Exposure assessment of nanosized engineered agglomerates and aggregates using Nuclepore filter

Transcription

1 J Nanopart Res (213) 15:1955 DOI 1.17/s RESEARCH PAPER Exposure assessment of nanosized engineered agglomerates and aggregates using Nuclepore filter Sheng-Chieh Chen Jing Wang Heinz Fissan David Y. H. Pui Received: 15 May 213 / Accepted: 19 August 213 Ó Springer Science+Business Media Dordrecht 213 Abstract Nuclepore filter collection with subsequent electron microscopy analysis for nanosized agglomerates (2 5 nm in mobility diameter) was carried out to examine the feasibility of the method to assess the personal engineered nanoparticle exposure. The number distribution of the nanoparticles collected on the filter surface was obtained by visual counting and converted to the distribution in the air using validated capillary tube models. The model was validated by studying the overall s of S.-C. Chen D. Y. H. Pui (&) Particle Technology Laboratory, Mechanical Engineering, University of Minnesota, 111 Church St., S.E., Minneapolis, MN 55455, USA dyhpui@umn.edu S.-C. Chen chens@umn.edu J. Wang Institute of Environmental Engineering, ETH Zurich, Wolfgang-Pauli-Str. 15, 893 Zurich, Switzerland J. Wang Analytical Chemistry, Empa, Ueberlandstrasse 129, 86 Dübendorf, Switzerland H. Fissan Institute of Energy and Environmental Technology (IUTA) e.v., Bliersheimer Str. 6, Duisburg, Germany H. Fissan Center for Nanointegration Duisburg-Essen (CeNIDE), Forsthausweg 2, Building LH, 4757 Duisburg, Germany nanoparticles (Ag and soot) with different agglomeration degrees through 1 lm pore diameter Nuclepore filters at different face velocities (2 15 cm/s). In the model, the effects of the maximum length of agglomerates on interception deposition and the dynamic shape factor on impaction deposition were taken into account. Results showed that the data of the overall were in very good agreement with the properly applied models. A good agreement of filter surface collection between the validated model and the SEM analysis of this study was obtained, indicating a correct particle number distribution in the air can be converted from the Nuclepore filter surface collection and this method can be applied for quantitative engineered nanoparticle exposure assessment. Keywords Engineered aggregates and agglomerates Exposure assessment Nuclepore filter Capillary tube model Nanoparticle size distribution Fractal dimension Introduction Engineered nanoparticles often exhibit unique physicochemical properties which impart specific characteristics in engineered nanomaterials. However, only limited knowledge is known about what effect these properties may have on human health. Researchers are continuously trying to clarify these effects on human exposure to nanoparticles using various methods,

2 Page 2 of 15 J Nanopart Res (213) 15:1955 including in vivo and in vitro tests (NIOSH 25, 21; Oberdörster et al. 1994; Porter et al. 21; Shvedova et al. 28; Tran et al. 1999; Wang et al. 211a), laboratory simulated exposure measurement (Ogura et al. 29; Tsai et al. 29), and workplace exposure measurement (Fissan and Horn 213; Kuhlbusch et al. 211; NIOSH 29, 21, 211; Tsai et al. 211, 212; Wang et al. 212). Although discrepancies were found between the results (dose response) from the in vivo and in vitro methods, data have shown that the physicochemical characteristics of the nanoparticles could influence their effects in biological systems (Jiang et al. 28; Powers et al. 27; Shvedova et al. 25; Warheit et al. 24). These characteristics included particle size, shape, surface area, chemical properties, charge, solubility, oxidant generation potential as well as degree of agglomeration (or fractal dimension) (NIOSH 29). Besides, some studies have indicated that mass and bulk chemistry were less important than particle size, surface area, and surface chemistry of the nanoparticles (Oberdörster et al. 1994; NIOSH 25). Therefore, how to best characterize exposures using these different exposure metrics for engineered nanoparticles is a present research focus. Workplace personal exposure assessment is attracting increasing attention because data from real work conditions can be obtained, which can also provide information for toxicity tests (in vivo and in vitro tests) and validate the laboratory simulated results. In our previous study (Chen et al. 213), Nuclepore filter collection with subsequently electron microscopy analysis has been validated to be a method that can provide the particle size distribution of personal exposure (Cyrs et al. 21). Meanwhile, the chemical composition of the collected particles can be analyzed by the Energy Dispersive Spectroscopy method. The number distribution of spherical nanoparticles collected on the filter surface was counted visually and converted to the distribution in the air using the modified Spurny model, which was applicable when the inertia parameter, I, was less than 1.. The parameter I was calculated as: I ¼ 2 9 q qd p q ReP; Re ¼ m ; ð1þ where q p is density of particle, q is density of medium, Re Reynolds number of flow approaching a pore, P is filter porosity, q is face velocity, D ¼ p r ffiffi, r P is pore radius, and m is the kinematic viscosity of the flow. However, the method was only valid for spherical particles and many of the released particles have elongated shapes such as nanotubes, chain-like or branch-like agglomerates. For example, such particles may be released from carbon nanotube manufacturing, welding process (Friedlander and Pui 24), nanopodwer manufacturing (Pratsinis 1998; Hülser et al. 211), and so on. The characteristic of elongated particles across filters is different from those of spheres with the same mobility diameter due to the different dynamics (Kim et al. 29a; Wang 213; Wang et al. 211b, c). Theoretical background The modified Spurny model is able to predict very well for spherical particles when the inertia parameter is kept\1. (Chen et al. 213; Manton 1978; Spurny et al. 1969). Hereafter, the modified Spurny model is referred to as Spurny-sphere model. The details of particle collection efficiency modeling can be found elsewhere (Chen et al. 213), and here we focus on the specific parts responsible for the difference between agglomerates and spheres. When an agglomerate and a spherical particle have the same mobility diameter (d m ), their deposition efficiency in Nuclepore filters due to the pore diffusion and surface diffusion (Eqs. 5 and 1 of Chen et al. 213) will be the same, since they have the same diffusivity, which is the only variable parameter of the particle in those equations and is defined as: D ¼ ktb; ð2þ where k is Boltzmann s constant, T is the absolute temperature, and B is the particle mobility. There may exist slight differences between the diffusivities, because the mobility sizes are measured in a differential mobility analyzer (DMA), and the orientation of an elongated particle in a DMA could be different from that in the flow through a filter (Wang et al. 211b). However this only affects high-aspect-ratio particles and the effects are small (Wang et al. 211b), thus we ignore this difference in the present study. In contrast, depositions of agglomerates and spheres with the same mobility diameter are substantially different due to different impaction and interception which are dependent on the shape effect on drag force and effective interception length.

3 J Nanopart Res (213) 15:1955 Page 3 of 15 The efficiency of impaction, e i, was calculated as (Spurny et al. 1969): e i ¼ 2e i 1 þ n e2 i ð1 þ nþ 2 ; ð3þ where e i is a function of the Stokes number, Stk, and n is a function of the filter porosity, P. The e i and n are defined as: e i ¼ 2Stk pffiffi n þ 2Stk 2 1 n exp p Stk ffiffi 2Stk 2 n ð4þ n pffiffiffi P n ¼ p 1 ffiffiffi ð5þ P The Stk for agglomerates should be corrected by the corresponding dynamic shape factor, v, to account for the effect of shape on particle motion for different agglomeration degrees of the agglomerates (Cheng et al. 1991; Kelly and McMurry 1992; Kim et al. 29a). The modified Stk and v were defined as: Stk ¼ ðd mþ 2 q p Cðd m Þq ; ð6þ 18lr v v ¼ d mecðd m Þ d m Cd ð me Þ ; ð7þ where C(d m ) is the Cunningham slip correction factor of particles with mobility-size d m, l is gas viscosity, and d me is the mass equivalent diameter, i.e., the diameter of a sphere having the same mass as that of the agglomerate. Kim et al. (29a) measured the dynamic shape factor for Ag agglomerates with open structures and Ag aggregates partially sintered at 2 C using the DMA-APM (Aerosol Particle Mass Analyzer) method. Results were shown in their Fig. 12. For practical purpose, we developed quartic polynomial equations for calculating the v of the two types of particles based on the data. For the open agglomerates, the v for particles within the size range 2 4 nm was found to be: v ¼ 2: dm 4 þ 6: dm 3 5: dm 2 þ 2: d m þ :81: ð8þ The v for the open agglomerates in the ranges \2 nm and [4 nm was equal to 1. and 4.1, respectively. The v for the aggregates within the size range 3 3 nm was: v ¼ 7: dm 4 þ 6: dm 3 1: dm 2 þ 2:7 1 2 d m þ :37: ð9þ The v for the aggregates in the ranges\3 nm and [3 nm was equal to 1. and 2.4, respectively. Kim et al. (29a) also found that the mass mobility-size fractal dimension, D fm, of the open agglomerates and aggregates was 2.7 and 2.25, respectively. D fm was defined as (Park et al. 24): m ¼ Ad D fm m ; ð1þ where m is the mass of the agglomerate measured by APM and A is a prefactor. The authors pointed out that the exponent D fm was often presented in aerosol literature as a reference fractal dimension such as in Park et al. (24) and Eggersdorfer et al. (212). The maximum projected length, L max, of an agglomerate was larger than its mobility diameter and should be used as the effective interception length for the interception deposition (Kim et al. 29a, b). By TEM analysis, Kim et al. (29a) found the maximum length of Ag agglomerates and aggregates had a power law relationship with particle mobility diameter as: L max ¼ bdm c ; ð11þ where c was for Ag agglomerates with D fm = 2.7 and 1.88 for Ag aggregates with D fm = 2.25, and the corresponding values of b were.63 and 1.47, respectively. Hereafter the empirical Eq. (11) is referred to as the Kim model. In addition to the Kim model, Vainshtein and Shapiro (25) determined the maximum length (or outer diameter) of an agglomerate by calculating the ratio (drag coefficient) of the actual drag on the agglomerate to the Stokes drag on a sphere of equal maximum length. Hereafter, the model is referred to as the Shapiro model. The authors simplified the calculation by assuming the agglomerate has a spherically symmetric porous structure. Then the drag coefficient can relate the maximum length of the agglomerate to d m, primary sphere size (d s ), and the fractal dimension based on the maximum length (D fl ) which is calculated as: N s ¼ L DfL max ; ð12þ d s where N s is the number of primary particles in an agglomerates. With d m, d s, and D fl known, L max then

4 Page 4 of 15 J Nanopart Res (213) 15:1955 can be solved implicitly. Our previous study showed that the ratio of D fm to D fl was around 1.2 for open Ag agglomerates (Shin et al. 29); therefore, the L max of agglomerates with a certain D fm can be calculated by the Shapiro model accordingly using the ratio of 1.2. This study used the value 1.2 for the both Ag open agglomerates and aggregates. The interception efficiency of the open agglomerates and aggregates, e R, through the Nuclepore filters can be calculated as (Spurny et al. 1969): e R ¼ N R ð2 N R Þ; ð13þ where N R ¼ L max =2r. In this study, Ag and soot agglomerates with different fractal dimensions were generated and tested for their s through 1 lm pore diameter Nuclepore filters at different face velocities. Particles collected on the filter surface were also analyzed by SEM to differentiate the depositions due to different mechanisms. The data of overall and SEM results were compared with the Spurny model combined with the agglomerate L max models including the Kim model and Shapiro model, whereby the effect of dynamic shape factor of agglomerates on impaction deposition and the effect of maximum length on interception deposition were taken into account. The combined Spurny model and agglomerate L max models is referred to as the Spurny-agglomerate model in this study. The objective of this study is to demonstrate the new model can give rise to the airborne particle size distribution of agglomerates accurately from the SEM analysis of filter surface collection. The final goal is to show the Nuclepore filter collection with the subsequent SEM analysis can be used not only to assess the personal exposure to spherical particles but also to engineered and combustion-generated chainlike and branch-like nanoparticles present in both workplaces and ambient environments. Materials and methods Filters and test conditions The filter and test condition used were almost the same as those in Chen et al. (213). Briefly, the Nuclepore filters used in this study were 47 mm Whatman Ò - Track-Etched Polycarbonate Membrane Filters with 1 lm pore diameter. The porosity and thickness of the filter were.16 ( pores/cm 2 ) and 11 lm, respectively. The TSI particle size selector was used as the filter holder to mount the Nuclepore filters and test the of agglomerates. In the tests, 2, 3.5, 5, 7.5, 1, and 15 cm/s face velocities were used, which corresponded to 1.4, 2.4, 3.4, 5.1, 6.8, and 1.2 L/min flow rates, respectively. Ag agglomerates experiments To evaluate the applicability of the Spurny-agglomerate model, we conducted the tests for monodisperse Ag agglomerates with different agglomeration degrees and soot agglomerates through the 1 lm Nuclepore filters. Figures 1 and 2 show the schematic diagram of the generation and test system of the Ag open agglomerates, aggregates, and spheres and soot agglomerates, respectively. A high purity silver rod (ESPI Metals, Ashland, OR, USA) was placed in a ceramic boat in the generation furnace and Nitrogen gas was used as the carrier gas with 1.5 L/min flow rate. Silver was vaporized and condensed into primary silver nanoparticles as the furnace was operated at 1,15 C. The average primary sphere size observed from TEM analysis of this study was 16.1 ± 2.7 nm, which was very close to the value 16.2 ± 3.1 nm obtained in our previous paper (Kim et al. 29a). These primary particles stick upon collision to form nanoparticle agglomerates in the agglomeration chamber located just downstream of the generation furnace. Agglomerate sintering was carried out in the second furnace with room temperature (RT, or not sintered), 2, and 6 C when the particle structure became more and more compact from an open structure at RT to spherelike at 6 C (Kim et al. 29a; Ku and Maynard 26; Shin et al. 29; Wang et al. 21). For reference, the number median diameter (NMD) of RT, 2, and 6 C sintering Ag particles was around 95, 7, and 55 nm, respectively, from Scanning Mobility Particle Sizer (SMPS, Model 3936, TSI Inc., Shoreview, MN, USA) measurements. Monodisperse, singly charged Ag nanoparticles in the size range of 2 5 nm were generated by classifying the polydisperse particles using a long- DMA (Model 381, TSI Inc., Shoreview, MN, USA). The valve located immediately upstream of the DMA was used to restrict the aerosol flow rate in order to keep the ratio of sheath to the aerosol flow rate at 1.

5 J Nanopart Res (213) 15:1955 Page 5 of 15 Fig. 1 Setup of Ag agglomerates generation and test Generation Furnace Rotameter HEPA Filter Agglomeration Chamber Silver Sintering Furnace Neutralizer N 2 Po- 21 CPC Flow Meter TSI o C 2 o C 6 o C P Po-21 TSI 4143 Filter Holder Δ Make -up DMA Po-21 Agglomeration Chamber Dryer Cooler Diffusion Burner DMA Filter Holder ΔP Po-21 P Flow Meter TSI 4143 Make-up Rotameter CPC Fig. 2 Setup of soot agglomerates generation and test The aerosol flow rate needed to be reduced to less than 1 L/min, when the DMA classified particles with sizes larger than 3 nm. To avoid unwanted electrostatic effects on filtration, a Po-21 neutralizer was used to bring a charging equilibrium on the classified monodisperse Ag particles (Liu and Lee 1976). By adjusting the valve in front of the system pump (bottom-left corner) and the flow mode of the Condensation Particle Counter (CPC, Model 3775, TSI Inc., Shoreview, MN, USA) with low or high flow, the face velocities from 2 to 15 cm/s for the particle tests can be acquired. By switching the three-way valve located in front of the CPC inlet, the particle, P en, was obtained by measuring the average particle concentration upstream, C up, and downstream, C down, of the Nuclepore filter as: P en ¼ C down C up : ð14þ

6 Page 6 of 15 J Nanopart Res (213) 15:1955 Soot agglomerates experiments Soot agglomerates were also generated and tested to obtain more data of agglomerate through the Nuclepore filter. The same diffusion burner as that of Kim et al. (29b) was used, in which more details of the burner and the operation procedure can be found. Briefly, the bottom part of the burner was an aluminum cylinder composed of five concentric rings. The center ring (first ring) was not used in this study. Compressed N 2 was fed into the second ring as the stabilizer. Propane gas, served as the fuel, was fed into the third ring and compressed air was fed into the fourth and fifth rings as oxidant and sheath gas, respectively. The soot particles from the burner first went through a cooling tube and a diffusion dryer, then entered an agglomeration chamber. The chamber volume was 2 L, which provided a residence time of 2 min when the flow rate was 1 L/ min. Under this residence time, the NMD of the soot was about 11 nm. It is to be noted, a longer residence time enhances agglomeration growth and increases the NMD of the soot (Kim et al. 29b). The average primary sphere size of the soot agglomerates was 28 ± 7.7 nm (Kim et al. 29b), which was relatively larger than that of Ag agglomerates. By adjusting the valves in front of the system pump (or downstream the TSI flow meter) and on the pathway of the make-up air and using the low or high flow mode of the CPC, we controlled and achieved the flow rate of soot stream with 1 L/min and face velocities from 2 to 15 cm/s for the agglomerate tests. The classification and test methods for the soot agglomerates were the same as those for Ag particles. Ag and soot agglomerates generated and used for the test were examined by the scanning electron microscopy (SEM, JEOL 65, CharFac, University of Minnesota) to inspect their shapes and sizes. Penetration modeling and SEM analysis In order to examine whether the airborne particle number distribution of agglomerates can be accurately converted from the filter surface collection, four samples of monodisperse Ag particles with different agglomeration degrees and mobility sizes were generated, sampled on Nuclepore filters and analyzed by SEM to validate the applicability of the models. Two different face velocities with 2 and 5 cm/s were used in the sampling. These particles included the open agglomerates with d m of 8 and 2 nm, 2 C partly sintered aggregates with d m of 15 nm and 6 C sintered spheres with d m of 1 nm. The sampling time for these particles was 3 6 min, during which the effect of particle loading on change was negligible; since the overall was almost the same before and at the end of sampling. The methods of conducting the particle collection and the visual determination of the partial depositions contributed from surface diffusion, impaction, and interception mechanisms were the same as those in Chen et al. (213), where more details can be found. Briefly, Eqs. (15) and (16) were used to calculate how many particles, based on CPC measurement, would approach the analyzed area of a SEM image. N total ¼ V C up ; ð15þ 4:2 1 6 N SEM ¼ N total ; ð16þ 11:4 where N total is the total number of particles approaching the whole filtration area of the filter (11.4 cm 2 ), V is the total sampling air volume, and N SEM is the number of particles approaching a SEM analyzed area of cm 2, which is obtained by setting the magnification of the SEM image with 5,. The value was then compared with that by visual counting. Besides, the counted depositions were also compared with the modeling results of Eqs. (3) and (13) for impaction and interception depositions, respectively. In Eq. (3), the dynamic shape factor for agglomerates was taken into account. The Kim empirical model and Shapiro model were considered in Eq. (13) for the effective interception length. In addition, the surface diffusion deposition, model not shown here, was also compared with the model developed by Manton (1979) or Eq. (1) in Chen et al. (213). The Spurny model combined with the Kim model is referred to as the Spurny Kim model in this study. In contrast, if the Kim model is substituted by the Shapiro model, the model is referred to as the Spurny Shapiro model. Results and discussion Particle SEM images Figure 3 shows the SEM images of the polydisperse Ag (a, b) and soot (c, d) open agglomerates which were

7 J Nanopart Res (213) 15:1955 Page 7 of 15 generated and tested in the present study. The magnification of images (a), (b), (c), and (d) is 5,9, 1,9, 2,39, and 45,9, respectively. The comparisons of images (a) with (c) and (b) with (d) show the two agglomerates have similar shapes with chain-like and branch-like structures. In Kim et al. (29a, b), the D fm for the RT Ag and soot agglomerates was 2.7 ±.6 and 1.9 ±.1, respectively. The close values of D fm could be the reason that the two agglomerates are very similar in shapes. For the 2 C sintered Ag agglomerates, the D fm was found to be 2.25 (Kim et al. 29a). This study used Eq. (11) and the values of b and c for Ag open agglomerates to calculate the maximum length of the current soot agglomerates. This study also conducted the SEM analysis for the 2 C sintering Ag aggregates. As expected, the images show the aggregates with structural compactness in between open agglomerates and spheres. Experiments and modeling results In our previous study (Chen et al. 213), it has been shown that the Spurny-sphere model was applicable for spherical particles when the inertia parameter, I, was B1. Figure 4 shows an example that the model agrees with the data very well for Ag spheres through 1 lm pore diameter Nuclepore filter with 2 cm/s face velocity (I =.53). It also compares the s of Ag spheres, open agglomerates and aggregates between data and models. For the agglomerate and aggregate modeling, the ratio of D fm to D fl with 1.2 was used to convert the D fm in the Shapiro model calculations. Then the D fm of 2.25 and 2.7 corresponded to the D fl of 1.88 and 1.73, respectively. It is seen from the data, the s demonstrate substantial difference among the three different fractal dimension Ag particles, especially in large size ranges ([1 nm). The decreases with decreasing fractal dimension when the increasing L max of fractal agglomerates enhanced the deposition efficiency and decreased the. The significant differences between sphere and fractal agglomerates emphasize that a serious error could be made if one uses the sphere model to predict the of fractal agglomerates. By taking into account the L max calculated from Kim model and Shapiro model in the Spurny model for the enhancement of interception deposition, it is seen the Spurny Kim agglomerate model predicts a very close with the data for both D fm = 2.25 and 2.7 agglomerates. In contrast, the data depart from the Fig. 3 SEM images of agglomerates used in the tests. a, b Ag, c, d soot. The magnification of images a, b, c and d was 5,9, 1,9, 2,39, and 45,9, respectively a Ag c Soot b Ag d Soot

8 Page 8 of 15 J Nanopart Res (213) 15: Ag, pore diameter: 1 μm, face velocity: 2 cm/s Spurny-sphere (D fm =3.) Spurny-Shapiro (D fm =2.25 or D fl =1.88) Spurny-Kim (D fm =2.25) Spurny-Shapiro (D fm =2.7 or D fl =1.73) Spurny-Kim (D fm =2.7) D fm =3. D fm =2.25 D fm = Fig. 4 Comparison of particle between data (symbols) and models (curves) for different fractal dimension Ag particles Spurny Shapiro model significantly for particles larger than 2 nm when the model overestimates the s. This can be attributed to that the Shapiro model underestimates the L max of large particles. Table 1 shows the comparison of L max calculated by Kim empirical model and the Shapiro model. It is seen that both models predict a larger L max for D fm = 2.7 agglomerates than the D fm = 2.25 aggregates. The Shapiro model predicts a larger L max at smaller size ranges (B2 nm) than the Kim model, while the later has a larger L max at larger size ranges C2 nm) for both aggregates and open agglomerates. The two methods have close values for L max of particles with d m *15 nm for D fm = 2.7 agglomerates; for D fm = 2.25 aggregates, the two models give similar L max values for particles with d m *2 nm. The difference of L max between the two models increases with increasing particle sizes. The significant underestimation of L max by Shapiro model for particles larger than 5 nm could be attributed to the increasing incompatibility between the assumption of spherically symmetric porous structure of agglomerate in the Shapiro model and the chain-like and branch-like agglomerates with increasing size. We also examined the effect of the dynamic shape factor on the for the Spurny Kim model which is the more accurate model than the Spurny Shapiro model for both D fm = 2.7 and 2.25 fractal agglomerates. Results showed the would be decreased for only\2. % for both D fm = 2.25 and 2.7 agglomerates under 2 cm/s face velocity by comparing with the spheres (dynamic shape factor of 1) with mobility diameter from 3 to 1, nm. However, the decrease of can be as large as *5 % at a higher face velocity with 5 cm/s for the 2 nm D fm = 2.7 agglomerates and a larger decrease will occur with a further increased face velocity. Therefore, the dynamic shape factor should be taken into account in the modeling of Nuclepore filters. Figure 5 compares the Ag open agglomerate through 1 lm pore filer between data and Spurny Kim model at 2 15 cm/s face velocities. It is seen the s of the agglomerates increase with increasing face velocity for the same mobility diameter agglomerates. The Spurny Kim model is in very good agreement with data for face velocities cm/s, while they are in disagreement for face velocities of 1 and 15 cm/s. This could be attributed to that the high face velocity increased the value of I to be out of the model applicability. The corresponding I in the cases of 1 and 15 cm/s face velocities is 2.7 and 4., respectively, which is higher than the Table 1 Comparison of L max between Shapiro model and Kim empirical model d m (nm) Shapiro Kim Shapiro Kim L max (D fm = 2.7) L max (D fm = 2.7) L max (D fm = 2.25) L max (D fm = 2.25) , ,8 1, 1,62 3,88 1,4 2,45

9 J Nanopart Res (213) 15:1955 Page 9 of 15 Fig. 5 Comparison of particle between data (symbols) and Spurny Kim models (curves) for D fm = 2.7 open Ag agglomerates at different face velocities Ag agglomerates (D fm =2.7) pore diameter: 1 µm 15 cm/s 1 cm/s 7.5 cm/s 5. cm/s 3.5 cm/s 2. cm/s 15 cm/s Spurny-Kim 1 cm/s Spurny-Kim 7.5 cm/s Spurny-Kim 5. cm/s Spurny-Kim 3.5 cm/s Spurny-Kim 2. cm/s Spurny-Kim applicable I of *1.. It is interesting that the corresponding I in the cases of 5 and 7.5 cm/s face velocities is 1.4 and 2. which is also larger than 1. but the agreement between model and data is still good. This is because the supposed corresponding I at these face velocities is calculated using the material density of Ag with 1.5 g/cm 3. However, the effective density, q e, of Ag agglomerates is expected to be lower and also size dependent. That is, the corresponding I of the open agglomerates cannot be regarded as the same with spheres and a factor to account for the reduced density for the I calculation is required. The factor is the ratio of the effective density to the material density, q e /q p, which is also equal to the ratio of the real volume to the equivalent volume, (d me /d m ) 3, (Kelly and McMurry 1992). From Table 1 of Kim et al. (29a), it can be calculated that the corresponding d me value for 1, 2, and 3 nm d m open Ag agglomerates was about 58, 96, and 125 nm, respectively. Therefore, the corresponding effective density for the d m 1, 2, and 3 nm open Ag agglomerates are found to be 2., 1.2, and.8 g/cm 3, respectively. This indicates that the Spurny Kim model should be applicable for face velocity higher than 2 cm/s for the current agglomerates and aggregates. The agreement between data and model for face velocity up to 7.5 cm/s shown in Fig. 5 supports the above argument. The above calculation indicates that the inertia parameter I becomes a function of particle size for agglomerates or aggregates, due to the dependence of particle effective density on size. Thus, the criterion for the model applicability is not only determined by the face velocity, filter structure, and particle material, but also determined by the particle size with fractal particles. For small agglomerates/aggregates, the effective density is close to its material density, thus low face velocity values are required to give rise to low values of I, fulfilling the model applicability. This is the reason why there is a large difference between the data and Spurny Kim model for these small particles (2 5 nm) at 1 and 15 cm/s face velocities, as shown in Fig. 5. For large agglomerates/ aggregates, the effective density can be much lower than its material density, thus even high face velocity values may fulfill the model applicability. This explains the better agreement between the data and model for 1 and 15 cm/s with increasing particle size. Calculations for the I values of 1, 2, and 3 nm agglomerates indicate that the Spurny Kim model should be able to predict their very well even at 15 cm/s face velocities when the I are.77,.46, and.31, respectively. However, there still exists a noticeable discrepancy, which is discussed in the following section. In addition to the effect of applicability of the current model on the prediction for agglomerates, agglomerate alignment with the flow direction could be another reason that caused the disagreement between data and model for 1 and 15 cm/s face velocities. Once the alignment occurs, agglomerates approach the filter surface with a smaller projected width than the L max, leading to a higher. This hypothesis can be confirmed by the comparison of between Ag sphere and agglomerate for the face velocities from 2 to 15 cm/s,

10 Page 1 of 15 J Nanopart Res (213) 15:1955 as shown in Fig. 6. It is seen there is a significant difference between spheres and agglomerates for particles larger than 1 nm at relatively low face velocities ( cm/s). However, the two curves get closer and closer with increasing face velocity. They finally almost collapse together at 15 cm/s face velocity. Therefore, in order to have an accurate prediction of for agglomerates by Spurny Kim model, the face velocity should be kept lower than 7.5 cm/s to prevent the effect of alignment for the open agglomerates with D fm B 2.7. It is to be mentioned that the dramatic increase of for the Ag open agglomerates at high face velocity was not due to the solid particle bounce, since there was no bounce effect observed for the Ag spheres with the same high face velocity (Chen et al. 213). There is still a question if the agglomerates were broken up by impaction on the filter at high velocities. If they broke up, the small fragments may penetrate the filter and be detected by the downstream CPC, leading to increasing. However, Froeschke et al. (23) and Seipenbusch et al. (27) already found the impact fragmentation occurred for open Ag agglomerates only when the velocity was larger than one hundred times of the maximum face velocity of this study. Therefore, the increased for the open Ag agglomerated can only be attributed to the alignment effect. It is interesting to know if the current Spurny Kim model is applicable for different material agglomerates. Figure 7 compares the agglomerate of Ag and soot open agglomerates between the Spurny Kim mode and data. In the small particle size range (2 5 nm), the experimental of Ag is slightly higher than soot particles, which may be attributed to the more compact structure of Ag (D fm = 2.7) than soot particles (D fm = 1.9). For particles larger than 1 nm when impaction mechanism becomes stronger, the soot data generally show higher than the Ag agglomerates due to the lower density of soot. The model accounts for the lower density of soot but not the slight difference of.8.6 face velocity: 2. cm/s Ag spheres D fm=2.7 Ag agglomerates cm/sec cm/sec cm/sec.8 1 cm/sec.8 15 cm/sec Fig. 6 Comparison of particle between Ag sphere and D fm = 2.7 open Ag agglomerates with different face velocities from cm/s

11 J Nanopart Res (213) 15:1955 Page 11 of 15 the mass mobility-size fractal dimensions between Ag and soot, thus the modeling curves of soot are slightly higher than Ag agglomerates in the whole size range. As expected, the soot data show good agreement with the model at 2 and 5 cm/s face velocities and they are very close to those of the Ag open agglomerates. For the face velocities of 1 and 15 cm/s, as mentioned earlier, there is an alignment effect increasing of agglomerates. Based on the results shown in Fig. 7, it can be concluded that the Spurny Kim model can be also applied for predicting the of other material agglomerates with a similar mass mobility-size fractal dimension as the Ag and soot open agglomerates. As Thick curves: Soot_Spurny-Kim Thin curves: Ag_Spurny-Kim Solid symbol: Soot data Open symbols: Ag data 15 cm/s 1 cm/s 5. cm/s 2. cm/s 15 cm/s 1 cm/s 5. cm/s 2. cm/s Fig. 7 Comparison of agglomerate of Ag and soot open agglomerates between the Spurny Kim mode and data for face velocities from cm/s Fig. 8 SEM images of Ag particle deposition on filter surface, a 8 nm RT agglomerates with 5 cm/s face velocity, b 2 nm RT agglomerates with 5 cm/s, c 15 nm aggregates with 2 cm/s, and d 1 nm sphere with 2 cm/s a 8 nm Ag-RT_5 cm/s b 2 nm Ag-RT_5 cm/s c 15 nm Ag-2 o C_2 cm/s d 1 nm Ag-6 o C_2 cm/s

12 Page 12 of 15 J Nanopart Res (213) 15:1955 mentioned earlier, data of small open Ag particles (2 5 nm) departing from the Spurny Kim model was due to their high effective density and also the corresponding I. In comparison, the small soot agglomerates had relatively smaller effective densities and also the corresponding I than that of Ag. Therefore, the Spurny Kim model predicted a good for small soot agglomerates. Surface deposition of SEM analysis Figure 8 shows examples of Ag particle deposition images for (a) 8 nm open agglomerates at 5 cm/s face velocity, (b) 2 nm open agglomerates also at 5 cm/s face velocity, (c) 15 nm 2 C sintered aggregates at 2 cm/s face velocity, and (d) 1 nm sphere also at 2 cm/s face velocity. It is to be noted that in order to obtain statistically reliable results from the SEM images, the visual analysis was carried out on the images with 5, magnification for all four particles when a filter area containing about 7 pores could be examined. modeling results by the Spurny Kim model showed the individual of impaction, surface diffusion, and interception for the 8 nm RT agglomerates was 94, 82, and 7 %, respectively, and that for the 2 nm RT agglomerates was 88, 93, and 25 %, respectively. In comparison, the partial of impaction, surface diffusion, and interception for the 15 nm aggregates was 93, 85, and 52 %, respectively, and that for the 1 nm spheres was 93, 78, and 81 %, respectively. Table 2 summarizes the number of Ag particles collected on filter surface determined by the Spurny Kim and Spurny-sphere models and SEM analysis for the 8 and 2 nm RT agglomerates, 15 nm aggregates, and 1 nm spheres. The average upstream concentration of the filter, C up, determined from CPC, for the 8, 1, 15, and 2 nm particles was 4913 ± 383, 464 ± 28, 179 ± 32, and 175 ± 49 #/cm 3, respectively. The total number of particles approaching the whole filter was calculated by Eq. (13) and that approaching the area for SEM analysis was calculated by Eq. (14). Results showed that there were 368 ± 29, 77 ± 4, 49 ± 2, and 128 ± 4 particles of the 8 nm RT agglomerates, 1 nm spheres, 15 nm aggregates, and 2 nm RT agglomerates, respectively, approaching the area of the SEM image. By multiplying the surface deposition efficiency, 46 % (= 1 Table 2 Comparison of surface deposition determined by the Spurny Kim model, Spurny-sphere model and SEM analysis Difference between SEM and SMPS (%) Interception (#), by SEM Impaction? surface diffusion (#), by SEM Surface deposition (#), by model Particle size, face velocity and D fm approaching (#) C up (#/cm 3 ) Number of particles 8 nm (D fm = 2.7) 5 cm/s 4913 ± 383 Whole filter SEM image 368 ± 29 (368 ± 29) 9.46 = 169 ± 13 a 66 ± 1 88 ± nm (D fm = 3.) 2 cm/s 464 ± 28 Whole filter SEM image 77 ± 4 (77 ± 4) 9.41 = 31 ± 2 b 15 ± 4 18 ± nm (D fm = 2.25) 2 cm/s 179 ± 32 Whole filter SEM image 49 ± 2 (49 ± 2) 9.59 = 29 ± 1 a 1 ± 2 24 ± nm (Dfm = 2.7) 5 cm/s 175 ± 49 Whole filter SEM image 128 ± 4 (128 ± 4) 9.8 = 12 ± 3 a 16 ± 4 93 ± By Spurny Kim model By Spurny-sphere model a b

13 J Nanopart Res (213) 15:1955 Page 13 of ), 41 % (= ), 59 % (= ), and 8 % (= ) for the 8, 1, 15, and 2 nm Ag particles, respectively, there would be 169 ± 13, 32 ± 2, 28 ± 1, and 12 ± 3 Ag particles on the corresponding SEM images. In comparison, the surface deposition by visual analysis of SEM images showed (sum of the last two columns of Table 2) there were 154 ± 23, 33 ± 7, 34 ± 7, and 19 ± 16 Ag particles on the corresponding images. The relative difference of the surface collections determined by the two methods was less than 15 %. In addition, it was found the deposition due to impaction? diffusion (the second from last column of Table 1) or interception (the last column of Table 1) was also in agreement with the model individually. It is concluded that a very good agreement between the data and model was found for various filtration operation conditions in terms of face velocity, particle size, density (density of agglomerates is size dependent), and fractal dimension. The Spurny Kim and Spurny-sphere models were validated for predicting both the overall particle collection and partial surface collection of open agglomerates, aggregates, and spheres. By adopting the validated model and the counting method proposed by Cyrs et al. (21) (grouped particles into several size fractions), it is foreseeable a very accurate particle number distribution in the air can be calculated from the surface collection of Nuclepore filters for engineered nanoparticles with different agglomeration degrees, e.g., D fm = 2.7, 2.25, and 3.. It remains a research interest to study the Nuclepore filter of other engineered nanoparticles with different fractal dimensions than those of this study. Conclusions Penetration of Ag open agglomerates, aggregates and spheres, and soot agglomerates through Nuclepore filters was studied experimentally and the data were compared with modified models. Experimental results showed that there was a significant difference between spheres and open agglomerates of the same mobility-size, which might lead to a severe error in converting the surface deposition to the size distribution in the air if incorrect model was used. By taking into account the dynamic shape factor effect on impaction deposition and the effective interception length for the D fm = 2.7 and 2.25 Ag and D fm = 1.9 soot agglomerates, Spurny Kim model is in good agreement with data for face velocities of cm/s. One needs to keep the face velocity lower than 7.5 cm/s for keeping the inertia parameter within the range of the model applicability, and for preventing the agglomerate alignment effect for open agglomerates. The current results provide information to select an appropriate Nuclepore filter and filtration conditions for engineered nanoparticle exposure assessment study. With the data inversion method proposed here, Nuclepore filter sampling provides quantitative and reliable airborne size distributions for spherical and agglomerated particles. The following is a simple procedure of the sampling and data evaluation methods for the engineered nanoparticle exposure assessment by using the current 1 lm Nuclepore filter. (1) To check if the pressure drop of the clean 1 lm Nuclepore filters is within cm H 2 O by passing filtrated air with 5 cm/s face velocity (Chen et al. 213). It is to be noted that the pressure drop may be different if the filters are from different companies when the porosity and thickness may be different. (2) A flow rate that corresponds to a face velocity lower than 7.5 cm/s should be used to minimize the alignment effect of fractal engineered nanoparticles. (3) To plan a sampling time that allows obtaining an adequate amount of particle collection on filter surface. If the particle number concentration of the workplace is around 2, #/cm 3 and the number median diameter and geometric standard deviation are 1 nm and 1.8, respectively, there will be 3 5 particles observed near each pore in the SEM analysis when the sampling flow rate and sampling time are 3.4 L/min (5 cm/s) and 3 h. In a 5, magnification SEM image, there will be 2 3 particles which can be analyzed. (4) Assuming the collected particles are with known material and have a close shape and fractal dimension with the current Ag aggregates or open agglomerates, the particle mobility diameter can be calculated from the observed particle maximum length by using Eq. (9). (5) To group the collected particles into 5 1 size fractions based on the converted mobility diameter when more fractions can have a higher resolution (Cyrs et al. 21). (6) Dividing the number of particles counted at the filter surface (by impaction and surface diffusion, the two depositions can be

14 Page 14 of 15 J Nanopart Res (213) 15:1955 easily differentiated based on particle mobility diameter) or at the pore opening (by interception) with the modeling of individual mechanism. (7) By taking the flow rate and sampling time into account, particle number distribution in the air is then can be obtained. It is to be noted that the size distribution in the air may not be accurately represented by the current method for particles with much different shapes from the current aggregates and agglomerates (or with D fm in , and \1.9) and with mixture forms (including different fractal dimensions of particles). Nevertheless, after obtaining more information on particle maximum length and effective density for different fractal dimension engineered nanoparticles, the above exposure assessment method then can be applied and more accurate results can be obtained. Acknowledgments This work was supported by the NSF Grant (Award ID: ) on Real Time Measurement of Agglomerated or Aggregated Airborne Nanoparticles Released From a Manufacturing Process and Their Transport Characteristics. Financial support by the European Committee for Standardisation in the frame of mandate M/461 Standardisation activities regarding nanotechnologies and nanomaterials is acknowledged. The authors thank the support of members of the Center for Filtration Research: 3 M Corporation, Boeing Commercial Airplanes, Cummins Filtration Inc., Donaldson Company, Inc., Entegris, Inc., Hollingsworth & Vose Company, MANN? HUMMEL GMBH, MSP Corporation, Samsung Electronics Co., Ltd, Shigematsu Works Co., Ltd, TSI Inc., and W. L. Gore & Associates, Inc., and the affiliate member National Institute for Occupational Safety and Health (NIOSH). Parts of this work were carried out in the Characterization Facility, University of Minnesota, a member of the NSF-funded Materials Research Facilities Network ( via the MRSEC program. One of the authors, Heinz Fissan, acknowledges the support by the Institute of Energy and Environmental Technology (IUTA), Duisburg, Germany. References Chen SC, Wang J, Fissan H, Pui YHD (213) Use of Nuclepore filters for ambient and workplace nanoparticle exposure assessment spherical particles. Atmos Environ 77: Cheng MT, Xie GW, Fu TH, Shaw DT (1991) Filtration of ultrafine chain aggregate aerosols by Nuclepore filters. Aerosol Sci Technol 15:3 35 Cyrs WD, Boysen DA, Casuccio G, Lersch T, Peters TM (21) Nanoparticle collection efficiency of capillary pore membrane filters. J Aerosol Sci 41: Eggersdorfer ML, Kadau D, Herrmann HJ, Pratsinis SE (212) Aggregate morphology evolution by sintering: number and diameter of primary particles. 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