Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS
After demonstrating the effectiveness of self-organizing maps for detecting particles in spICP-ToF-MS time-series, and for characterizing and comparing natural nanoparticle systems, algorithms, a tutorial, and test data are provided.
Authors
- Marc Fabien Benedetti (ORCID: https://orcid.org/0000-0003-1075-8205)
- Mickaël Tharaud (ORCID: https://orcid.org/0000-0001-6131-655X)
- Lucas Mesnard
- Carla Costamanga
- C W Cuss (ORCID: https://orcid.org/0009-0009-7056-9802)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Memorial University of Newfoundland (CA)
- Institut de physique du globe de Paris (FR)
- Université Paris Cité (FR)
- Université Paris Sciences et Lettres (FR)
- Fundación Ciencias Exactas y Naturales (AR)
Publication Details
- Journal
- Journal of Analytical Atomic Spectrometry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ja00179j
- Citations
- 2
- Primary Topic
- Advanced Chemical Sensor Technologies
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Agence Nationale de la Recherche
- Institut de Physique du Globe de Paris
- Université de Paris
- Natural Sciences and Engineering Research Council of Canada