Classification of Aggregates Using Multispectral Two-Dimensional Angular Light Scattering Simulations
Airborne particulate matter plays an important role in climate change and health impacts, and is generally irregularly shaped and/or forms agglomerates. These particles may be characterized through their light scattering signals. Two-dimensional angular scattering from such particles produce a speck...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/27/19/6695 |
| _version_ | 1797477779378274304 |
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| author | Jaeda M. Mendoza Kenzie Chen Sequoyah Walters Emily Shipley Kevin B. Aptowicz Stephen Holler |
| author_facet | Jaeda M. Mendoza Kenzie Chen Sequoyah Walters Emily Shipley Kevin B. Aptowicz Stephen Holler |
| author_sort | Jaeda M. Mendoza |
| collection | DOAJ |
| description | Airborne particulate matter plays an important role in climate change and health impacts, and is generally irregularly shaped and/or forms agglomerates. These particles may be characterized through their light scattering signals. Two-dimensional angular scattering from such particles produce a speckle pattern that is influenced by their morphology (shape and material composition). In what follows, we revisit morphological descriptors obtained from computationally generated light scattering patterns from aggregates of spherical particles. These descriptors are used as inputs to a multivariate statistical algorithm and then classified via supervised machine learning algorithms. The classification results show improved accuracy over previous efforts and demonstrate the utility of the proposed morphological descriptors. |
| first_indexed | 2024-03-09T21:23:30Z |
| format | Article |
| id | doaj.art-37905c1fcd8744e78ca15d62b53af66c |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T21:23:30Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-37905c1fcd8744e78ca15d62b53af66c2023-11-23T21:15:44ZengMDPI AGMolecules1420-30492022-10-012719669510.3390/molecules27196695Classification of Aggregates Using Multispectral Two-Dimensional Angular Light Scattering SimulationsJaeda M. Mendoza0Kenzie Chen1Sequoyah Walters2Emily Shipley3Kevin B. Aptowicz4Stephen Holler5Department of Physics and Engineering Physics, Fordham University, Bronx, NY 10458, USADepartment of Physics and Engineering Physics, Fordham University, Bronx, NY 10458, USADepartment of Physics and Engineering, West Chester University, West Chester, PA 19383, USADepartment of Physics and Engineering Physics, Fordham University, Bronx, NY 10458, USADepartment of Physics and Engineering, West Chester University, West Chester, PA 19383, USADepartment of Physics and Engineering Physics, Fordham University, Bronx, NY 10458, USAAirborne particulate matter plays an important role in climate change and health impacts, and is generally irregularly shaped and/or forms agglomerates. These particles may be characterized through their light scattering signals. Two-dimensional angular scattering from such particles produce a speckle pattern that is influenced by their morphology (shape and material composition). In what follows, we revisit morphological descriptors obtained from computationally generated light scattering patterns from aggregates of spherical particles. These descriptors are used as inputs to a multivariate statistical algorithm and then classified via supervised machine learning algorithms. The classification results show improved accuracy over previous efforts and demonstrate the utility of the proposed morphological descriptors.https://www.mdpi.com/1420-3049/27/19/6695light scatteringaggregatesT-matrixmachine learningclassificationclusters |
| spellingShingle | Jaeda M. Mendoza Kenzie Chen Sequoyah Walters Emily Shipley Kevin B. Aptowicz Stephen Holler Classification of Aggregates Using Multispectral Two-Dimensional Angular Light Scattering Simulations Molecules light scattering aggregates T-matrix machine learning classification clusters |
| title | Classification of Aggregates Using Multispectral Two-Dimensional Angular Light Scattering Simulations |
| title_full | Classification of Aggregates Using Multispectral Two-Dimensional Angular Light Scattering Simulations |
| title_fullStr | Classification of Aggregates Using Multispectral Two-Dimensional Angular Light Scattering Simulations |
| title_full_unstemmed | Classification of Aggregates Using Multispectral Two-Dimensional Angular Light Scattering Simulations |
| title_short | Classification of Aggregates Using Multispectral Two-Dimensional Angular Light Scattering Simulations |
| title_sort | classification of aggregates using multispectral two dimensional angular light scattering simulations |
| topic | light scattering aggregates T-matrix machine learning classification clusters |
| url | https://www.mdpi.com/1420-3049/27/19/6695 |
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