What question did this study set out to answer?

The aim is to develop a flexible application for processing SP-ICP-TOF-MS datasets, improving data analysis capabilities and visualization.

February 5, 2026

IsotopeTrack: A fast and flexible application for the analysis of SP-ICP-TOF-MS datasets

Key Points

The aim is to develop a flexible application for processing SP-ICP-TOF-MS datasets, improving data analysis capabilities and visualization.
Developed IsotopeTrack as an open-source Python-based platform for SP-ICP-TOF-MS data processing.
Implemented complete calibration methodologies with transport rate and sensitivity calibrations.
Utilized three peak detection algorithms: Currie method, Formula C, and compound Poisson log-normal.
Validated the platform using diverse engineered nanoparticles including TiO₂, CeO₂, and various metallic alloys.
Achieved high accuracy in analyzing multi-element alloy compositions with measured values closely matching known compositions.
Produced ultra-uniform gold nanoparticles with precise mean diameters, significantly aiding characterization.
Enabled comprehensive visualizations such as elemental correlations and ternary diagrams, facilitating better data exploration.

Abstract

Rationale: Existing data processing applications for 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 including TiO₂, CeO₂, metallic alloys (Ni-Fe-Co, Ni-Fe-Cr-Mn, Ni-Fe-Mo), and 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 nanoparticles 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 nanoparticle characterization in complex systems.

Perguntar à IA

Bookmark