Improved single particle ICP-MS assessment using a novel Python-based data processing algorithm (Sparta) for nanoparticle quantification

Single particle inductively coupled plasma-mass spectrometry (spICP-MS) is a valuable tool to characterise nanoparticles (NPs) regarding their element-specific mass, size and particle number concentration (PNC). However, spICP-MS still suffers from a lack of harmonised and transparent data processing algorithms, resulting in little user-flexibility in adapting parameters, when working with e.g. the manufacturer software. In this study, we present a transparent Python-based algorithm (called ‘Sparta’), validated and critically compared with existing data processing methods (SPCal and an in-house Excel method as well as two commercial instrument software), applied for measurements of ∼30 nm Au, ∼74 nm TiO$_2$ and ∼50, ∼100 and ∼300 nm SiO2 NPs, using instruments from two different manufacturers using milli vs. microsecond dwell times. Sparta is capable of correcting baseline drift, determining the particle detection threshold (PDT) via the Poisson and iterative Gaussian method, performing a peak summation necessary for microsecond dwell times, and even extracting specific mass or size distributions from e.g. polydisperse materials via a Gaussian peak-fitting. Although all data processing methods benchmarked sizes and PNCs suit well for Au NPs, results show that millisecond dwell times systematically overestimated sizes for TiO$_2$ and SiO$_2$ (from 50–100 nm). For microsecond dwell times, only SiO$_2$ (50nm) showed slight overestimation due to the methodological LOD$_{size}$ of 53.1 nm for our algorithm. Nevertheless, Sparta accurately removes spurious background events of challenging samples such as SiO$_2$ at larger particle sizes (i.e., 300 nm). Thus, it can be readily applied to other engineered and natural NPs or even for biological cells (single cell ICP-MS) showing its great potential in improving data processing for spICP-MS.