Abstract
Single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) is used to measure the mass amounts of elements in individual nano and submicron particles. In spICP-TOFMS, element signals can only be recorded as "particles" if they are above the critical value, which is the threshold used to distinguish between particle-derived and background signals. If elements in particles are present in amounts close to or below the critical value, then these elements cannot be quantitatively measured, and the shape of the measured mass distributions will not be accurate. In addition, recorded spICP-TOFMS signal distributions are impacted by measurement uncertainty due to counting statistics inherent to the mass analyzer. Counting noise is most pronounced for elements detected with low signal levels and can lead to systematic biases in the observed element masses and mass ratios from a particle event. In turn, spICP-TOFMS data can lead to incorrect conclusions about element composition and/or size of recorded particles. To better understand how biases and noise can alter the interpretation of data, we employ Monte Carlo simulations to model spICP-TOFMS signals as a function of measurement parameters, such as particle size distribution (PSD), multi-element composition, absolute sensitivities (TofCts g-1), and measurement noise from ion-counting (Poisson) statistics. Monte Carlo simulations allow for the systematic comparison of known (simulated) element mass distributions to experimental (measured) data. To demonstrate the accuracy of our model in predicting spICP-TOFMS signal structure, we highlight the match between data from in-lab measurements and simulations for the detection of CeO2, ferrocerium mischmetal, and bastnaesite particles. Through Monte Carlo simulations, we explore how analyte PSDs and other measurement parameters can lead to the determination of biased particle sizes, particle numbers, element ratios, and multi-element compositions.
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