Abstract
This study provides an overview of a newly developed open source program written in Python, TOFHunter, which permits the rapid and untargeted screening of inductively coupled plasma (ICP)-time-of-flight (TOF)-mass spectrometry (MS) datasets. ICP-TOF-MS is an analytical tool capable of providing quasi simultaneous detection of all nuclides from Li to Pu. This capability has triggered an increase in studies investigating single-particle analysis in which the TOF-MS provides correlated elemental/isotopic signatures on a particle basis in time. Similarly, laser ablation mapping has seen rapid growth owing to ICP-TOF-MS's capacity to handle fast washout times (<10 ms) while providing a broad nuclide coverage. The caveat to this broad mass coverage and high time resolution comes in the form of large, overwhelming datasets. With datasets typically on the scale of gigabytes, it is easy for a user to only focus on very targeted analytes; however, this focus diminishes the opportunity offered by the TOF-MS detector. TOFHunter applies chemometric methods, principal component analysis (PCA), and interesting features finder (IFF) on ICP-TOF-MS data, allowing for investigation of correlations, major and minor variance sources, and sample screening. The unique spectra identified by the (IFF) are used to generate a list of mass peaks, which are then matched with both nuclides and potential interferences before being exported for the user to investigate. Several case studies are discussed herein, demonstrating TOFHunter's ability to screen aqueous injections, single-particle/single-cell analysis, and probe laser ablation mapping files for unique regions of interest.
| Original language | English |
|---|---|
| Pages (from-to) | 910-920 |
| Number of pages | 11 |
| Journal | Journal of Analytical Atomic Spectrometry |
| Volume | 40 |
| Issue number | 3 |
| DOIs | |
| State | Published - Feb 25 2025 |
Funding
The authors acknowledge the different collaborators for generously providing the samples, from which the generated data were repurposed in this study. The collaboration and data shared were instrumental in testing the efficacy of the software algorithm. The engineered nanoparticles samples (UCNPs) presented in Fig. 3 and 5 were provided by Dr Christoph Gimmler from the Fraunhofer Center for Applied Nanotechnology in Hamburg, Germany, which is part of the Fraunhofer Institute for Applied Polymer Research. The Pd-doped nanoplastics (Fig. 4 and 5) were provided by Prof. Densie Mitrano from ETH Zurich in Zurich, Switzerland. The THP-1 cells presented in Fig. 4 were provided by Dr Tina Bürki-Thurnherr from Empa, St. Gallen, Switzerland. The data generated from the exposed THP-1 cells originated from a previous study34 and was repurposed here. This work was supported by the Oak Ridge National Laboratory, managed by UT-Battelle for the US Department of Energy under contract DE-AC05-000R22725. This work was supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory and the United States National Nuclear Security Administration's Office of Defense Nuclear Nonproliferation Research and Development.