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...

Full description

Saved in:
Bibliographic Details
Published in:Molecules (Basel, Switzerland) Switzerland), 2022-10, Vol.27 (19), p.6695
Main Authors: Mendoza, Jaeda M., Chen, Kenzie, Walters, Sequoyah, Shipley, Emily, Aptowicz, Kevin B., Holler, Stephen
Format: Article
Language:English
Subjects:
Aerosols
Agglomeration
Aggregates
Air pollution
Airborne particulates
Algorithms
Classification
Climate change
clusters
Computer simulation
Computer-generated environments
Environment
Light
Light scattering
Machine learning
Methods
Morphology
Optical properties
Particle size
Particulate emissions
Particulate matter
Simulation
Speckle patterns
Spheres
Statistical analysis
T-matrix
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules27196695