Development of a sample preparation approach to measure the size of nanoparticle aggregates by electron microscopy

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Development of a sample preparation approach to measure the size of nanoparticle aggregates by electron microscopy Agnieszka Dudkiewicz a,b,c,∗ , Angela Lehner d , Qasim Chaudhry e , Kristian Molhave f , Guenter Allmaier d , Karen Tiede g , Alistair B.A. Boxall b , Peter Hofmann h , John Lewis a a

The Food and Environment Research Agency, Sand Hutton, York Y041 1LZ, UK Department of Environment, The University of York, Heslington, York YO10 5DD, UK c National Centre for Food Manufacturing, The University of Lincoln, Holbeach PE12 7AG, UK d Research Group Bio- and Polymer Analysis, Institute of Chemical Technologies and Analytics, TU Wien (Vienna University of Technology), Getreidemarkt 9, A-1060 Vienna, Austria e University of Chester, Parkgate Road, Chester CH1 4BJ, UK f Department of Micro and Nanotechnology, Technical University of Denmark, DTU Bldg 345b, Lyngby 2800, Denmark g Research Centre for Global Food Security and Ecosystems, University of Hohenheim, Wollgrasweg 43, 70155 Stuttgart, Germany h Department of Geoinformatics — Z GIS, University of Salzburg, Schillerstr. 30, A-5020 Salzburg, Austria b

a r t i c l e

i n f o

Article history: Received 12 February 2018 Received in revised form 22 May 2018 Accepted 23 May 2018 Available online xxx Keywords: Nanoparticles Aggregates Measurement Electron microscopy Sample preparation Artefacts

a b s t r a c t Electron microscopy (EM) is widely used for nanoparticle (NP) sizing. Following an initial assessment of two sample preparation protocols described in the current literature as “unperturbed”, we found that neither could accurately measure the size of NPs featuring a broad size distribution, e.g., aggregates. Because many real-world NP samples consist of aggregates, this finding was of considerable concern. The data showed that the protocols introduced errors into the measurement by either inducing agglomeration artefacts or providing a skewed size distribution towards small particles (skewing artefact). The focus of this work was to develop and apply a mathematical refinement to correct the skewing artefact. This refinement provided a much improved agreement between EM and a reference methodology, when applied to the measurement of synthetic amorphous silica NPs. Further investigation, highlighted the influence of NP chemistry on the refinement. This study emphasised the urgent need for greater and more detailed consideration regarding the sample preparation of NP aggregates to routinely achieve accurate measurements by EM. This study also provided a novel refinement solution applicable to the size characterisation of silica and citrate-coated gold NPs featuring broad size distributions. With further research, this approach could be extended to other NP types. © 2018 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Introduction Accurate information regarding the size and morphology of a particle or nanoparticle (NP) is important to understanding and predicting its chemical reactivity, physical stability, and biological availability (Dudkiewicz, Luo, Tiede, & Boxall, 2012; Paul, Datta, Datta, & Saha, 2013; Stefaniak, 2017). In all fields of nanoscience, electron microscopy (EM) is recognised as the primary technique used for analyte characterisation (Calzolai, Gilliland, & Rossi, 2012; EFSA Scientific Committee, 2011; Thomas, 2013). Other techniques

∗ Corresponding author at: The Food and Environment Research Agency, Sand Hutton, York Y041 1LZ, UK. E-mail address: [email protected] (A. Dudkiewicz).

such as laser light scattering methods (e.g., dynamic light scattering, DLS), centrifugation, and field-flow fractionation coupled to a range of detectors are also regularly used to characterise NPs (Calzolai et al., 2012). These techniques have certain advantages over EM; e.g., they can characterise liquid suspensions of NPs or measure zeta potential (particularly DLS) and are less expensive to operate. However, NP characterisation using different methods introduces drawbacks because of the specific chemical and physical principles upon which each method is based (Dudkiewicz, Wagner et al., 2015). Therefore, the use of at least two methods to characterise NPs has been recommended, one of which should be EM (EFSA Scientific Committee, 2011). Although EM is widely relied upon, a lack of consideration of the complexity of its outputs can lead to misinterpretation of the results in terms of the characteristics of a particle and, therefore, its

https://doi.org/10.1016/j.partic.2018.05.007 1674-2001/© 2018 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

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likely behaviour. For example, characterisation of NP clustering by ‘standard’ high-vacuum EM is very challenging because a dry specimen is required for this type of imaging technique (Carter et al., 2016). Removal of the water from an NP liquid suspension is likely to introduce analytical artefacts, the most common of which is NP agglomeration (Dudkiewicz et al., 2011; Tiede, Dudkiewicz, Boxall, & Lewis, 2015; Tiede et al., 2008). If agglomeration occurs during sample preparation, an accurate assessment of the initial state of NPs in the suspension (e.g., their aggregation) will not be possible. NP agglomeration and aggregation are defined as distinct phenomena. While an agglomerate is an “assemblage of particles which are loosely coherent”, an aggregate features rigidly joined particles (ISO 14887, 2000). Other methods that allow the measurement of NPs in liquid suspensions may distinguish the agglomeration/aggregation status in the initial sample, whereas this distinction might not be possible from EM images of dry samples (Tiede et al., 2008, 2010). Nevertheless, NP agglomeration and its distinction from aggregation might be of primary importance in determining the fate of NPs and their effects in living organisms. Agglomeration of NPs in different media is a widely studied phenomena because it has been shown to affect NP toxicity (Fede et al., 2012). In the literature, there are two main sample preparation approaches that are regularly used when attempting to minimise the possibility of NP agglomeration occurring. For example, sedimentation by ultracentrifugation has been used to quantify viruses, hydrocolloids and NPs because it allows the uniform coating of a sample onto an EM grid (Baalousha, Prasad, & Lead, 2014; Bergh, Borsheim, Bratbak, & Heldal, 1989; Bettarel, Sime-Ngando, Amblard, & Laveran, 2000; Lienemann, Heissenberger, Leppard, & Perret, 1998; Zheng, Webb, Greenfield, & Reid, 1996). This method has been referred to by several authors as “unperturbed”; i.e., it does not introduce artefacts to the sample (Baalousha et al., 2014; Chanudet & Filella, 2006; Hagendorfer et al., 2012; Lienemann et al., 1998). Adsorption is the other method that has been widely used to prepare NP samples with little or no apparent agglomeration for EM analysis (Kaiser & Watters, 2007a, 2007b, 2007c). In most related studies, a coating agent is used to attract NPs and immobilise them by adsorption on the surface of an EM substrate. Adsorption protocols often vary between studies and the effect of differences such as the concentration and type of coating agent and the rinsing of the coated substrate on EM samples has not been widely studied. In this study, the principle aim is to develop procedures to allow the accurate measurement of NP aggregates. The initial stages involve the evaluation of the two abovementioned protocols that claim to provide “unperturbed” data from naturally aggregated NP systems. Next, mathematical methods to support the characterisation process are developed and tested. It is hoped that gaining a better understanding of the factors that affect these procedures will assist other users to develop protocols to obtain accurate data from their own NP/matrix systems. To our knowledge, this paper presents the first study that focuses on the assessment of suitable sample preparation protocols for the measurement of NP aggregates by EM. Experimental EM images of samples and stock dispersions used in the study are provided in the Supplementary material. Experimental design To evaluate sample preparation methods for presence of artefacts, we used a sample of multimodal spherical silica (SiO2 Poly). Protocols that we chose for the evaluation included sedimentation and four variants of adsorption. The results of this evaluation

are included in the Supplementary material because of the large amount of data collected. We found that neither of the applied methods was artefact free, with the prominent artefacts being agglomeration and/or a skewed size distribution towards small particles, i.e., a skewing artefact. The causes of the skewing artefact are currently not fully understood and would benefit from further research. However, when this artefact is present, it results in the overestimation of the number of small NPs compared with that of large NPs. In the second part of the study, as presented below and summarised schematically in Fig. 1, we focused on the development of a mathematical refinement for the sample preparation method where aggregation was absent/least prominent, but the skewing artefact was present (see Fig. 1(a)). The determination of the refinement factor (f) was based on Eq. (1). The f value for silica NPs was derived using the reference method gas-phase electrophoretic molecular mobility analysis (GEMMA, also known as nano-electrospray differential mobility analysis), as shown in Fig. 1(b). It is important to note that both GEMMA and EM provide the size of dry NPs. This makes them prone to certain artefacts if specific considerations during sample preparation are not in place; e.g., for GEMMA, the sample preparation requires removal of nonvolatile dissolved substances that would otherwise create crystals, which could not then be distinguished from the analysed NPs. f =

Relative number of NPs counted on EM images . (1) Relative number of NPs counted by the reference method

The obtained f value was used to correct the size distribution of SAS which, as shown in our previous study (Dudkiewicz, Wagner et al., 2015), was strongly affected by the skewing artefact. To determine the efficacy of the developed refinement, we compared the refined size distribution of SAS against that determined by GEMMA (see Fig. 1(c)). This comparison was achieved by applying a uniform measurement expression, the mass equivalent diameter (MED), introduced in our previous study (Dudkiewicz, Wagner et al., 2015). Furthermore, to test if calculated f values could be used to refine the size distribution of other polydisperse NPs, we repeated the determination of f for multimodal spherical citrate-coated gold NPs (Au Poly) using centrifugal liquid sedimentation (CLS) as a reference method to obtain sizing data in the liquid phase. The choice of the two reference methods used in this study was influenced by the nature of the samples. Both methods had previously been shown to provide accurate estimates of NP number concentrations and excellent resolution of NP populations in multimodal systems (Anderson, Kozak, Coleman, Jämting, & Trau, 2013; Contado, 2017; Dudkiewicz, Wagner et al., 2015; Jeon, Oberreit, Schooneveld, & Hogan, 2016; Kesten, Reineking, & Porstendörfer, 1991; Supplementary material). In addition, CLS does not require sample preparation for NP suspensions (thereby decreasing the risk of producing potential artefacts) and has a shorter analysis time than GEMMA. CLS detects NPs based on their interaction with light, making it a good method of choice for Au-based NPs, which efficiently scatter and absorb light. Characterisation of silica-based NPs by any technique using light-based detection is challenging (Dudkiewicz, Wagner et al., 2015). Thus, GEMMA was used for the characterisation of these NPs. Spherical silica A polymodal sample of SiO2 Poly was obtained by mixing two dispersions of spherical silica: K12 (0.04% w/w) and K80 (3.5% w/w) in borate buffer at pH 8.0 (0.05 M H3 BO3 , 0.05 M KCl, 0.004 M NaOH; BB8). Both K12 (commercial name: Klebosol 30V12) and K80 (commercial name: Klebosol 30V50) were kindly provided by AZ Electronics Materials (Trosly-Breuil, France) and were characterised by six different methods in our previous work (Dudkiewicz,

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Fig. 1. Schematic representation of the approach used to achieve skewing artefact refinement in the present study: (a) determination of the skewing artefact by comparison of electron microscopy (EM) and reference method measurements, (b) use of data from (a) to model the refinement factor f against NP size, and (c) use of f to refine the size distribution of aggregates and comparison with the reference method upon conversion to of mass equivalent diameter (MED).

Wagner et al., 2015). The K80 dispersion had a bimodal size distribution with one particle population size range between 28 and 62 nm and the other between 70 and 106 nm. The size distribution of K12 was monomodal with a particle size range between 12 and 50 nm. Both stock dispersions contained 30% w/w silica. SiO2 Poly was diluted with BB8 to give a total silica concentration of 1.8 × 10−2 % w/w prior to preparation for EM. Synthetic amorphous silica nanoparticles The aggregated NP selected for this study was synthetic amorphous silica (SAS) because of its widespread use in the food and pharmaceutical industries (Dekkers et al., 2011). The size distribution of these aggregates is of interest because SAS is approved for direct human consumption (Dekkers et al., 2011; Peters et al., 2012). The SAS dispersion was provided by the JRC Institute of Materials and Measurement (Geel, Belgium). Detailed characterisation of the dispersion stability and particle size distribution was provided in previous publications (Dudkiewicz, Boxall et al., 2015; Dudkiewicz, Wagner et al., 2015; Grombe et al., 2014). The size distribution of SAS was broad, with the main population of aggregates and primary particles with MEDs ranging from ∼8 to 100 nm. Large aggregates of a few hundred nanometres were also present but comprised <1% of all measured particles by number (Dudkiewicz, Boxall et al., 2015). In this study, the pH of the SAS stock dispersion and diluted sample was slightly alkaline (8.2 and 8.0, respectively), indicating that the particles carried a negative charge (Gun’ko, Zarko, Leboda, & Chibowski, 2001). Citrate-coated gold nanoparticles Citrate-coated Au NPs with five different nominal sizes (10, 15, 20, 30, and 50 nm) were purchased from BB International (Cardiff, UK). These five dispersions were characterised in-house by transmission electron microscopy (TEM) and mixed with each other in

deionised water to obtain a dispersion with polymodal size distribution (Au Poly). The size and concentration of Au NPs in the stock dispersions as well as the concentration of Au in the Au Poly sample are given in Table 1. Sample preparation protocol for electron microscopy Standard polyvinyl formal-carbon coated TEM grids (Agar Scientific, Stansted, UK) were used to prepare all samples for imaging by scanning electron microscopy (SEM) and TEM. The selected sample preparation protocol is referred to in the Supplementary material as T2 Gel and was based on adsorption of NPs to a TEM grid coated with freshly prepared solution of 0.1% gelatin from porcine skin type A (G6144-100G, Sigma Aldrich, Gillingham, UK). This protocol involved: (1) (2) (3) (4) (5)

Floating the TEM grid on a drop of gelatin solution for 5 min. Rinsing the grid three times using a drop of deionised water. Placing the grid on the top of the sample drop for 2 min. Rinsing using two drops of deionised water. Removing excess moisture using filter paper following each rinsing and grid exposure step.

Electron microscopy and image analysis Images of SAS and SiO2 Poly were acquired using an FEI Sirion S field-emission gun SEM (FEI, Hillsboro, OR, USA). The SEM instrumental settings included a through-the-lens detector, beam setting of −5 kV and spot size 3. Prior to imaging, TEM grids with prepared samples were attached to aluminium SEM stubs with carbon tape. Stubs were then coated with Pt/Pd using a sputter coater (JFC-2300HR, JEOL, Tokyo, Japan) with a thickness controller (FC-TM20, JEOL). Coating improved the contrast of the silica NPs, which possibly also enhanced the precision of measurements. The thickness of the coating was determined and subtracted from the measurements as

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Table 1 Size and concentration data for Au NPs obtained from TEM and provided by the manufacturer. Au NPs nominal size (nm) Au NPs mean diameter measured by TEM (nm) Au concentration in stock dispersion (␮g/mL) Au concentration in Au Poly (␮g/mL) Particle number concentration in Au Polya (number × 1011 /mL) a

10 9.5 57.6 1.2 1.3

15 14.9 47.7 4.8 1.4

20 20.9 56.6 11.3 1.2

30 30.7 1090 54.5 1.9

50 48.6 853 170.6 1.5

Calculated based on the Au NP mean diameter measured by TEM and Au concentration in Au Poly.

described previously (Dudkiewicz, Boxall et al., 2015; Dudkiewicz, Wagner et al., 2015). A JEOL-JEM 2011 TEM operating at 200 kV (York JEOL Nanocentre) was used for the image acquisition of Au NPs in stock dispersions and Au Poly. For these samples, no sputter coating was applied. Samples were analysed in triplicate with the exception of Au Poly. For Au Poly, a greater measurement uncertainty was observed because of the smaller number of NPs per individual Au NP population found in the obtained images; therefore, the replicate number was increased to ten. All samples were imaged at relatively low magnification to visualise larger areas and capture a higher number of particles. Representative images can be found in the Supplementary material. Images were taken from randomly selected non-neighbouring areas in the centre of the grid to avoid places that were handled with tweezers. For SiO2 Poly, 20 images were taken per replicate with a micrograph size of 13.06 ␮m2 . For Au NP stock dispersions and Au Poly, 30 images were captured per replicate with a micrograph size of 0.41 ␮m2 . Measurements of SAS reported in a previous publication (Dudkiewicz, Wagner et al., 2015) were re-used in this study. The SEM measurement output was expressed as histograms with a bin width of 0.5 nm for SAS, Au Poly and Au NPs in stock dispersions and 2 nm for SiO2 Poly. All images were analysed using an eCognition Architect framework (version 8.7, Trimble- Sunnyvale, CA, USA) with object-based image analysis (OBIA) software. The experiments performed aimed to enable measurement of agglomeration artefacts and aggregated NPs in EM images. The specific OBIA approach used in this study, except for that of single NPs, also allowed the measurement of NP agglomerates and aggregates by merging particles with a common border despite the often strong contrasts at particle interfaces. Particle size measurements for spherical NPs were expressed as equivalent circle diameter (ECD). This measurement was obtained from OBIA. The size of SAS was expressed as MED, which was calculated based on ECD as described previously (Dudkiewicz, Wagner et al., 2015). For ideally spherical NPs, the MED was equal to ECD. The MED, although derived by calculation here, can be measured directly by single-particle inductively coupled plasma–mass spectroscopy. However, this technique suffers from high limits of detection for some elements like Si, preventing the accurate determination of the SAS size distribution (Dudkiewicz, Wagner et al., 2015). Reference methods The selected reference methods were GEMMA and CLS. These methods were chosen because, unlike standard light scattering methods, they are able to provide good resolution (separation of multiple size populations) in the measurement of spherical polymodal NPs (Anderson et al., 2013; Contado, 2017; Dudkiewicz, Wagner et al., 2015). Additionally, both CLS and GEMMA, as well as other related methods where a condensation particle counter is used for particle detection, allow accurate prediction of particle number concentrations (Anderson et al., 2013; Dudkiewicz, Wagner et al., 2015; Jeon et al., 2016; Kesten et al., 1991; Supplementary material). The principles of particle measurement by GEMMA and CLS have been described in detail elsewhere

(Anderson et al., 2013; Braun et al., 2011; Dudkiewicz, Boxall et al., 2015; Hinterwirth et al., 2013; Kallinger, Weiss, Lehner, Allmaier, & Szymanski, 2013; Weiss et al., 2012). Both of these methods, like the majority of available NP measurement methods, assume that the measured particles are spherical (Linsinger et al., 2012).

Gas-phase electrophoretic mobility molecular analysis GEMMA of SiO2 Poly and SAS was conducted using an instrument that has been described in detail in a previous publication (Bacher et al., 2001). GEMMA provides diameter measurements of dry NPs according to particle electrophoretic mobility. For spherical SiO2 Poly, electrophoretic mobility diameter (EMD) equalled the ECD determined from SEM. The characterisation of SAS was performed in a previous study (Dudkiewicz, Wagner et al., 2015), in which the algorithm that was used to produce the NP size distribution was also discussed. These data were re-used in the current publication. Characterisation of SiO2 Poly was performed using same settings as previously described (Dudkiewicz, Wagner et al., 2015). The SiO2 Poly stock dispersion required purification prior to GEMMA to remove, at least partially, hydrophobic contaminants that could clog the instrument’s capillaries. Also, dissolved nonvolatile small molecules required removal because the electrospray process would otherwise turn them into tiny crystals that could not then be distinguished from the measured NPs. While hydrophobic contaminants were not expected to be present in the sample (subject to the manufacturer’s recipe), dissolved non-volatile substances in the BB8 used to prepare SAS and SiO2 Poly for all analyses needed to be removed. To remove large particles (the instrument at given settings could deal with NPs with an EMD of up to 240 nm), the samples were centrifuged at 3745 × g for 5 min at 20 ◦ C, which induced sedimentation of particles with a diameter larger than 200 nm (Universal 30 RF, Hettich Zentrifugen, Newport Pagnell, UK). To remove dissolved substances, spin filtration was performed using Nanosep centrifugal filters (Pall, Port Washington, NY, US) with a molecular weight cut-off of 30 kDa based on a modified polyethersulfone membrane. The sample (60 ␮L) and ammonium acetate (340 ␮L, 0.02 M, pH 8; AA8) were applied to the membrane filter and centrifuged for 5 min at 14000 × g using a 1-14 Sigma table-top centrifuge (Osterode am Harz, Germany) until only about 60 ␮L of supernatant was left on top of the membrane. The eluate was discarded, AA8 (400 ␮L) was added and then the centrifugation step was repeated once more. The supernatant was pipetted into a vial and then AA8 was added to dilute the sample by a total of 500-fold. The sample was prepared for analysis in duplicate. The measurement of each replicate was composed of ten individual readings and the reported result was a median of these readings. To evaluate the effect of the spin filtration on the size distribution of SiO2 Poly, the samples were also analysed following a single spin filtration step. The process did not markedly change the SiO2 Poly size distribution; therefore, only results for samples prepared using the full preparation protocol are reported here. The original data output from the GEMMA instrument was a histogram with an exponentially increasing bin width. For the statistical comparison with SEM measurements of SiO2 Poly, the

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original output from GEMMA was recalculated into a histogram with a 2-nm bin width. Centrifugal liquid sedimentation CLS was conducted using a disc centrifuge (CPS DC24000, Analytic, Cambridge, UK) to characterise Au Poly and Au NPs stock dispersions. The instrument was operated at a maximum speed of 24 000 rpm. The analytical procedure settings were adjusted following the instrument manufacturer’s guidelines; namely, an Au NP density of 19.3 g/cm3 and refractive index of 0.47. The measured particle size range was set to 5–150 nm. The disc of the CLS was filled with a gradient of sucrose solution (8%–24%) and a layer of dodecane on the top to avoid gradient destabilisation through water evaporation. Calibration of the instrument was carried out prior to each measurement. Reference citrate-coated Au NPs (8012, NIST, Gaithersburg, MD, USA) with a nominal diameter of 30 nm were used as a calibration standard. The calibration settings were a half peak width of 6 nm and peak diameter of 27 nm (estimated based on Kaiser and Watters (2007b). Samples were analysed using five (Au NPs) and ten (Au Poly) replicates. The CLS instrument used light scattering and Mie theory to determine the NP concentration, while the NP size was determined based on their settling velocity according to Stoke’s law (CPS Instruments Inc., 2005). Data analysis Curve fitting to the experimental data was performed using the Solver add-in for Microsoft Excel. Results and discussion Refinement of the skewing artefact to allow measurement of SAS aggregates The size distribution of SiO2 Poly obtained by SEM and GEMMA is shown in Fig. 2. The sample featured four-modal size distribution, with three NP groups representing the majority of single NP populations, and the fourth group, representing the largest measured NPs, which was composed of agglomerates. The agglomeration was not necessarily related to the sample preparation, but could originate from sample/stock suspension storage prior to analysis (Petersen et al., 2014). The proportion of agglomerates with a particular size distribution was smaller when the sample was measured by EM than when measured by GEMMA (4% vs. 7%). In the same size distribution, the proportion of the smallest NP group was greater for the EM data (51%) compared with that obtained from GEMMA (26%). This suggests that the sample preparation process resulted in a prominent skewing artefact, but with little or no agglomeration. The size distributions of SiO2 Poly determined from GEMMA and EM (presented in Fig. 2) were used to obtain f values. These values were then plotted against SiO2 Poly diameter (Fig. 3). Fig. 3(a) contains f values calculated for NPs within each of the four distinct population groups plotted against the mean diameter of NPs in each group. Fig. 3(b) includes f values calculated every 2 nm, matching the bin size of histograms that were used to plot the size distribution curves shown in Figs. 2 and 3(b). The points in Fig. 3(a) clearly reveal that the relationship between SiO2 Poly size and f was non-linear. The size of a NP would influence its diffusion behaviour as well as the percentage of its surface area in contact with the grid. The dependencies of NP diffusion coefficient and percentage of the NP surface in contact with the EM grid coating on NP diameter should obey the power law, as shown in Fig. 4(a) and (b). Thus, if the skewing artefact was proportional to the NP diffusion coefficient or percentage of the NP surface in contact with the EM grid coating, then the relationship between f

Fig. 2. SEM image and size distribution of SiO2 Poly obtained from EM and GEMMA.

and size should also correspond to the power law. The power curve fit to the dependence of f on SiO2 Poly diameter had an R2 of 0.828 (Fig. 3(a)). Given that only four data points were used for this fitting, the power law was not considered to describe the relationship of f with NP diameter very well. This could mean that factors other than the percentage of a particle surface attached to the EM substrate and diffusion behaviour affected the skewing artefact. A much better result was obtained by fitting an exponential curve to the data points in Fig. 3(a), giving an R2 of 0.999. This fit also represented well the f values calculated for individual SiO2 Poly particle sizes grouped in 2-nm intervals, as shown in Fig. 3(b). The f values in Fig. 3(b) were broadly scattered around the fitting line in areas of the SiO2 Poly size distribution where proportionally fewer particles were measured. A small number of data points in these areas represented extremely high f values. For this reason, the curve fitted to f values calculated for the four data points in Fig. 3(a) was applied. It should be emphasised that the calculation of f for the four data points presented in Fig. 3(a) was based on a large number of particles, so the f values were expected to be accurate. Based on the exponential fit presented in Fig. 3(a), Eq. (2) describing the relationship of f with particle size was derived. f = 0.60 + 11.14e−0.11d ,

(2)

where e is a mathematical constant (2.71828) and f is the refinement factor as a quotient of the observed to actual relative number of particles, which depends on particle diameter (d). Eq. (2) was then used to calculate f values for individual particle sizes in the SEM SiO2 Poly sample prepared according to the T2 Gel protocol. Recorded particle numbers within the ECD distribution of SiO2 Poly were then divided by f to obtain the refined size distribution depicted in Fig. 3(b). This size distribution compared relatively well with that obtained from GEMMA. Good comparability of the size distributions of spherical silica NPs determined by SEM and

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Fig. 3. Exponential and power fits of f through SiO2 Poly size distribution: (a) f calculated for mean particle diameter for each of the four distinct size populations and (b) f calculated for diameters in 2-nm intervals as well as normalised SiO2 Poly size distributions obtained from GEMMA and SEM after refinement (particle percentage abundance divided by f).

by asymmetric flow field flow fractionation in our previous publication (Dudkiewicz, Wagner et al., 2015). We believe that this is the first report of an artefact-free MED distribution of SAS in dispersion, and therefore, has the greatest accuracy achieved to date. Transferability of the sample preparation refinement In Section “Refinement of the skewing artefact to allow measurement of SAS aggregates”, we developed an analytical procedure that allowed an artefact-free size distribution of silica NPs to be obtained. The relationship of f with silica NP size did not seem to strongly depend on diffusion coefficient or particle surface area. It was therefore clear that more research was required to determine what other particle properties could be contributing to the skewing artefact. To do this, we calculated f for NPs with different chemistry, i.e., Au Poly. Measurements of Au Poly and Au NP stock dispersions by both, TEM and CLS, are presented in the Supplementary material. The dependence of f on Au Poly diameter is plotted in Fig. 6. This dependence was well represented by the exponential curve (R2 = 0.990) described by Eq. (3). f = 0.64 + 1.50e−0.07d .

Fig. 4. Power law dependence of (a) diffusion coefficient and NP diameter, assuming that NPs were suspended in water at a temperature of 20 ◦ C and (b) percentage of NP surface in contact with the coating on the EM grid, assuming the thickness of the coating was 1 nm. Both dependencies were plotted for hypothetical NP diameters. In (a), diffusion coefficients were calculated based on the Stokes–Einstein equation D=

kb T , where D is diffusion coefficient, kb 3␲d

is the Boltzmann constant, T is absolute

temperature,  is a constant,  is the dynamic viscosity of the liquid, and d is particle diameter.

GEMMA was also reported in previous studies, in which the NPs featured relatively narrow size ranges compared with that of SiO2 Poly (Dudkiewicz, Wagner et al., 2015; Tuoriniemi et al., 2014). The MED distribution of SAS was refined in the same way; the results are presented in Fig. 5. The refinement changed the MED distribution of SAS from strongly skewed towards small MED values to a Gaussian-like distribution with a tail (see Fig. 5(a) and (b)). This MED distribution now compared well with the reference measurement (GEMMA, Fig. 5(d)). The largest difference of percentile MED between SEM and GEMMA was only 2 nm, which was much smaller than the 10 nm prior to refinement (see Fig. 5(c)). Moreover, the refined MED distribution did not include additional peaks related to the presence of contaminants/interfering substances that were observed in the GEMMA measurements (Fig. 5(a) and (b)) and also

(3)

It should be noted that the obtained Eq. (3) differed considerably from Eq. (2). The overestimation of the small particle size fraction was larger for the case of SiO2 Poly (at d = 20 nm, f = 1.83) than that of Au Poly (at d = 20 nm, f = 1.01), implying that not only size, but also NP chemistry can affect f. The surface chemistry of citrate-coated Au Poly and NaOH-stabilised SiO2 Poly and SAS should differ because these particle types should carry different surface charges. Based on the electronegativity differences of bonds with oxygen at the NP surface (electronegativity difference on the Pauling scale for an Si O bond at the surface of SiO2 NPs is 1.6 eV and that of a C O bond of citrate is 0.9 eV), the surface of SiO2 NPs should carry a greater negative charge compared to that of Au Poly NPs. This means that SiO2 NPs interact more strongly with the positively charged amino group of gelatin compared with the citrate-coated Au Poly NPs. Nevertheless, both Eqs. (2) and (3) and the graphs showing the dependence of f on particle diameter (especially Fig. 3(a)) suggest that the skewing artefact primarily affects NPs smaller than ∼50 nm. This further suggests that if the sample does not contain a fraction of particles smaller than 50 nm, the adsorption method could provide accurate size information without the need for refinement. Problems with the transferability of a single sample preparation method to chemically different NPs were also noted previously by other authors. Baalousha et al. (2014) showed that the agglomeration state of Au NPs prepared by sedimentation for atomic force microscopy imaging varied depending on the type of stabilising

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Fig. 5. Size distribution of SAS determined by GEMMA and SEM: (a) direct data output expressed as MED, (b) data output after application of the refinement, and (c) and (d) respective cumulative frequency distributions.

tailored to the specific sample/analytical purpose. Both methods required some adaptation of the NP dispersing matrix, indicating that their applicability would be restricted to matrices in which NPs were stable. Therefore, in studies where authors seek to determine the agglomeration state of NPs with different chemistries, and/or in specific matrices, the application of these methods would require careful consideration. Conclusions

Fig. 6. Dependence of f on Au Poly diameter.

agent on the NP surface (citrate or polyvinyl pyrrolidone), the functionalisation of the microscopy substrate (poly-l-lysine), and/or the presence of an agent (CaCl2 ) added to improve NP recovery in Au NP dispersions. Baalousha and co-workers focused on the measurement of the recovery and concentration of spherical NPs with relatively narrow size distributions. Therefore, the materials they used and the aim of their study were slightly different to those presented here. Nevertheless, both studies encountered a similar challenge: the need to adapt the method for NPs with different chemistry. Comparing the two sample preparation methods of sedimentation and adsorption based on the data presented here and reported by Baalousha et al. (2014), it was noticed that both methods had certain advantages and limitations, making them suitable for slightly different analytical purposes. The adsorption approach using the T2 Gel protocol presented here featured an apparent lack of agglomeration of NPs with very different chemistries (silica and citrate-coated Au NPs). Furthermore, in conjunction with the refinement approach, it allowed the accurate measurement of SAS, which could not be achieved by sedimentation because this method was shown to cause agglomeration of silica-based NPs (see Supplementary material). The sedimentation method presented by Baalousha et al. (2014) required a much lower concentration of NPs in a sample compared to that needed for the adsorption procedure and allowed recovery/concentration calculations to be performed. The advantages and limitations of both sample preparation protocols suggest that the appropriate choice of protocol should be

This study assessed the possibility of the accurate measurement of SAS using high-vacuum EM. All the sample preparation techniques evaluated, even ones regarded in the literature as “unperturbed”, introduced agglomeration or a skewing artefact, thereby hampering the accurate determination of the size distribution of NP aggregates (or NPs with a broad size distribution). To address the issue of agglomeration, an attempt was made to refine the skewing artefact. The full analytical process of the measurement of SAS by EM consisted of four stages: (1) Sample preparation by a specific protocol (T2 Gel) and measurement of aggregates in EM images using dedicated analytical software. (2) Determination of fractal characteristics of the aggregates to enable transformation of the size distribution from ECD to MED (presented in a previous publication (Dudkiewicz, Wagner et al., 2015). (3) Determination of f using spherical NPs featuring a polymodal size distribution and the same chemistry as the aggregated NPs (for SAS, we used SiO2 Poly; for NPs with different surface chemistry, f would be different, as demonstrated here for citrate-coated Au NPs). (4) Use of f to refine the size distribution of aggregates. Using the developed analytical procedure, the MED distributions of SAS obtained by SEM and the reference method became comparable. This is the first report of an artefact-free protocol for the determination of the accurate size distribution of NP aggregates. For the sample preparation method presented here, the skewing

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artefact strongly affected NPs with a size of <50 nm. Thus, refinement would not be necessary particles with a size of >50 nm. Future research effort should be focused on the simplification of the analytical approach reported here and could include determination of the relationship between NP surface chemistry and/or charge and f. Determination of such a relationship would eliminate the need for the labour-intensive stage 3 of the analytical procedure. Additional testing of the fractal geometry-based MED transformations (see Dudkiewicz, Wagner et al., 2015) is also advised, because this novel approach has not yet been tested on other types of NPs besides SAS. Conflict of interest There are no conflicts of interest to declare. Acknowledgements The authors acknowledge directors and staff of The University of York’s JEOL Nanocentre for technical help and facilitating access to the electron microscopes. We also thank Dr Thomas Linsinger from the Joint Research Centre, Directorate F — Health, Consumers and Reference Materials, Belgium for his professional opinion on the content of the manuscript. This work received financial support from EU Seventh Framework Programme NanoLyse (FP7/20072013) under grant agreement no. 245162, as well as the Food Standards Agency, UK. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.partic.2018.05.007. References Anderson, W., Kozak, D., Coleman, V. A., Jämting, Å. K., & Trau, M. (2013). A comparative study of submicron particle sizing platforms: Accuracy, precision and resolution analysis of polydisperse particle size distributions. Journal of Colloid and Interface Science, 405, 322–330. Baalousha, M., Prasad, A., & Lead, J. R. (2014). Quantitative measurement of the nanoparticle size and number concentration from liquid suspensions by atomic force microscopy. Environmental Science: Processes & Impacts, 16, 1338–1347. Bacher, G., Szymanski, W. W., Kaufman, S. L., Zöllner, P., Blaas, D., & Allmaier, G. (2001). Charge-reduced nano electrospray ionization combined with differential mobility analysis of peptides, proteins, glycoproteins, noncovalent protein complexes and viruses. Journal of Mass Spectrometry, 36, 1038–1052. Bergh, O., Borsheim, K. Y., Bratbak, G., & Heldal, M. (1989). High abundance of viruses found in aquatic environments. Nature, 340, 467–468. Bettarel, Y., Sime-Ngando, T., Amblard, C., & Laveran, H. (2000). A comparison of methods for counting viruses in aquatic systems. Applied and Environmental Microbiology, 66, 2283–2289. Braun, A., Couteau, O., Franks, K., Kestens, V., Roebben, G., Lamberty, A., et al. (2011). Validation of dynamic light scattering and centrifugal liquid sedimentation methods for nanoparticle characterisation. Advanced Powder Technology, 22, 766–770. Calzolai, L., Gilliland, D., & Rossi, F. (2012). Measuring nanoparticles size distribution in food and consumer products: A review. Food Additives & Contaminants: Part A, 29, 1183–1193. Carter, S., Fisher, A., Garcia, R., Gibson, B., Marshall, J., & Whiteside, I. (2016). Atomic spectrometry update: Review of advances in the analysis of metals, chemicals and functional materials. Journal of Analytical Atomic Spectrometry, 31, 2114–2164. Chanudet, V., & Filella, M. (2006). A non-perturbing scheme for the mineralogical characterization and quantification of inorganic colloids in natural waters. Environmental Science & Technology, 40, 5045–5051. Contado, C. (2017). Field flow fractionation techniques to explore the “nano-world”. Analytical and Bioanalytical Chemistry, 409, 2501–2518. CPS Instruments Inc. (2005). CPS disc centrifuge operating manual. CPS Instruments (Accessed 31 July 2013), from http://www.cpsinstruments.eu/pdf/Manual.pdf Dekkers, S., Krystek, P., Peters, R. J. B., Lankveld, D. P., Bokkers, B. G., van HoevenArentzen, P. H., et al. (2011). Presence and risks of nanosilica in food products. Nanotoxicology, 5, 393–405. Dudkiewicz, A., Boxall, A. B. A., Chaudhry, Q., Molhave, K., Tiede, K., Hofmann, P., et al. (2015). Uncertainties of size measurements in electron microscopy characterization of nanomaterials in foods. Food Chemistry, 176, 472–479.

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