Analytical and Bioanalytical Chemistry

, Volume 408, Issue 19, pp 5075–5087 | Cite as

Drift correction of the dissolved signal in single particle ICPMS

  • Geert CornelisEmail author
  • Sebastien Rauch
Research Paper
Part of the following topical collections:
  1. Single-particle-ICP-MS Advances


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.

Graphical Abstract

A method is presented where a spICP-MS signal affected by drift (left) is corrected (right) by adjusting the local (moving) averages (green) and standard deviations (purple) to the respective values at a reference time (red). In combination with removing particle events (blue) in the case of calibration standards, this method is shown to obtain particle size distributions where that would otherwise be impossible, even when the deconvolution method is used to discriminate dissolved and particle signals


Nanoparticles/nanotechnology Mass spectrometry/ICPMS Chemometrics/statistics Metals/heavy metals 



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.

Supplementary material

216_2016_9509_MOESM1_ESM.pdf (1.2 mb)
ESM 1 (PDF 1203 kb)


  1. 1.
    Laborda F, Bolea E, Jimenez-Lamana J. Single particle inductively coupled plasma mass spectrometry: a powerful tool for nanoanalysis. Anal Chem. 2014;86(5):2270–8.CrossRefGoogle Scholar
  2. 2.
    Tuoriniemi J, Cornelis G, Hassellov M. A new peak recognition algorithm for detection of ultra-small nano-particles by single particle ICP-MS using rapid time resolved data acquisition on a sector-field mass spectrometer. J Anal Atom Spectrom. 2015;30(8):1723–9.CrossRefGoogle Scholar
  3. 3.
    Montano MD, Badiei HR, Bazargan S, Ranville JF. Improvements in the detection and characterization of engineered nanoparticles using spICP-MS with microsecond dwell times. Environ Sci Nano. 2014;1(4):338–46.CrossRefGoogle Scholar
  4. 4.
    Tuoriniemi J, Cornelis G, Hassellöv M. Improving accuracy of single particle ICPMS for Measurement Of Size Distributions And Number Concentrations Of Nanoparticles By Determining Analyte Partitioning During Nebulization. J Anal Atom Spectrom. 2014;29:743–52.CrossRefGoogle Scholar
  5. 5.
    Laborda F, Jimenez-Lamana J, Bolea E, Castillo JR. Critical considerations for the determination of nanoparticle number concentrations, size and number size distributions by single particle ICP-MS. J Anal Atom Spectrom. 2013;28(8):1220–32.CrossRefGoogle Scholar
  6. 6.
    Degueldre C, Favarger PY. Colloid analysis by single particle inductively coupled plasma-mass spectroscopy: a feasibility study. Colloids Surf A Physicochem Eng Asp. 2003;217(1/3):137–42.CrossRefGoogle Scholar
  7. 7.
    Cornelis G, Hassellov M. A signal deconvolution method to discriminate smaller nanoparticles in single particle ICP-MS. J Anal Atom Spectrom. 2014;29(1):134–44.CrossRefGoogle Scholar
  8. 8.
    Lee S, Bi X, Reed RB, Ranville JF, Herckes P, Westerhoff P. Nanoparticle size detection limits by single particle ICP-MS for 40 elements. Environ Sci Technol. 2014;48(17):10291–300.CrossRefGoogle Scholar
  9. 9.
    Alkemade CTJ, Snelleman W, Boutilier GD, Pollard BD, Winefordner JD, Chester TL, et al. A review and tutorial discussion of noise and signal-to-noise ratios in analytical spectrometry–I. Fundamental principles of signal-to-noise ratios. Spectrochim Acta B Atom Spectrosc. 1978;33(8):383–99.CrossRefGoogle Scholar
  10. 10.
    Laborda F, Medrano J, Castillo JR. Quality of quantitative and semiquantitative results in inductively coupled plasma mass spectrometry. J Anal Atom Spectrom. 2001;16(7):732–8.CrossRefGoogle Scholar
  11. 11.
    Cheatham MM, Sangrey WF, White WM. Sources of error in external calibration ICP-MS analysis of geological samples and an improved nonlinear drift correction procedure. Spectrochim Acta B Atom Spectrosc. 1993;48(3):E487–506.CrossRefGoogle Scholar
  12. 12.
    Brown RJC, Goddard SL, Blakley KC, Brown AS. Improvements to standard methodologies for the analytical determination of metals in stationary-source emissions samples. J Air Waste Manag Assoc. 2011;61(7):764–70.CrossRefGoogle Scholar
  13. 13.
    Tanner M. Shorter signals for improved signal to noise ratio, the influence of Poisson distribution. J Anal Atom Spectrom. 2010;25(3):405–7.CrossRefGoogle Scholar
  14. 14.
    Tanner M, Günther D. Short transient signals, a challenge for inductively coupled plasma mass spectrometry, a review. Anal Chim Acta. 2009;633(1):19–28.CrossRefGoogle Scholar
  15. 15.
    Mitrano DM, Ranville JF, Bednar A, Kazor K, Hering AS, Higgins CP. Tracking dissolution of silver nanoparticles at environmentally relevant concentrations in laboratory, natural, and processed waters using single particle ICP-MS (spICP-MS). Environ Sci Nano. 2014;1(3):248–59.CrossRefGoogle Scholar
  16. 16.
    Telgmann L, Metcalfe CD, Hintelmann H. Rapid size characterization of silver nanoparticles by single particle ICP-MS and isotope dilution. J Anal Atom Spectrom. 2014;29(7):1265–72.CrossRefGoogle Scholar
  17. 17.
    Chen WC, Wee P, Brindle ID. Elimination of the memory effects of gold, mercury, and silver in inductively coupled plasma atomic emission spectroscopy. J Anal Atom Spectrom. 2000;15(4):409–13.CrossRefGoogle Scholar
  18. 18.
    Pace HE, Rogers NJ, Jarolimek C, Coleman VA, Higgins CP, Ranville JF. Determining transport efficiency for the purpose of counting and sizing nanoparticles via single particle inductively coupled plasma mass spectrometry. Anal Chem. 2011;83(24):9361–9.CrossRefGoogle Scholar
  19. 19.
    Tuoriniemi J, Cornelis G, Hassellöv M. Size discrimination and detection capabilities of single-particle ICP-MS for environmental analysis of silver nanoparticles. Anal Chem. 2012;29:743–52.Google Scholar
  20. 20.
    Prescott JR. A statistical model for photomultiplier single-electron statistics. Nucl Instrum Methods. 1966;39(1):173.CrossRefGoogle Scholar
  21. 21.
    Boumans PWJM, McKenna RJ, Bosveld M. Analysis of the limiting noise and identification of some factors that dictate the detection limits in a low-power inductively coupled argon plasma system. Spectrochim Acta B Atom Spectrosc. 1981;36(11):1031–58.CrossRefGoogle Scholar
  22. 22.
    Bi X, Lee S, Ranville JF, Sattigeri P, Spanias A, Herckes P, et al. Quantitative resolution of nanoparticle sizes using single particle inductively coupled plasma mass spectrometry with the K-means clustering algorithm. J Anal Atom Spectrom. 2014;29(9):1630–9.CrossRefGoogle Scholar
  23. 23.
    Brockwell PJ, Davis RA. Time series: Theory and models. Springer series in statistics. Berlin: Springer; 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Soil and EnvironmentSwedish University of Agricultural SciencesUppsalaSweden
  2. 2.Department of Chemistry and Molecular BiologyUniversity of GothenburgGöteborgSweden
  3. 3.Water Environment Technology, Department of Civil and Environmental EngineeringChalmers University of TechnologyGöteborgSweden

Personalised recommendations