Paper
Machine learning analysis to classify nanoparticles from noisy spICP-TOFMS data
A two-stage semi-supervised machine learning approach was developed as a robust method to classify cerium-rich engineered, incidental, and natural nanoparticles measured by spICP-TOFMS.
Nanoparticle identification using single particle ICP-ToF-MS acquisition coupled to cluster analysis. From engineered to natural nanoparticles
Characterization and identification of multielement nanoparticles thanks to the use of a spICP-ToF-MS coupled to hierarchical agglomerative clustering (HAC).
Machine learning: our future spotlight into single-particle ICP-ToF-MS analysis
Using the multi-element capabilities of single-particle ICP-ToF-MS in combination with a laser ablation and machine learning algorithms, environmentally relevant road runoff samples were characterized.
Technical Note
Introducing “time-of-flight single particle investigator” (TOF-SPI): a tool for quantitative spICP-TOFMS data analysis
TOF-SPI is software for accurate, robust, and high-throughput analysis of single-particle ICP-TOFMS data.
About this collection
This collection consists of recently published and invited contributions that highlight strategies to maximise information extraction from multidimensional dataset generated from ICP-ToF-MS. The collection is Guest Edited by Björn Meermann (BAM, Germany), Lukas Schlatt (Nu Instruments Ltd, UK) and Lyndsey Hendriks (University of Vienna, Austria). The collection is now open to new submissions and new articles will be added as they are published.