Classification of (micro)plastics using cathodoluminescence and machine learning Article Swipe
Elena M. Höppener
,
Sadegh Shahmohammadi
,
Luke A. Parker
,
Sieger Henke
,
Jan Harm Urbanus
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.talanta.2022.123985
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.talanta.2022.123985
Microplastics are a growing environmental and toxicological concern, having been found in the remotest locations of the earth and within multiple organs of the human body. However, the scale of the problem is not yet fully known as the smallest micro- and nanoplastics (MNPs) cannot accurately be measured due to limitations in measurement and detection techniques. In this paper, we combine the nanoscale resolution of Scanning Electron Microscopy (SEM) with the spectroscopic power of cathodoluminescence (CL) to show that six of the most common plastics – HDPE, LDPE, PP, PA, PS and PET – have unique spectra that could help in identifying the smallest MNPs. This was done by building a spectral database using 111 plastic samples from reference and consumer plastics with different sizes (0.001–1 mm), colours (e.g. black, blue, green, red, white/transparent and yellow) and shapes (e.g. irregular, fibre, spheres). We then trained multiple classification models using an Artificial Neural Network approach. With the use of these classification models we were able to classify the six plastics, including difficult samples such as black coloured plastics, based on their CL spectra with 97% accuracy, showing that our approach is robust towards sample differences. As most misclassifications occurred between LDPE and HDPE, a separate model for LDPE and HDPE allowed for >99% accuracy for the classification of HDPE and LDPE by using a two-step approach. This novel "proof-of-concept" to MNP analysis demonstrates the utility of SEM paired with CL to characterise microplastics with detailed spatial and chemical resolution warranting its further development and adoption.
Related Topics
Concepts
Cathodoluminescence
High-density polyethylene
Low-density polyethylene
Microplastics
Chemistry
Sample (material)
Scanning electron microscope
Biological system
Artificial intelligence
Composite material
Polyethylene
Pattern recognition (psychology)
Materials science
Optics
Computer science
Chromatography
Physics
Luminescence
Environmental chemistry
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.talanta.2022.123985
- OA Status
- hybrid
- Cited By
- 15
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4303712296
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4303712296Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.talanta.2022.123985Digital Object Identifier
- Title
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Classification of (micro)plastics using cathodoluminescence and machine learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
- Publication date
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2022-10-09Full publication date if available
- Authors
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Elena M. Höppener, Sadegh Shahmohammadi, Luke A. Parker, Sieger Henke, Jan Harm UrbanusList of authors in order
- Landing page
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https://doi.org/10.1016/j.talanta.2022.123985Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.talanta.2022.123985Direct OA link when available
- Concepts
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Cathodoluminescence, High-density polyethylene, Low-density polyethylene, Microplastics, Chemistry, Sample (material), Scanning electron microscope, Biological system, Artificial intelligence, Composite material, Polyethylene, Pattern recognition (psychology), Materials science, Optics, Computer science, Chromatography, Physics, Luminescence, Environmental chemistry, BiologyTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 6, 2024: 8, 2023: 1Per-year citation counts (last 5 years)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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