Two-stage semi-supervised machine learning for classification of Ti-rich nanoparticles and microparticles measured by spICP-TOFMS
An application of two-stage SSML model to classify individual Ti-containing particles via spICP-TOFMS analysis. With two-stage SSML, we demonstrate low false-positive rates (≤5%) and moderate particle recoveries for all measured particle types.
Authors
- Hark Karkee (ORCID: https://orcid.org/0000-0002-4588-8512)
- Alexander Gundlach‐Graham (ORCID: https://orcid.org/0000-0003-4806-6255)
- Raven L. Buckman Johnson (ORCID: https://orcid.org/0000-0001-8331-6884)
Institutions
- Iowa State University (US)
- Tofwerk (Switzerland) (CH)
Publication Details
- Journal
- Journal of Analytical Atomic Spectrometry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ja00108k
- Citations
- 3
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.55
Funders
- National Science Foundation
- Colorado School of Mines
- Iowa State University
- Division of Chemistry