Correlation Model for Size Measurement of Nanoparticles by Inductively Coupled Plasma-Mass Spectrometry

A data treatment model for fast and reliable particle size measurement by single particle ICP-MS was developed in this work. The proposed approach is based on the correlation between the known size of nanoparticle vs. the peak area obtained by time-resolved analysis. The designed model showed sufficient reliability and speed for industrial applications depending upon the credibility of correlation curve and the accuracy of particle size. For this, a window method was designed, where the data range is selected to identify ideal peaks in time-resolved spectrum and the average peak area of the particles is estimated from parameter, σ, of its Gauss fitting of the relationship. The regression coefficient (R2) of the correlation curve for commercial Au nanoparticles of <0.999 was obtained in 5 repeated measurements. The method was confirmed using NIST Au standard nanoparticles and the results showed excellent correlation with a R2 of 0.9998. The size of unknown particles determined using the proposed method matched well with that determined using TEM: the values matched within ±12% and the relative expanded uncertainty was 26% for 10 repeated measurements. Furthermore, the developed method was applied to iron oxide (Fe2O3) nanoparticles used in semiconductor manufacturing and the result showed a correlation coefficient of >0.998 as the background subtraction level was adjusted to the mean of blank signal plus 3σ (standard deviation). Overall, the developed method successfully demonstrated great potential for the determination of the size of nanoparticle with high accuracy and speed, especially for on-line monitoring of various chemicals used in the manufacturing of semiconductors.