
| _version_ | 1827596417661665280 |
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| author | Xiangrui Kong Ivana Staničić Viktor Andersson Tobias Mattisson Jan B.C. Pettersson |
| author_facet | Xiangrui Kong Ivana Staničić Viktor Andersson Tobias Mattisson Jan B.C. Pettersson |
| author_sort | Xiangrui Kong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T03:10:17Z |
| format | Article |
| id | doaj.art-c67b3a65fbe44188b6d3e6801efd8623 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-09T03:10:17Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-c67b3a65fbe44188b6d3e6801efd86232023-12-04T05:22:32ZengElsevierMethodsX2215-01612023-12-0111102384Phase recognition in SEM-EDX chemical maps using positive matrix factorizationXiangrui Kong0Ivana Staničić1Viktor Andersson2Tobias Mattisson3Jan B.C. Pettersson4Department of Chemistry and Molecular Biology, Atmospheric Science, University of Gothenburg, Gothenburg SE-412 96, SwedenDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg SE-412 96, SwedenDepartment of Chemistry and Molecular Biology, Atmospheric Science, University of Gothenburg, Gothenburg SE-412 96, SwedenDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg SE-412 96, SwedenDepartment of Chemistry and Molecular Biology, Atmospheric Science, University of Gothenburg, Gothenburg SE-412 96, Sweden; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2215016123003801PMFSEMEDXChemical loopingNon-negative matrix factorization |
| spellingShingle | Xiangrui Kong Ivana Staničić Viktor Andersson Tobias Mattisson Jan B.C. Pettersson Phase recognition in SEM-EDX chemical maps using positive matrix factorization MethodsX PMF SEM EDX Chemical looping Non-negative matrix factorization |
| title | Phase recognition in SEM-EDX chemical maps using positive matrix factorization |
| title_full | Phase recognition in SEM-EDX chemical maps using positive matrix factorization |
| title_fullStr | Phase recognition in SEM-EDX chemical maps using positive matrix factorization |
| title_full_unstemmed | Phase recognition in SEM-EDX chemical maps using positive matrix factorization |
| title_short | Phase recognition in SEM-EDX chemical maps using positive matrix factorization |
| title_sort | phase recognition in sem edx chemical maps using positive matrix factorization |
| topic | PMF SEM EDX Chemical looping Non-negative matrix factorization |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123003801 |
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