2026/02/04 by Houssame-Eddine Ahabchane, Aaron Goodman, Madjid Hadioui +1 · 1 voice
Chemistry · Earth and Planetary Sciences · Physics and Astronomy · #Analytical chemistry methods development #Geochemistry and Elemental Analysis #X-ray Spectroscopy and Fluorescence Analysis
paper · doi:10.1071/en25111
openalex created_date 2026/02/04 · openalex publication_date 2026/02/04 · openalex updated_date 2026/07/14
Environmental context Single particle–inductively coupled plasma–time-of-flight mass–spectrometry (SP-ICP-ToF-MS) is a powerful technique for characterizing nanoparticles, colloidal particles and fine and ultrafine particles in waters, soil, air and biota. However, this technology generates massive datasets that can take days to process, severely bottlenecking large scale environmental measurements. To address this shortcoming, this paper introduces a streamlined, automated workflow that drastically reduces analysis time of SP-ICP-ToF-MS, allowing researchers to quickly and efficiently extract crucial insights from complex heterogenous environmental systems. Rationale Existing data processing applications for single particle–inductively coupled plasma–time-of-flight–mass spectrometry (SP-ICP-ToF-MS), while valuable, have some limitations including restricted multi-sample analysis capabilities, vendor-specific constraints and the lack of efficient visualization approaches. Researchers need flexible, interactive visualization tools that can reveal compositional patterns and enable side-by-side sample comparisons for rapid trend identification and comprehensive data exploration across large datasets. Methodology we developed IsotopeTrack, an open-source Python-based platform designed specifically for SP-ICP-ToF-MS data processing with cross-platform compatibility (Windows and MacOS). The application implements complete calibration methodologies including transport rate and sensitivity calibrations, three distinct peak detection algorithms (Currie method, Formula C and compound Poisson log-normal) and element specific parameter optimization. Performance was validated using diverse engineered nanoparticles (NPs) including titanium dixoide (TiO2), cerium oxide (CeO2), metallic alloys (nickel–iron–cobalt, Ni–Fe–Co; nickel–iron–chromium–manganese, Ni–Fe–Cr–Mn; nickel–iron–molybdenum, Ni–Fe–Mo) and gold/silver (Au/Ag) core-shell particles. The platform features an interactive results canvas with drag and drop capabilities for constructing customized analysis pipelines and it supports multiple file formats. Results IsotopeTrack successfully analyzed multi-element alloy compositions with a high accuracy. For Ni–Fe–Co alloys, measured mass compositions of 60Ni (29.3%), 57Fe (55.1%) and 59Co (17.7%) closely matched known values of 28% Ni, 55% Fe and 17% Co. Ultra-uniform gold NPs yielded mean diameters of 52.0 ± 5.0 nm, 29.5 ± 4.4 nm and 21.1 ± 4.3 nm for nominal 50-, 30 and 20-nm particles. The platform generated comprehensive visualizations including elemental correlations, isotopic ratio distributions, ternary diagrams and composition heatmaps. Processing time was reduced from hours to minutes through parallel processing. The comparison of environmental samples consisting of hundreds of thousands of particles was greatly facilitated. Discussion IsotopeTrack addressed critical limitations in SP-ICP-ToF-MS data analysis by providing batch processing and interactive visualization tools. Element specific optimization ensured analytical rigor, while dramatically reducing processing time. This open-source framework represents a significant advance in single particle analysis, enabling efficient processing of large datasets essential for NP characterization in complex systems.