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Possibilities and limitations of different analytical methods for the size determination of a bimodal dispersion of metallic nanoparticles
Colloids and Surfaces A: Physicochem. Eng. Aspects 377 (2011) 386–392
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Colloids and Surfaces A: Physicochemical and Engineering Aspects journal homepage: www.elsevier.com/locate/colsurfa
Possibilities and limitations of different analytical methods for the size determination of a bimodal dispersion of metallic nanoparticles Dirk Mahl, Jörg Diendorf, Wolfgang Meyer-Zaika, Matthias Epple ∗ Inorganic Chemistry and Center for Nanointegration Duisburg-Essen (CeNIDE), University of Duisburg-Essen, Universitaetsstr. 5-7, 45117 Essen, Germany
a r t i c l e
i n f o
Article history: Received 3 August 2010 Received in revised form 7 January 2011 Accepted 13 January 2011 Available online 21 January 2011 Keywords: Nanoparticles Electron microscopy Dynamic light scattering Analytical disc centrifugation Nanoparticle tracking analysis Particle size analysis
a b s t r a c t Silver nanoparticles (about 70 nm) and gold nanoparticles (about 15 nm) were prepared and colloidally stabilized with poly(vinylpyrrolidone) (PVP). The pure nanoparticles as well as a 1:1 mixture (w:w) were analysed with a variety of methods which probe the size distribution: Scanning electron microscopy, transmission electron microscopy, dynamic light scattering, analytical disc centrifugation, and Brownian motion analysis (nanoparticle tracking analysis). The differences between the methods are highlighted and their ability to distinguish between silver and gold nanoparticles in the mixture is demonstrated. The size distribution data from the different methods were clearly different, therefore it is recommended to apply more than one method to characterize the nanoparticle dispersion. In particular, the smaller particles were undetectable by dynamic light scattering and nanoparticle tracking analysis in the presence of the large particles. For the 1:1 mixture, only electron microscopy and analytical disc centrifugation were able to give quantitative data on the size distribution. On the other hand, it is not possible to make statements about an agglomeration in dispersion with electron microscopy because an agglomeration may also have occurred during the drying process. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Metallic nanoparticles are of high importance in current materials research, with potential applications in biomedicine, energy conversion, imaging, or as pigments [1,2]. Their size plays a critical role because it influences their physical and biological properties [3–7]. In a dispersion (typically in water), two cases must be clearly distinguished. In the first case, the dispersed material may consist of agglomerated nanoparticles with effective agglomerate diameters in the micrometer range; in the second case the dispersed material may consist of individual particles with a diameter in the nanorange (in the following denoted as “nanoparticulate”). The average particle size and the particle size distribution are important parameters to characterize dispersions of metallic nanoparticles which are usually not monodisperse. Therefore, for a given synthetic product, size distribution data are usually presented in the literature, but very often only electron microscopic images are shown which give little or no information about the state of dispersion before the necessary drying procedure. In addition, they represent only an extremely small fraction of the whole sample and may not be representative.
∗ Corresponding author. Tel.: +49 (0)201 183 2402; fax: +49 (0)201 183 2621. E-mail address: [email protected] (M. Epple). 0927-7757/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.colsurfa.2011.01.031
Other methods are sometimes used in addition, with dynamic light scattering (DLS) probably being the most prominent. Unfortunately, DLS has a tendency to produce artefacts because large particles scatter the light much more intensely than smaller particles. This may produce strongly misleading results if the batch of nanoparticles is not monodisperse but contains particles of different size and possibly also of different shape. Furthermore, the DLS data can be represented in different ways of distribution data (by size, by number, by intensity, . . .), leading to different positions of the maximum in the size distribution curve. Dieckmann et al. have shown the limitations of different techniques (TEM, DLS; analytical ultracentrifugation, asymmetric flow field flow fractionation) for a dispersion of ZnS nanoparticles [8], and Planken et al. have pointed out the limitations of dynamic light scattering in comparison to analytical ultracentrifugation and transmission electron microscopy for dispersed silica nanoparticles [9]. Bootz et al. have studied polymeric nanoparticles by SEM, DLS, and analytical ultracentrifugation with a size around 180 nm. They concluded that it is important to use more than one method to obtain meaningful results [10]. Hussain et al. evaluated the toxicity of different nanoparticles by DLS and TEM. In contrast to several other methods, DLS was able to identify changes in the size affect toxicity. However, they also concluded that it is necessary to use comprehensive particle characterization assays for toxicity studies, and that a single method is not sufficient for a meaningful characterization [7]. Landsiedel et al. agreed with Hussain et al. that a combination
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of different characterization methods is mandatory, and preferred TEM and analytical disc centrifugation over DLS. They concluded that DLS is a suitable method if a narrow size distribution of the particles is known by other techniques [11]. In a comprehensive study with a wide range of particle sizes with disc centrifugation and DLS, Bowen emphasized the importance of the particle shape and also the particle size distribution in order to obtain meaningful results [12]. In a seminal experiment, Lange compared electron microscopy, ultracentrifugation, disc centrifugation, dynamic light scattering in a round-robin experiment with well defined dispersions of polymeric nanoparticles with sizes from 80 up to several 100 nm. Monomodal, bimodal, and hexamodal particle size distributions were prepared. He concluded that ultracentrifugation was best suited for the characterization of such systems, and that dynamic light scattering works only with a narrow particle size distribution [13]. Cumberland and Lead have studied the agglomeration behaviour of silver nanoparticles in different environmentally relevant media by different methods, also emphasizing the need for a number of different methods for a full characterization of a colloidal dispersion [14]. Typically, polymeric and ceramic nanoparticles were used for these comparative studies. To explore the possibilities of and the deviations between different methods in the case of a bimodal mixture of metallic nanoparticles, we have prepared samples of silver nanoparticles (spherical; about 70 nm diameter) and gold nanoparticles (spherical; about 15 nm diameter) and analysed them in pure form as well as in a 1:1 (w:w) mixture. Several methods probing different sample properties were employed: Scanning electron microscopy (SEM), transmission electron microscopy (TEM), dynamic light scattering (DLS), analytical disc centrifugation, and nanoparticle tracking analysis (Brownian motion analysis). The question was how the size distribution data for the mixture would compare to those of the individual particles, and whether the methods would be able to differentiate between small and large metallic nanoparticles in the mixture.
2. Materials and methods Silver nanoparticles were synthesized by reduction with glucose in the presence of polyvinylpyrrolidone (PVP) according to Wang et al. [15] 2 g glucose and 1 g PVP were dissolved in 40 mL water and heated to 90 ◦ C. Then 0.5 g AgNO3 dissolved in 1 mL water were quickly added. The dispersion was kept at 90 ◦ C for 1 h and then let to cool to room temperature. The particles were washed twice by ultracentrifugation (30 min; 30,000 rpm; 66,100 g), followed by redispersion in water with ultrasonication. Thereby NO3 − , excess glucose and its oxidation products, excess PVP, and excess Ag+ were all fully removed. Gold nanoparticles were prepared by adding HAuCl4 (2.54 × 10−5 mol dissolved in 5 mL water) and sodium citrate (1.70 × 10−4 mol dissolved in 5 mL water) to 90 mL water at 100 ◦ C according to Turkevich et al. [16] After the suspension was cooled down slowly to 25 ◦ C, polyvinylpyrrolidone (PVP; 3.05 × 10−7 mol dissolved in 5 mL water) was added. The gold nanoparticles were purified twice to remove counter-ions, oxidation products of citrate, and excess polymer by ultracentrifugation (20 min, 20,000 rpm, 29,400 g) and redispersion in water with ultrasonication [17]. After purification by ultracentrifugation, both silver and gold dispersions contained only the functionalized metallic nanoparticles and no residual counter-ions or impurities. The metal concentration in the final dispersions was determined by atomic absorption spectroscopy (AAS; graphite tube furnace; Thermo Electron Corporation, M-Series).
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Both samples of silver and gold nanoparticles were prepared with a concentration of 37.3 mg L−1 . A 1:1 mixture was prepared by mixing equal volumes of these silver and gold nanoparticle dispersions, leading to concentrations of 18.65 mg L−1 for each silver and gold. Polyvinylpyrrolidone (PVP K30, Povidon 30; Fluka, molecular weight 40,000 g mol−1 ), tri-sodium citrate dihydrate (Fluka, p.a.), silver nitrate (Fluka, p.a.), and d-(+)-glucose (Baker) were used. HAuCl4 was prepared by dissolving elemental gold (>99.98%) in aqua regia, followed by heating until the evolution of nitrous gases was finished and subsequent dissolution in ultra pure water. Ultrapure water was prepared with an ELGA Purelab ultra instrument and used throughout the experiments. Scanning electron microscopy (SEM) was performed with a FEI Quanta 400 FEG equipped with a secondary electron (SE) detector. The acceleration voltage was 30 kV, and the spot size was 0.4 nm for gold nanoparticles and 0.6 nm for both the silver nanoparticles and the gold/silver nanoparticle mixture. Transmission electron TEM was performed with a Philips CM 200 FEG instrument (200 kV) equipped with a “Super Twin Lens”. A drop of a diluted solution of the sample was brought on a holey carbon film coated copper grid (Okenshoji Co. Ltd., Tokyo). Dynamic light scattering (DLS) for particle size analysis and zetapotential determination was performed with a Malvern Zetasizer Nano ZS ZEN 3600 (25 ◦ C; laser wavelength 633 nm). The scattering was monitored at a fixed angle of 173◦ in backward scattering mode. The primary data were derived from the correlation function of the scattered intensity. Analytical disc centrifugation was performed with a CPS Instruments Disc Centrifuge DC 24000 instrument at 25 ◦ C (24,000 rpm, 28,978 g). Two sucrose solutions (8 wt% and 24 wt%) were used to provide a density gradient with dodecane as stabilizing agent. The calibration standard was a dispersion of poly(vinyl chloride) (PVC) particles in water with a particle size of 377 nm. The wavelength of the laser light source was 470 nm. Nanoparticle tracking analysis (Brownian motion analysis) for particle size determination was performed with a NanoSight LM 10 instrument at 25 ◦ C at a laser wavelength of 638 nm. For Brownian motion analysis the samples had to be strongly diluted. Between 1200 and 1700 tracks were evaluated for each sample. The effective particle concentration was between 5 × 108 and 8 × 108 particles per mL. Dynamic light scattering and analytical disc centrifugation both require the refractive index of the dispersed material. For the pure samples, we used the refractive indices of gold and silver, respectively. For the mixture of gold and silver, we have used both values in the evaluation, but found no significant difference in the results. The experiments by dynamic light scattering, analytical disc centrifugation and Brownian motion analysis were each run at least in triplicate. The deviation between the results was in all cases smaller than the experimental error given by the evaluation software of the instruments.
3. Results and discussion Electron microscopy gives a direct image of a part of a particle population. Fig. 1 shows representative SEM and TEM images of silver and gold nanoparticles and of the mixture. It is evident that SEM is only able to visualize larger nanoparticles; the gold nanoparticles are near the resolution limit of the instrument. The particle shape and size can be easily derived, also for the mixture of the nanoparticles. The quantification of the particle proportions in the mixture is complicated by possible agglomeration and by smaller particles which may lie underneath larger particles or on their surface.
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Fig. 1. Electron microscopic images of silver nanoparticles, gold nanoparticles, and a 1:1 mixture of silver and gold nanoparticles.
The electron microscopic data can be quantified by counting individual particle sizes. Fig. 2 shows the results for silver (by SEM) and for gold (by TEM). The average size for silver was 70 ± 19 nm and for gold 15 ± 1.5 nm (numbers obtained by counting 100–200 particles and computing the average maximum diameter and the standard deviation). Electron microscopy gives the diameter of the metallic core of the nanoparticles because the hydrated polymer shell has collapsed in the high vacuum. No information about the state of agglomeration can be obtained by electron microscopy because agglomeration may also occur during the drying process. It was not possible to obtain accurate size distribution data for the mixture because the resolution in SEM is not sufficient for the (small) gold nanoparticles and because in TEM, the number of visible (large) silver nanoparticles is too small. Dynamic light scattering permits the determination of the particle size distribution from dispersed particles. It relies on Brownian motion which is monitored by Rayleigh scattering from dispersed particles. As Fig. 3 shows, the method gives reasonable size distribution data for the silver and gold silver nanoparticles alone, but it is completely unable to discriminate between silver and gold nanoparticles in the mixture. This is due to the fact that the scattering power of dispersed particles increases with the 6th power of
the particle diameter, therefore few large particles can completely mask many small particles. Dynamic light scattering measures the scattering intensity based on Rayleigh scattering. With a number of assumptions (monomodal particle size distribution, spherical particles) it is possible to compute different particle size distributions, e.g. by intensity, by volume, and by number [18]. It is noteworthy that these size distributions are all different due to the fact that the above assumptions are not fulfilled in a bimodal sample (as in our case). The z-average gives a rough measure of the average particle size. In general, the obtained average particle sizes are considerably larger than the naked dried core particle due to the contribution of the polymeric stabilizer and the hydration layer because the hydrodynamic radius is probed by DLS. Depending on the kind of particle size distribution used in DLS (by scattering intensity, by volume, by number), the values for the average particle size differ by a factor of 2–4 (Table 1). Dynamical light scattering also permits to measure the zeta potential of charged dispersed particles. In the mixture of silver and gold nanoparticles, only the value of the zeta potential of the larger silver nanoparticles was found, underscoring the fact that larger particles can completely mask the scattering signal of smaller particles (Table 1).
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Fig. 2. Size distribution of silver nanoparticles from SEM data and of gold nanoparticles from TEM data by visual analysis. The size distribution is shown by particle number (A and B), by particle volume (C and D) and by particle surface area (E and F). The largest diameter of each particle was used for the computations.
Nanoparticle tracking analysis (Brownian motion analysis) relies on the visual tracking of the motion of small particles as a function of time. Large particles move more slowly than small particles. The inspection of a large number of particles also permits
to analyse mixtures of small and large particles. For both pure silver or gold nanoparticle dispersions, the analysis gave satisfactory results. However, the particle size distribution was broader than that obtained by the other methods, and a clear differentiation of
Table 1 Summary of the results obtained by the different methods for silver nanoparticles, gold nanoparticles, and the 1:1 mixture of both. Dynamic light scattering and nanoparticle tracking analysis were not able to discriminate between large silver particles and small gold particles. The experimental uncertainties are given for all data where available. These represent the error ranges given by the analysis software of the corresponding experiment. The reproducibility was in all cases better than the error ranges given by the analysis software. For the SEM and TEM data, standard deviations are given. Method
Diameter
Ag nanoparticles
Au nanoparticles
1:1 mixture of Ag and Au nanoparticles
SEM TEM DLS
By number By number By number By intensity By volume z-average Zeta potential Polydispersity index (PDI) By number By number By weight By surface By number By volume
70 ± 19 nm 18–72 nm 63 ± 21 nm 124 ± 50 nm 94 ± 47 nm 102 nm −36 ± 2 mV 0.173 95 ± 36 nm 40 ± 19 nm 48 ± 23 nm 43 ± 20 nm 15 nm 15 nm
13 nm 15 ± 1.5 nm 22 ± 7 nm 52 ± 23 nm 30 ± 13 nm 42 nm −57 ± 7 mV 0.207 57 ± 28 nm 11 ± 3 nm 13 ± 3 nm 12 ± 3 nm 70 nm 70 nm
72–97 nm (Ag; 5% by number); 12 nm (Au; 95% by number) 43–112 nm (Ag; 7% by number); 13 nm (Au; 93% by number) 55 ± 20 nm 121 ± 48 nm 86 ± 41 nm 121 nm −34 ± 1 mV 0.171 87 ± 45 nm (average)
Nanoparticle tracking analysis Analytical disc centrifugation
Theoretical data
34 ± 16 nm (Ag; 66% by volume); 13 ± 3 nm (Au; 34% by volume) Ag: 1.8%; Au: 98.2% Ag: 65%; Au: 35%
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Fig. 3. Dynamic light scattering data of silver nanoparticles, gold nanoparticles, and a 1:1 mixture of both.
silver and gold particles in the mixture was not possible (Fig. 4). It must also be noted that the number of tracked particles is limited in this method, and that the samples must be diluted to avoid an “overcrowded” observation field with too many nanoparticles. Analytical disc centrifugation relies on the sedimentation of particles under centrifugal force. It is necessary to disperse the particles in a highly concentrated sugar solution with a density gradient. The particles move radially outwards and are detected by an incident light beam which is scattered or absorbed. This mode of detection (“relative weight”) cannot be easily related to the particle number or size. As Fig. 4D shows, disc centrifugation can easily separate large and small particles, and it also gives average particle sizes which reasonably agree with the electron microscopic data. We also see that this method tends to underestimate the particle diameters. This is probably caused by a lower effective density of the polymer-stabilized gold and silver nanoparticles in comparison to naked metallic nanoparticles. The density of pure gold and silver metal was used for the dispersions of gold and silver nanoparticles because it is difficult to estimate the influence of the polymer and the thickness of the polymeric shell on the hydrodynamic density. However, the polymer and the hydration layer contribute to the viscous drag of the particles during centrifugation. In our special case of a mixture of two different materials we faced the problem to choose a mixed density for both metals because the evaluation software accepts only one value for the dispersed material. As compromise for the 1:1 mixture we calculated an average value from the two densities. This led to slightly variable results for the diameters in the measurement of the mixture. Analytical disc centrifugation can also give results on the volume fractions of the different components in the mixture. For the mixture, it gave 66% for silver and 34% for gold (calculated as integral volume fractions). It is also very instructive to compare the number particle size distribution data of all methods in a cumulative way (Fig. 5). Disc centrifugation consistently gives the smallest particle diameters whereas nanoparticle tracking analysis gives the largest particle diameters. Only disc centrifugation is able to detect the bimodal particle size distribution in the Au/Ag mixture. Note that such
Fig. 4. Size distribution data (by number) from nanoparticle tracking analysis (A–C) and analytical disc centrifugation (D).
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As we have 18.65 mg L−1 of each metal in the mixed dispersion, the particle concentrations are 5.45 × 1014 L−1 for gold and 9.87 × 1012 L−1 for silver. Therefore, the number ratio of particles is 55:1 for Au:Ag (98.2%:1.8%). The volume fractions of the nanoparticles are 9.63 × 1017 nm3 L−1 = 9.63 × 10−7 for gold and 1.78 × 1018 nm3 L−1 = 1.78 × 10−6 for silver, and the volume ratio is 0.54:1 for Au:Ag (35%:65%). Note that the volume data corresponds to the diameter of the solid core; in the case of the hydrodynamic diameter which is probed by DLS, disc centrifugation, and Brownian motion analysis, the ratio is different. In dynamic light scattering, the scattering power of a particle increases with the 6th power of its diameter. Therefore, one silver particle scatters as much light as 10,000 gold particles, and the total scattering power of all silver nanoparticles in the 1:1 mixture is 180 times larger than that of all gold particles. A look on Table 1 shows that dynamic light scattering and Brownian motion analysis are not able to give any data on the bimodal particle size distribution. Both electron microscopic methods give approximate numbers for the particle size distribution, with slightly fewer gold nanoparticles than expected. This may be due to undetected small gold nanoparticles lying below the large silver particles or on their surface. Analytical disc centrifugation can distinguish between larger and smaller particles, and it also gives quantitative data about the particle distribution by volume. The uncertainties given by the instruments are larger than the experimental reproducibility, but these (often neglected) numbers illustrate that the difference between two apparently different average diameters is often not significant. Note that further available methods for particle size distribution that we did not apply do not discuss here are analytical ultracentrifugation [8–10,13,19], asymmetric flow field flow fractionation (aFFFF) [8], and small angle scattering, either of X-rays [20] or of neutrons [21,22]. These methods, however, are less often used for the characterization of nanoparticles. It must also be mentioned that fixed-angle dynamic light scattering as applied in commercial equipment (as in our case) gives only limited information on the particle size distribution. If the scattering signal is measured at different angles, much more information can be derived, including the better discrimination of particles in a polydisperse system. This has been unequivocally demonstrated [23,24], but it is, unfortunately, not at all the standard procedure when nanoparticle dispersions are characterized.
4. Conclusions
Fig. 5. Cumulative particle size distribution data (by number) from all different methods.
cumulative data could not be obtained for the mixture by electron microscopy due to the constraints mentioned above. Table 1 summarizes all results of the different methods. The theoretical data for the dispersions can be computed as follows: If we take 15 nm as average diameter of the gold nanoparticles and assume spherical particles, the volume of each particle is 1767 nm3 . With a density of gold of 19,320 kg m−3 , we compute a weight of each particle of 3.41 × 10−20 kg. For a spherical 70 nm silver particle, we obtain 180,000 nm3 as volume and with a density of 10,500 kg m−3 a weight of 1.89 × 10−18 kg.
The different available methods for particle size analysis of the dispersions of metallic nanoparticles gave different results even for the same well-defined ensemble of particles, in good agreement with the literature for ceramic and polymeric nanoparticles (see introduction). This is due to the fact that these methods all rely on different physical principles and/or detection methods. In addition, electron microscopy probes dry particles, i.e. the metallic core only, whereas the other methods probe the hydrodynamic radius which is always larger. A mismatch factor of up to three is possible when comparing two different methods when care is not taken to verify the type of average provided by the instrument (e.g. intensity, number or volume). This situation is further complicated if only the average diameter of a polydisperse system is reported. Strictly, the particle size distribution should be probed as well to define the system, as different averages (e.g. by intensity, by number, by volume) give rise to different numbers [18]. The fact that often only the results of one method are reported (typically, TEM or DLS) makes it difficult to compare the results of different groups, especially when biological properties are studied [11]. It is also important to note that not all methods can detect agglomeration phenomena (which
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may also occur after dispersion in cell-culture media or biological fluids) [25]. In the case of the given mixture of large silver and small gold nanoparticles, some methods completely failed to detect the small particles, therefore their presence would have been easily overlooked. Only electron microscopy and analytical disc centrifugation gave quantitative data on the fractions of silver and gold nanoparticles in the mixture. As a size-dependent effect of the distribution of metallic nanoparticles in the body and of their toxicity has been shown [4,26–30], it cannot be excluded that in some cases smaller particles that went undetected in fact were responsible for the biological reaction to apparently exclusively larger particles. For adequate conclusions, a range of methods must to be applied to permit a meaningful interpretation of, e.g., biological data. Acknowledgements We thank the Deutsche Forschungsgemeinschaft (DFG) for financial support of this project within the Priority Program NanoBioResponses (SPP1313). We thank Dr G. Hoffmann for the investigation of the samples by nanoparticle tracking analysis. We thank Mr S. Boukercha for help with the scanning electron microscopy. References [1] G. Schmid, Nanoparticles. From Theory to Application, Wiley-VCH, Weinheim, 2004. [2] D.A. Giljohann, D.S. Seferos, W.L. Daniel, M.D. Massich, P.C. Patel, C.A. Mirkin, Gold nanoparticles for biology and medicine, Angew. Chem. Int. Ed. 49 (2010) 3280–3294. [3] V. Sokolova, M. Epple, Inorganic nanoparticles as carriers of nucleic acids into cells, Angew. Chem. Int. Ed. 47 (2008) 1382–1395. [4] Y.W. Jun, J.W. Seo, J. Cheon, Nanoscaling laws of magnetic nanoparticles and their applicabilities in biomedical sciences, Acc. Chem. Res. 41 (2008) 179–189. [5] D.W. Grainger, D.G. Castner, Nanobiomaterials and nanoanalysis: opportunities for improving the science to benefit biomedical technologies, Adv. Mater. 20 (2008) 867–877. [6] S.J. Choi, J.M. Oha, J.H. Choy, Human-related application and nanotoxicology of inorganic particles: complementary aspects, J. Mater. Chem. 18 (2008) 615–620. [7] S.M. Hussain, L.K. Braydich-Stolle, A.M. Schrand, R.C. Murdock, K.O. Yu, D.M. Mattie, J.J. Schlager, M. Terrones, Toxicity evaluation for safe use of nanomaterials: recent achievements and technical challenges, Adv. Mater. 21 (2009) 1549–1559. [8] Y. Dieckmann, H. Colfen, H. Hofmann, A. Petri-Fink, Particle size distribution measurements of manganese-doped ZnS nanoparticles, Anal. Chem. 81 (2009) 3889–3895. [9] K.L. Planken, B.W.M. Kuipers, A.P. Philipse, Model independent determination of colloidal silica size distributions via analytical ultracentrifugation, Anal. Chem. 80 (2008) 8871–8879.
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Colloids and Surfaces A: Physicochemical and Engineering Aspects journal homepage: www.elsevier.com/locate/colsurfa
Possibilities and limitations of different analytical methods for the size determination of a bimodal dispersion of metallic nanoparticles Dirk Mahl, Jörg Diendorf, Wolfgang Meyer-Zaika, Matthias Epple ∗ Inorganic Chemistry and Center for Nanointegration Duisburg-Essen (CeNIDE), University of Duisburg-Essen, Universitaetsstr. 5-7, 45117 Essen, Germany
a r t i c l e
i n f o
Article history: Received 3 August 2010 Received in revised form 7 January 2011 Accepted 13 January 2011 Available online 21 January 2011 Keywords: Nanoparticles Electron microscopy Dynamic light scattering Analytical disc centrifugation Nanoparticle tracking analysis Particle size analysis
a b s t r a c t Silver nanoparticles (about 70 nm) and gold nanoparticles (about 15 nm) were prepared and colloidally stabilized with poly(vinylpyrrolidone) (PVP). The pure nanoparticles as well as a 1:1 mixture (w:w) were analysed with a variety of methods which probe the size distribution: Scanning electron microscopy, transmission electron microscopy, dynamic light scattering, analytical disc centrifugation, and Brownian motion analysis (nanoparticle tracking analysis). The differences between the methods are highlighted and their ability to distinguish between silver and gold nanoparticles in the mixture is demonstrated. The size distribution data from the different methods were clearly different, therefore it is recommended to apply more than one method to characterize the nanoparticle dispersion. In particular, the smaller particles were undetectable by dynamic light scattering and nanoparticle tracking analysis in the presence of the large particles. For the 1:1 mixture, only electron microscopy and analytical disc centrifugation were able to give quantitative data on the size distribution. On the other hand, it is not possible to make statements about an agglomeration in dispersion with electron microscopy because an agglomeration may also have occurred during the drying process. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Metallic nanoparticles are of high importance in current materials research, with potential applications in biomedicine, energy conversion, imaging, or as pigments [1,2]. Their size plays a critical role because it influences their physical and biological properties [3–7]. In a dispersion (typically in water), two cases must be clearly distinguished. In the first case, the dispersed material may consist of agglomerated nanoparticles with effective agglomerate diameters in the micrometer range; in the second case the dispersed material may consist of individual particles with a diameter in the nanorange (in the following denoted as “nanoparticulate”). The average particle size and the particle size distribution are important parameters to characterize dispersions of metallic nanoparticles which are usually not monodisperse. Therefore, for a given synthetic product, size distribution data are usually presented in the literature, but very often only electron microscopic images are shown which give little or no information about the state of dispersion before the necessary drying procedure. In addition, they represent only an extremely small fraction of the whole sample and may not be representative.
∗ Corresponding author. Tel.: +49 (0)201 183 2402; fax: +49 (0)201 183 2621. E-mail address: [email protected] (M. Epple). 0927-7757/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.colsurfa.2011.01.031
Other methods are sometimes used in addition, with dynamic light scattering (DLS) probably being the most prominent. Unfortunately, DLS has a tendency to produce artefacts because large particles scatter the light much more intensely than smaller particles. This may produce strongly misleading results if the batch of nanoparticles is not monodisperse but contains particles of different size and possibly also of different shape. Furthermore, the DLS data can be represented in different ways of distribution data (by size, by number, by intensity, . . .), leading to different positions of the maximum in the size distribution curve. Dieckmann et al. have shown the limitations of different techniques (TEM, DLS; analytical ultracentrifugation, asymmetric flow field flow fractionation) for a dispersion of ZnS nanoparticles [8], and Planken et al. have pointed out the limitations of dynamic light scattering in comparison to analytical ultracentrifugation and transmission electron microscopy for dispersed silica nanoparticles [9]. Bootz et al. have studied polymeric nanoparticles by SEM, DLS, and analytical ultracentrifugation with a size around 180 nm. They concluded that it is important to use more than one method to obtain meaningful results [10]. Hussain et al. evaluated the toxicity of different nanoparticles by DLS and TEM. In contrast to several other methods, DLS was able to identify changes in the size affect toxicity. However, they also concluded that it is necessary to use comprehensive particle characterization assays for toxicity studies, and that a single method is not sufficient for a meaningful characterization [7]. Landsiedel et al. agreed with Hussain et al. that a combination
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of different characterization methods is mandatory, and preferred TEM and analytical disc centrifugation over DLS. They concluded that DLS is a suitable method if a narrow size distribution of the particles is known by other techniques [11]. In a comprehensive study with a wide range of particle sizes with disc centrifugation and DLS, Bowen emphasized the importance of the particle shape and also the particle size distribution in order to obtain meaningful results [12]. In a seminal experiment, Lange compared electron microscopy, ultracentrifugation, disc centrifugation, dynamic light scattering in a round-robin experiment with well defined dispersions of polymeric nanoparticles with sizes from 80 up to several 100 nm. Monomodal, bimodal, and hexamodal particle size distributions were prepared. He concluded that ultracentrifugation was best suited for the characterization of such systems, and that dynamic light scattering works only with a narrow particle size distribution [13]. Cumberland and Lead have studied the agglomeration behaviour of silver nanoparticles in different environmentally relevant media by different methods, also emphasizing the need for a number of different methods for a full characterization of a colloidal dispersion [14]. Typically, polymeric and ceramic nanoparticles were used for these comparative studies. To explore the possibilities of and the deviations between different methods in the case of a bimodal mixture of metallic nanoparticles, we have prepared samples of silver nanoparticles (spherical; about 70 nm diameter) and gold nanoparticles (spherical; about 15 nm diameter) and analysed them in pure form as well as in a 1:1 (w:w) mixture. Several methods probing different sample properties were employed: Scanning electron microscopy (SEM), transmission electron microscopy (TEM), dynamic light scattering (DLS), analytical disc centrifugation, and nanoparticle tracking analysis (Brownian motion analysis). The question was how the size distribution data for the mixture would compare to those of the individual particles, and whether the methods would be able to differentiate between small and large metallic nanoparticles in the mixture.
2. Materials and methods Silver nanoparticles were synthesized by reduction with glucose in the presence of polyvinylpyrrolidone (PVP) according to Wang et al. [15] 2 g glucose and 1 g PVP were dissolved in 40 mL water and heated to 90 ◦ C. Then 0.5 g AgNO3 dissolved in 1 mL water were quickly added. The dispersion was kept at 90 ◦ C for 1 h and then let to cool to room temperature. The particles were washed twice by ultracentrifugation (30 min; 30,000 rpm; 66,100 g), followed by redispersion in water with ultrasonication. Thereby NO3 − , excess glucose and its oxidation products, excess PVP, and excess Ag+ were all fully removed. Gold nanoparticles were prepared by adding HAuCl4 (2.54 × 10−5 mol dissolved in 5 mL water) and sodium citrate (1.70 × 10−4 mol dissolved in 5 mL water) to 90 mL water at 100 ◦ C according to Turkevich et al. [16] After the suspension was cooled down slowly to 25 ◦ C, polyvinylpyrrolidone (PVP; 3.05 × 10−7 mol dissolved in 5 mL water) was added. The gold nanoparticles were purified twice to remove counter-ions, oxidation products of citrate, and excess polymer by ultracentrifugation (20 min, 20,000 rpm, 29,400 g) and redispersion in water with ultrasonication [17]. After purification by ultracentrifugation, both silver and gold dispersions contained only the functionalized metallic nanoparticles and no residual counter-ions or impurities. The metal concentration in the final dispersions was determined by atomic absorption spectroscopy (AAS; graphite tube furnace; Thermo Electron Corporation, M-Series).
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Both samples of silver and gold nanoparticles were prepared with a concentration of 37.3 mg L−1 . A 1:1 mixture was prepared by mixing equal volumes of these silver and gold nanoparticle dispersions, leading to concentrations of 18.65 mg L−1 for each silver and gold. Polyvinylpyrrolidone (PVP K30, Povidon 30; Fluka, molecular weight 40,000 g mol−1 ), tri-sodium citrate dihydrate (Fluka, p.a.), silver nitrate (Fluka, p.a.), and d-(+)-glucose (Baker) were used. HAuCl4 was prepared by dissolving elemental gold (>99.98%) in aqua regia, followed by heating until the evolution of nitrous gases was finished and subsequent dissolution in ultra pure water. Ultrapure water was prepared with an ELGA Purelab ultra instrument and used throughout the experiments. Scanning electron microscopy (SEM) was performed with a FEI Quanta 400 FEG equipped with a secondary electron (SE) detector. The acceleration voltage was 30 kV, and the spot size was 0.4 nm for gold nanoparticles and 0.6 nm for both the silver nanoparticles and the gold/silver nanoparticle mixture. Transmission electron TEM was performed with a Philips CM 200 FEG instrument (200 kV) equipped with a “Super Twin Lens”. A drop of a diluted solution of the sample was brought on a holey carbon film coated copper grid (Okenshoji Co. Ltd., Tokyo). Dynamic light scattering (DLS) for particle size analysis and zetapotential determination was performed with a Malvern Zetasizer Nano ZS ZEN 3600 (25 ◦ C; laser wavelength 633 nm). The scattering was monitored at a fixed angle of 173◦ in backward scattering mode. The primary data were derived from the correlation function of the scattered intensity. Analytical disc centrifugation was performed with a CPS Instruments Disc Centrifuge DC 24000 instrument at 25 ◦ C (24,000 rpm, 28,978 g). Two sucrose solutions (8 wt% and 24 wt%) were used to provide a density gradient with dodecane as stabilizing agent. The calibration standard was a dispersion of poly(vinyl chloride) (PVC) particles in water with a particle size of 377 nm. The wavelength of the laser light source was 470 nm. Nanoparticle tracking analysis (Brownian motion analysis) for particle size determination was performed with a NanoSight LM 10 instrument at 25 ◦ C at a laser wavelength of 638 nm. For Brownian motion analysis the samples had to be strongly diluted. Between 1200 and 1700 tracks were evaluated for each sample. The effective particle concentration was between 5 × 108 and 8 × 108 particles per mL. Dynamic light scattering and analytical disc centrifugation both require the refractive index of the dispersed material. For the pure samples, we used the refractive indices of gold and silver, respectively. For the mixture of gold and silver, we have used both values in the evaluation, but found no significant difference in the results. The experiments by dynamic light scattering, analytical disc centrifugation and Brownian motion analysis were each run at least in triplicate. The deviation between the results was in all cases smaller than the experimental error given by the evaluation software of the instruments.
3. Results and discussion Electron microscopy gives a direct image of a part of a particle population. Fig. 1 shows representative SEM and TEM images of silver and gold nanoparticles and of the mixture. It is evident that SEM is only able to visualize larger nanoparticles; the gold nanoparticles are near the resolution limit of the instrument. The particle shape and size can be easily derived, also for the mixture of the nanoparticles. The quantification of the particle proportions in the mixture is complicated by possible agglomeration and by smaller particles which may lie underneath larger particles or on their surface.
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Fig. 1. Electron microscopic images of silver nanoparticles, gold nanoparticles, and a 1:1 mixture of silver and gold nanoparticles.
The electron microscopic data can be quantified by counting individual particle sizes. Fig. 2 shows the results for silver (by SEM) and for gold (by TEM). The average size for silver was 70 ± 19 nm and for gold 15 ± 1.5 nm (numbers obtained by counting 100–200 particles and computing the average maximum diameter and the standard deviation). Electron microscopy gives the diameter of the metallic core of the nanoparticles because the hydrated polymer shell has collapsed in the high vacuum. No information about the state of agglomeration can be obtained by electron microscopy because agglomeration may also occur during the drying process. It was not possible to obtain accurate size distribution data for the mixture because the resolution in SEM is not sufficient for the (small) gold nanoparticles and because in TEM, the number of visible (large) silver nanoparticles is too small. Dynamic light scattering permits the determination of the particle size distribution from dispersed particles. It relies on Brownian motion which is monitored by Rayleigh scattering from dispersed particles. As Fig. 3 shows, the method gives reasonable size distribution data for the silver and gold silver nanoparticles alone, but it is completely unable to discriminate between silver and gold nanoparticles in the mixture. This is due to the fact that the scattering power of dispersed particles increases with the 6th power of
the particle diameter, therefore few large particles can completely mask many small particles. Dynamic light scattering measures the scattering intensity based on Rayleigh scattering. With a number of assumptions (monomodal particle size distribution, spherical particles) it is possible to compute different particle size distributions, e.g. by intensity, by volume, and by number [18]. It is noteworthy that these size distributions are all different due to the fact that the above assumptions are not fulfilled in a bimodal sample (as in our case). The z-average gives a rough measure of the average particle size. In general, the obtained average particle sizes are considerably larger than the naked dried core particle due to the contribution of the polymeric stabilizer and the hydration layer because the hydrodynamic radius is probed by DLS. Depending on the kind of particle size distribution used in DLS (by scattering intensity, by volume, by number), the values for the average particle size differ by a factor of 2–4 (Table 1). Dynamical light scattering also permits to measure the zeta potential of charged dispersed particles. In the mixture of silver and gold nanoparticles, only the value of the zeta potential of the larger silver nanoparticles was found, underscoring the fact that larger particles can completely mask the scattering signal of smaller particles (Table 1).
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Fig. 2. Size distribution of silver nanoparticles from SEM data and of gold nanoparticles from TEM data by visual analysis. The size distribution is shown by particle number (A and B), by particle volume (C and D) and by particle surface area (E and F). The largest diameter of each particle was used for the computations.
Nanoparticle tracking analysis (Brownian motion analysis) relies on the visual tracking of the motion of small particles as a function of time. Large particles move more slowly than small particles. The inspection of a large number of particles also permits
to analyse mixtures of small and large particles. For both pure silver or gold nanoparticle dispersions, the analysis gave satisfactory results. However, the particle size distribution was broader than that obtained by the other methods, and a clear differentiation of
Table 1 Summary of the results obtained by the different methods for silver nanoparticles, gold nanoparticles, and the 1:1 mixture of both. Dynamic light scattering and nanoparticle tracking analysis were not able to discriminate between large silver particles and small gold particles. The experimental uncertainties are given for all data where available. These represent the error ranges given by the analysis software of the corresponding experiment. The reproducibility was in all cases better than the error ranges given by the analysis software. For the SEM and TEM data, standard deviations are given. Method
Diameter
Ag nanoparticles
Au nanoparticles
1:1 mixture of Ag and Au nanoparticles
SEM TEM DLS
By number By number By number By intensity By volume z-average Zeta potential Polydispersity index (PDI) By number By number By weight By surface By number By volume
70 ± 19 nm 18–72 nm 63 ± 21 nm 124 ± 50 nm 94 ± 47 nm 102 nm −36 ± 2 mV 0.173 95 ± 36 nm 40 ± 19 nm 48 ± 23 nm 43 ± 20 nm 15 nm 15 nm
13 nm 15 ± 1.5 nm 22 ± 7 nm 52 ± 23 nm 30 ± 13 nm 42 nm −57 ± 7 mV 0.207 57 ± 28 nm 11 ± 3 nm 13 ± 3 nm 12 ± 3 nm 70 nm 70 nm
72–97 nm (Ag; 5% by number); 12 nm (Au; 95% by number) 43–112 nm (Ag; 7% by number); 13 nm (Au; 93% by number) 55 ± 20 nm 121 ± 48 nm 86 ± 41 nm 121 nm −34 ± 1 mV 0.171 87 ± 45 nm (average)
Nanoparticle tracking analysis Analytical disc centrifugation
Theoretical data
34 ± 16 nm (Ag; 66% by volume); 13 ± 3 nm (Au; 34% by volume) Ag: 1.8%; Au: 98.2% Ag: 65%; Au: 35%
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Fig. 3. Dynamic light scattering data of silver nanoparticles, gold nanoparticles, and a 1:1 mixture of both.
silver and gold particles in the mixture was not possible (Fig. 4). It must also be noted that the number of tracked particles is limited in this method, and that the samples must be diluted to avoid an “overcrowded” observation field with too many nanoparticles. Analytical disc centrifugation relies on the sedimentation of particles under centrifugal force. It is necessary to disperse the particles in a highly concentrated sugar solution with a density gradient. The particles move radially outwards and are detected by an incident light beam which is scattered or absorbed. This mode of detection (“relative weight”) cannot be easily related to the particle number or size. As Fig. 4D shows, disc centrifugation can easily separate large and small particles, and it also gives average particle sizes which reasonably agree with the electron microscopic data. We also see that this method tends to underestimate the particle diameters. This is probably caused by a lower effective density of the polymer-stabilized gold and silver nanoparticles in comparison to naked metallic nanoparticles. The density of pure gold and silver metal was used for the dispersions of gold and silver nanoparticles because it is difficult to estimate the influence of the polymer and the thickness of the polymeric shell on the hydrodynamic density. However, the polymer and the hydration layer contribute to the viscous drag of the particles during centrifugation. In our special case of a mixture of two different materials we faced the problem to choose a mixed density for both metals because the evaluation software accepts only one value for the dispersed material. As compromise for the 1:1 mixture we calculated an average value from the two densities. This led to slightly variable results for the diameters in the measurement of the mixture. Analytical disc centrifugation can also give results on the volume fractions of the different components in the mixture. For the mixture, it gave 66% for silver and 34% for gold (calculated as integral volume fractions). It is also very instructive to compare the number particle size distribution data of all methods in a cumulative way (Fig. 5). Disc centrifugation consistently gives the smallest particle diameters whereas nanoparticle tracking analysis gives the largest particle diameters. Only disc centrifugation is able to detect the bimodal particle size distribution in the Au/Ag mixture. Note that such
Fig. 4. Size distribution data (by number) from nanoparticle tracking analysis (A–C) and analytical disc centrifugation (D).
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As we have 18.65 mg L−1 of each metal in the mixed dispersion, the particle concentrations are 5.45 × 1014 L−1 for gold and 9.87 × 1012 L−1 for silver. Therefore, the number ratio of particles is 55:1 for Au:Ag (98.2%:1.8%). The volume fractions of the nanoparticles are 9.63 × 1017 nm3 L−1 = 9.63 × 10−7 for gold and 1.78 × 1018 nm3 L−1 = 1.78 × 10−6 for silver, and the volume ratio is 0.54:1 for Au:Ag (35%:65%). Note that the volume data corresponds to the diameter of the solid core; in the case of the hydrodynamic diameter which is probed by DLS, disc centrifugation, and Brownian motion analysis, the ratio is different. In dynamic light scattering, the scattering power of a particle increases with the 6th power of its diameter. Therefore, one silver particle scatters as much light as 10,000 gold particles, and the total scattering power of all silver nanoparticles in the 1:1 mixture is 180 times larger than that of all gold particles. A look on Table 1 shows that dynamic light scattering and Brownian motion analysis are not able to give any data on the bimodal particle size distribution. Both electron microscopic methods give approximate numbers for the particle size distribution, with slightly fewer gold nanoparticles than expected. This may be due to undetected small gold nanoparticles lying below the large silver particles or on their surface. Analytical disc centrifugation can distinguish between larger and smaller particles, and it also gives quantitative data about the particle distribution by volume. The uncertainties given by the instruments are larger than the experimental reproducibility, but these (often neglected) numbers illustrate that the difference between two apparently different average diameters is often not significant. Note that further available methods for particle size distribution that we did not apply do not discuss here are analytical ultracentrifugation [8–10,13,19], asymmetric flow field flow fractionation (aFFFF) [8], and small angle scattering, either of X-rays [20] or of neutrons [21,22]. These methods, however, are less often used for the characterization of nanoparticles. It must also be mentioned that fixed-angle dynamic light scattering as applied in commercial equipment (as in our case) gives only limited information on the particle size distribution. If the scattering signal is measured at different angles, much more information can be derived, including the better discrimination of particles in a polydisperse system. This has been unequivocally demonstrated [23,24], but it is, unfortunately, not at all the standard procedure when nanoparticle dispersions are characterized.
4. Conclusions
Fig. 5. Cumulative particle size distribution data (by number) from all different methods.
cumulative data could not be obtained for the mixture by electron microscopy due to the constraints mentioned above. Table 1 summarizes all results of the different methods. The theoretical data for the dispersions can be computed as follows: If we take 15 nm as average diameter of the gold nanoparticles and assume spherical particles, the volume of each particle is 1767 nm3 . With a density of gold of 19,320 kg m−3 , we compute a weight of each particle of 3.41 × 10−20 kg. For a spherical 70 nm silver particle, we obtain 180,000 nm3 as volume and with a density of 10,500 kg m−3 a weight of 1.89 × 10−18 kg.
The different available methods for particle size analysis of the dispersions of metallic nanoparticles gave different results even for the same well-defined ensemble of particles, in good agreement with the literature for ceramic and polymeric nanoparticles (see introduction). This is due to the fact that these methods all rely on different physical principles and/or detection methods. In addition, electron microscopy probes dry particles, i.e. the metallic core only, whereas the other methods probe the hydrodynamic radius which is always larger. A mismatch factor of up to three is possible when comparing two different methods when care is not taken to verify the type of average provided by the instrument (e.g. intensity, number or volume). This situation is further complicated if only the average diameter of a polydisperse system is reported. Strictly, the particle size distribution should be probed as well to define the system, as different averages (e.g. by intensity, by number, by volume) give rise to different numbers [18]. The fact that often only the results of one method are reported (typically, TEM or DLS) makes it difficult to compare the results of different groups, especially when biological properties are studied [11]. It is also important to note that not all methods can detect agglomeration phenomena (which
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may also occur after dispersion in cell-culture media or biological fluids) [25]. In the case of the given mixture of large silver and small gold nanoparticles, some methods completely failed to detect the small particles, therefore their presence would have been easily overlooked. Only electron microscopy and analytical disc centrifugation gave quantitative data on the fractions of silver and gold nanoparticles in the mixture. As a size-dependent effect of the distribution of metallic nanoparticles in the body and of their toxicity has been shown [4,26–30], it cannot be excluded that in some cases smaller particles that went undetected in fact were responsible for the biological reaction to apparently exclusively larger particles. For adequate conclusions, a range of methods must to be applied to permit a meaningful interpretation of, e.g., biological data. Acknowledgements We thank the Deutsche Forschungsgemeinschaft (DFG) for financial support of this project within the Priority Program NanoBioResponses (SPP1313). We thank Dr G. Hoffmann for the investigation of the samples by nanoparticle tracking analysis. We thank Mr S. Boukercha for help with the scanning electron microscopy. References [1] G. Schmid, Nanoparticles. From Theory to Application, Wiley-VCH, Weinheim, 2004. [2] D.A. Giljohann, D.S. Seferos, W.L. Daniel, M.D. Massich, P.C. Patel, C.A. Mirkin, Gold nanoparticles for biology and medicine, Angew. Chem. Int. Ed. 49 (2010) 3280–3294. [3] V. Sokolova, M. Epple, Inorganic nanoparticles as carriers of nucleic acids into cells, Angew. Chem. Int. Ed. 47 (2008) 1382–1395. [4] Y.W. Jun, J.W. Seo, J. Cheon, Nanoscaling laws of magnetic nanoparticles and their applicabilities in biomedical sciences, Acc. Chem. Res. 41 (2008) 179–189. [5] D.W. Grainger, D.G. Castner, Nanobiomaterials and nanoanalysis: opportunities for improving the science to benefit biomedical technologies, Adv. Mater. 20 (2008) 867–877. [6] S.J. Choi, J.M. Oha, J.H. Choy, Human-related application and nanotoxicology of inorganic particles: complementary aspects, J. Mater. Chem. 18 (2008) 615–620. [7] S.M. Hussain, L.K. Braydich-Stolle, A.M. Schrand, R.C. Murdock, K.O. Yu, D.M. Mattie, J.J. Schlager, M. Terrones, Toxicity evaluation for safe use of nanomaterials: recent achievements and technical challenges, Adv. Mater. 21 (2009) 1549–1559. [8] Y. Dieckmann, H. Colfen, H. Hofmann, A. Petri-Fink, Particle size distribution measurements of manganese-doped ZnS nanoparticles, Anal. Chem. 81 (2009) 3889–3895. [9] K.L. Planken, B.W.M. Kuipers, A.P. Philipse, Model independent determination of colloidal silica size distributions via analytical ultracentrifugation, Anal. Chem. 80 (2008) 8871–8879.
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