<i>Post hoc</i> Interlaboratory Comparison of Single Particle ICP-MS Size Measurements of NIST Gold Nanoparticle Reference Materials

Single particle inductively coupled plasma-mass spectrometry (spICP-MS) is an emerging technique that enables simultaneous measurement of nanoparticle size and number quantification of metal-containing nanoparticles at realistic environmental exposure concentrations. Such measurements are needed to understand the potential environmental and human health risks of nanoparticles. Before spICP-MS can be considered a mature methodology, additional work is needed to standardize this technique including an assessment of the reliability and variability of size distribution measurements and the transferability of the technique among laboratories. This paper presents the first <i>post hoc</i> interlaboratory comparison study of the spICP-MS technique. Measurement results provided by six expert laboratories for two National Institute of Standards and Technology (NIST) gold nanoparticle reference materials (RM 8012 and RM 8013) were employed. The general agreement in particle size between spICP-MS measurements and measurements by six reference techniques demonstrates the reliability of spICP-MS and validates its sizing capability. However, the precision of the spICP-MS measurement was better for the larger 60 nm gold nanoparticles and evaluation of spICP-MS precision indicates substantial variability among laboratories, with lower variability between operators within laboratories. Global particle number concentration and Au mass concentration recovery were quantitative for RM 8013 but significantly lower and with a greater variability for RM 8012. Statistical analysis did not suggest an optimal dwell time, because this parameter did not significantly affect either the measured mean particle size or the ability to count nanoparticles. Finally, the spICP-MS data were often best fit with several single non-Gaussian distributions or mixtures of Gaussian distributions, rather than the more frequently used normal or log-normal distributions