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Register a new userNanoparticle Size Distribution Quantification: Results of a SAXS Inter-Laboratory Comparison
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We present the first world-wide inter-laboratory comparison of small-angle X-ray scattering (SAXS) for nanoparticle sizing. The measurands in this comparison are the mean particle radius, the width of the size distribution and the particle concentration. The investigated sample consists of dispersed silver nanoparticles, surrounded by a stabilizing polymeric shell of poly(acrylic acid). The silver cores dominate the X-ray scattering pattern, leading to the determination of their radii size distribution using: i) Glatters Indirect Fourier Transformation method, ii) classical model fitting using SASfit and iii) a Monte Carlo fitting approach using McSAS. The application of these three methods to the collected datasets produces consistent mean number- and volume-weighted core radii of R$_n$ = 2.76 nm and R$_v$ = 3.20 nm, respectively. The corresponding widths of the log-normal radii distribution of the particles were $sigma_n$ = 0.65 nm and $sigma_v$ = 0.71 nm. The particle concentration determined using this method was 3.00 $pm$ 0.38 g/L (4.20 $pm$ 0.73 $times$ 10$^{-6}$ mol/L). We show that the results are slightly biased by the choice of data evaluation procedure, but that no substantial differences were found between the results from data measured on a very wide range of instruments: the participating laboratories at synchrotron SAXS beamlines, commercial and home-made instruments were all able to provide data of high quality. Our results demonstrate that SAXS is a qualified method for revealing particle size distributions in the sub-20 nm region (at least), out of reach for most other analytical methods.
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Monte-Carlo (MC) methods, based on random updates and the trial-and-error principle, are well suited to retrieve particle size distributions from small-angle scattering patterns of dilute solutions of scatterers. The size sensitivity of size determination methods in relation to the range of scattering vectors covered by the data is discussed. Improvements are presented to existing MC methods in which the particle shape is assumed to be known. A discussion of the problems with the ambiguous convergence criteria of the MC methods are given and a convergence criterion is proposed, which also allows the determination of uncertainties on the determined size distributions.
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