Performance of sp-ICP-TOFMS with signal distributions fitted to a compound Poisson model

Abstract

Accurate separation of signals from individual nanoparticles (NPs) from background ion signals is decisive to correct sizing and number-concentration determinations in single-particle (sp) ICP-MS analyses. In typical sp-ICP-MS approaches, NP signals are identified via outlier analysis based on the assumption of normally distributed (i.e. Gaussian) or Poisson-distributed background signals. However, for sp-ICP-MS with a Time-of-Flight (TOF) mass spectrometer that digitizes MS signal by fast analog-to-digital conversion (ADC), the background ion signals are neither Gaussian nor Poisson. Instead, steady-state ion signals with ICP-TOFMS follow a compound Poisson distribution that reflects noise contributions from Poisson-distributed arrival of ions and gain statistics of microchannel-plate-based ion detection. Here, we characterize this compound Poisson distribution with Monte Carlo simulations to establish net critical values (LC(ADC)) as detection decision levels for the discrimination of discrete NPs in sp-ICP-TOFMS analyses. We apply LC(ADC) to the analysis of gold-silver core–shell nanoparticles (Au–Ag NPs), and compare these results to conventional sigma-based NP-detection thresholds. Additionally, we investigate how accurate modelling of the compound Poisson TOFMS signal distribution enables separation of overlapping background and NP distributions; we demonstrate accurate size measurement of 20 nm Au NPs that have mean signal intensity of less than four counts.