A method is presented where drift, the random fluctuation of the signal intensity, is compensated for based on the estimation of the drift function by a moving average. It was shown using single particle ICPMS (spICPMS) measurements of 10 and 60 nm Au NPs that drift reduces accuracy of spICPMS analysis at the calibration stage and during calculations of the particle size distribution (PSD), but that the present method can again correct the average signal intensity as well as the signal distribution of particle-containing samples skewed by drift. Moreover, deconvolution, a method that models signal distributions of dissolved signals, fails in some cases when using standards and samples affected by drift, but the present method was shown to improve accuracy again. Relatively high particle signals have to be removed prior to drift correction in this procedure, which was done using a 3 × sigma method, and the signals are treated separately and added again. The method can also correct for flicker noise that increases when signal intensity is increased because of drift. The accuracy was improved in many cases when flicker correction was used, but when accurate results were obtained despite drift, the correction procedures did not reduce accuracy. The procedure may be useful to extract results from experimental runs that would otherwise have to be run again.
Nanoparticles/nanotechnology Mass spectrometry/ICPMS Chemometrics/statistics Metals/heavy metals
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The authors acknowledge funding sources from the European FP7 framework MARINA (CP-FP 263215) and the Horizon 2020 framework NANOFASE (646002), the Swedish research council (Vetenskapsrådet 621-2012-3937). Martin Hassellöv (Gothenburg University, Sweden) is thanked for allowing use of the sector-field ICPMS.
Compliance with ethical standards
The authors have no conflicts of interest to report.
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