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 (L_C(ADC)) as detection decision levels for the discrimination of discrete NPs in sp-ICP-TOFMS analyses. We apply L_C(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.