Microplastic and nanoplastic chemical characterization by thermal desorption and pyrolysis mass spectrometry with unsupervised machine learning
This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included. The code employs principal component analysis for dimensionality reduction, learns the resulting datasets' latent dimensionality, and completes Gaussian mixture modeling and fuzzy c-means clustering.*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[
"006:55"
]
|
| contactPoint |
{
"fn": "Thomas P. Forbes",
"hasEmail": "mailto:thomas.forbes@nist.gov"
}
|
| description | This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included. The code employs principal component analysis for dimensionality reduction, learns the resulting datasets' latent dimensionality, and completes Gaussian mixture modeling and fuzzy c-means clustering.*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST. |
| distribution |
[
{
"title": "readme",
"mediaType": "text/plain",
"description": "Describes data set and files.",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2957/readme.txt"
},
{
"title": "Microplastics and Nanoplastics chemical characterization by thermal desorption and pyrolysis mass spectrometry dataset",
"mediaType": "application/x-zip-compressed",
"description": "This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included. The code employs principal component analysis for dimensionality reduction, learns the resulting datasets' latent dimensionality, and completes Gaussian mixture modeling and fuzzy c-means clustering.*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2957/MNP-MS-ML%20NIST%20Data%20Publication.zip"
}
]
|
| identifier | ark:/88434/mds2-2957 |
| issued | 2023-04-24 |
| keyword |
[
"Chemical Characterization",
"Environment",
"GC-MS",
"Machine Learning",
"Mass Spectrometry",
"Microplastic",
"Nanoplastics"
]
|
| landingPage | https://data.nist.gov/od/id/mds2-2957 |
| language |
[
"en"
]
|
| license | https://www.nist.gov/open/license |
| modified | 2023-03-24 00:00:00 |
| programCode |
[
"006:045"
]
|
| publisher |
{
"name": "National Institute of Standards and Technology",
"@type": "org:Organization"
}
|
| theme |
[
"Chemistry:Analytical chemistry",
"Environment:Air / water / soil quality",
"Materials:Materials characterization",
"Nanotechnology:Nanomaterials"
]
|
| title | Microplastic and nanoplastic chemical characterization by thermal desorption and pyrolysis mass spectrometry with unsupervised machine learning |
