Determination of Nanoparticle Size Using a Flow Particle-Tracking

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Determination of Nanoparticle Size Using Flow Particle Tracking Method Yusuke Matsuura, Ayako Nakamura, and Haruhisa Kato Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b00249 • Publication Date (Web): 23 Feb 2018 Downloaded from http://pubs.acs.org on February 23, 2018

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Analytical Chemistry

Determination of Nanoparticle Size Using Flow Particle Tracking Method

Yusuke Matsuura, Ayako Nakamura, and Haruhisa Kato* National Metrology Institute of Japan, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 5, Higashi 1-1-1, Tsukuba, Ibaraki 305-8565, Japan E-mail: [email protected]

RECEIVED DATE (to be automatically inserted after your manuscript is accepted if required according to the journal that you are submitting your paper to)

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Abstract: We developed a novel method to determine the mean size of nanoparticles under flow conditions, the flow particle tracking (FPT) method. The liquid particle counting method is commonly utilized to determine the number-based size under flow conditions by converting the light scattering intensity of individual particles to the size using the relationship between the size and light scattering intensity of a size standard material; however, the determined size depends strongly on the type of size standard material. In contrast, the developed FPT method can reliably determine the mean size of nanoparticles under flow conditions according to the Stokes–Einstein assumption by observing the Brownian motion of individual particles; therefore, this method does not require a calibration step using a size standard and can be applied to any type of material. To reliably size particles under flow conditions, we determined the flow velocity profile in a sample cell by extracting only the flow velocity from the particle motion. After determining the self-diffusion coefficient of each particle and subtracting the effect of the flow velocity, we successfully obtained a reliable mean size. The developed method could contribute to application of microchannel reaction/synthesis devices using nanomaterials.

KEYWORDS Size, flow velocity profile, particle, flow particle tracking


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1. Introduction It is essential to monitor the size of products in research and process management of nanomaterials because the physical and chemical characteristics of materials are usually affected by their size.1–7 For example, metal nanoparticles utilized as catalysis for oxygen reduction must have a large surface area to afford high catalytic activity and sensitivity. Because the number of nanomaterial production techniques using microstructured flow channels has increased recently,8,9 techniques for online monitoring of the nanomaterial size are expected to be very attractive approaches to detecting nanomaterial agglomeration and foreign substances for quality control of the products. The light scattering liquid-borne particle counter (LSLPC) method is commonly used under flow conditions as an online method of sizing nanoparticles,10–14 where the intensity of light scattered by each nanoparticle is converted to the corresponding size calibrated by size standard materials. In the LSLPC system, a laser beam irradiates the flow channel, and a detector receives light scattered by the nanoparticles when it passes across the irradiated space. In this method, the size of each particle is determined using the relationship between the light scattering signal magnitude and corresponding refractive index of materials assuming spherical structure. Although the benefit of LSLPC is the high throughput counting speed to determine the particle number concentration in liquid; however, because the light scattering intensity depends on the material (refractive index) as well as the particle size, the size obtained by the LSLPC system is strongly affected by the type of material composing the particles used to calibrate the system. Thus, the size determined by the LSLPC method is not reliable if the standard and target materials are different, e.g. different kinds of materials existed in liquid phase. In this study, we improved the particle tracking analysis (PTA) method to reliably determine the size of nanoparticles under flow conditions. PTA is a sizing technique for determining the size of Brownian nanoparticles in the liquid phase.15–18 In a PTA system, individual nanoparticles are visualized as microsized bright spots resulting from the light they scatter, and the motion of the bright spots is captured by an optical microscope with a charge-coupled device (CCD) or complementary metal–oxide– semiconductor camera. After the Brownian motion of the particles is captured, the sizes of individual 3 ACS Paragon Plus Environment


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nanoparticles are determined by the following two-step analysis. First, the diffusion coefficient, D, of each nanoparticle is determined from the two-dimensional (2D) mean square displacement (MSD) in a certain time interval ∆t described as:

∆x 2 + ∆y 2 = 4 D∆t .

(1)

Second, the particle diameter, d, is then calculated from the coefficient using the Stokes–Einstein equation: d=

k BT , 3πηD

(2)

where kB is the Boltzmann constant, T is the absolute temperature, and η is the solvent viscosity. The PTA method can therefore determine the size of nanoparticles regardless of the type of material composing them since the diffusion coefficient is independent on the materials according to equation 2. The use of PTA in stop-flow mode for online sizing detection was reported recently;19 however; the need for stop-flow operation requires more complicated coupling between the online sizing and operation systems. The obtained mean size under flow conditions was obviously reduced with increasing flow rate. Then, stop-flow mode was required in online PTA assessment. In addition, new analytical software was released by the manufacture of the PTA instrument (NTA3.0, Malvern, England) and could be applied for stop-less PTA online measurement; however; this analytical software can be used at very slow flow rates, i.e., less than 0.01 mL/min,20 because the MSDs are calculated by subtracting the particle drift in the entire observation area, which appears as the effect of the flow velocity in this software. We examined size determination of 100 nm silica particles under various flow conditions using Nanosight NS300 and analytical software NTA 3.2 (Malvern, England) as shown in Figure 1. The error bars in the figure represent the combined standard uncertainties21 for particle size determination by PTA. As shown in figure 1, at higher than the flow velocity of 30 µm/s, the obtained size was smaller statistical significantly (approximately 20 % difference at 80 µm/s). This result indicated that sizing by the commercial PTA instrument is not sufficient to obtain accurate size of particles under flow conditions. In addition, when one performs PTA under flow conditions, one has to consider the flow ACS Paragon Plus Environment

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velocity profile in each position in the sample cell because the flow velocity profile should not be uniform. In this study, therefore, we established a novel particle tracking method, i.e., a flow particle tracking (FPT) method, to solve the problem of the effect of the flow velocity profile on the size obtained using the PTA principle. It is assumed that correcting the flow velocity by considering its profile allows online determination of the nanoparticle size in microstructured channels such as those in microfluidic reaction systems. The purpose of this study was to establish the novel PTA method (FPT method) under flow conditions. First, we determined the velocity profile of the liquid flow in the observation area using the particletracking velocimetry method22–24 to analyze the particle motion under the flow condition. The velocity profile was determined by assuming that the velocity of the tracers is equal to that of the flow. After determining the flow velocity profile, we calculated the corrected nanoparticle displacements by subtracting the flow velocity and then determined their sizes using the Stokes–Einstein assumption. According to the analytical algorithm used in our FPT method, both the size of the nanoparticles and the flow velocity profile are obtained simultaneously; therefore, it is not necessary to obtain the flow velocity profile in advance, indicating that our method has a strong advantage for application to online size measurement of nanoparticles. Second, to evaluate the FPT method, we compared the size determined by the FPT method with that determined by the non-flow PTA method. The size determined by the non-flow PTA method is expected to become smaller with increasing flow velocity, whereas the size determined by the FPT method would not vary with the flow conditions; therefore, we compared the size obtained by both methods under several flow rates. We also determined the size of nanoparticles of three different sizes and three different materials to confirm that the FPT method can determine the true size of nanoparticles regardless of the particle material, in contrast to the LSLPC method. The developed FPT method can reliably determine the mean size of nanoparticles in online flow channels and can be applied to nanoparticles of any material. Moreover, because the number-based size distribution of nanoparticles can be obtained using this method, this FPT method is expected to contribute to the improvement of nanomaterial research and development, i.e., inspection of ACS Paragon Plus Environment

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nanomaterial manufacturing processes and regulation of import/export of nanomaterials based on the number size distribution according to the European Commission, which defines a nanomaterial as “a natural, incidental, or manufactured material containing particles, in an unbound state or as an aggregate or as an agglomerate and where, for 50% or more of the particles in the number size distribution, one or more external dimensions is in the size range 1 nm–100 nm.”25

2. Experimental section

2.1 Samples

A surfactant-free aqueous dispersion of polystyrene latex (PSL100) was purchased from Fujikura Kasei Co. (Saitama, Japan); an aqueous dispersion of silica (Silica100) was purchased from Polysciences Inc. (Warrington, PA, US), and aqueous dispersions of gold (Gold30, Gold50, and Gold100) were purchased from Nanopartz Inc. (Loveland, CO, US). The suspensions were diluted with ultrapure water prepared using the Puric-ω system (Organo, Tokyo, Japan). Detailed information on the suspensions is shown in Table 1. The diameter values were determined by the respective suppliers using dynamic light scattering (DLS) or transmission electron microscopy (TEM).

2.2 FPT setup Figure 2a and 2b show an overview of the FPT system setup and its detailed structure, respectively. The flow channel was made of glasses and contained silicone rubber as a packing spacer. It had a hexagonal-plate-like sample space with a depth of 1 mm and a maximum width of 16 mm. The sample liquid was supplied from the inlet, flowed in the channel, and was discharged from the outlet using a syringe pump (Pump 11 Elite, Harvard Apparatus, Holliston, MA, US) at a constant flow rate of 0.01– 0.1 mL/min. To observe nanoparticles using light scattered from them, an incident beam from a 50-mW diode laser with a wavelength of 640 nm was used at the bottom of the channel, and an optical microscope (SP200XM, Brunel Microscope Ltd., England) was employed at its top to observe light 6 ACS Paragon Plus Environment

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scattered from individual particles; the microscope included an objective lens with a magnification of 20×, a relay lens adapter with a magnification of 3×, and a CCD image sensor (Marlin F-033B, Allied Vision, Germany). The number of effective pixels in the CCD sensor was 659 × 494. The size of the pixels was calibrated by the manufacturer to be 172 nm, and the observable volume was 110 µm × 84 µm × 20 µm. Light scattered from the nanoparticles was captured as bright spots for a duration of 60 s at a recording frame rate of 30 fps (frames per second). The exposure time of the CCD was varied depending on the dispersion because the scattered light intensity depended on the particle size and type of material. It was 30 ms for Gold30, Gold50, and Silica100, 6 ms for PSL100, and 2 ms for Gold100. More than 300 particles were used for calculation of mean size of particles.

3. Results and Discussion

3.1 Investigation of conventional PTA under flow condition We captured the motion of bright spots of light scattered from individual particles under laser irradiation and flow conditions instead of directly observing the motion of the corresponding nanoparticles. Figure 3 shows an example image of bright spots attributed to light scattered by the particles and their trajectories in the entire observation area at a flow rate of 0.05 mL/min using the 100nm gold particle suspension. We observed that the Brownian spots were transported in the flow direction. The size of the spots was approximately 2 µm, which differs from the actual particle size and makes it easier to determine the position of the spots compared to direct observation of individual particles. First, we performed non-flow PTA under flow conditions using PSL100. The system calculated the displacements of each bright spot trajectory between two frames and determined the particle size from them according to the Stokes–Einstein assumption. Figure 4 shows the mean size of the particles at ACS Paragon Plus Environment

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various flow rates obtained using the non-flow PTA algorithm. Although the calculated size in the nonflow condition was 91.5 nm, which is consistent with the official value in Table 1, the apparent mean sizes clearly shifted with increasing flow rate. The 2D MSD under flow conditions is determined by the following equation, which replaces equation 1:

∆x 2 + ∆y 2 = 4 D∆t + v 2 ∆t 2 ,

(3)

where v is the 2D flow velocity;26 therefore, in figure 4, we also described the apparent mean size, which is theoretically estimated as

d apparent =

d , 1 + kQ 2

(4)

where Q is the mean flow velocity, and k is a constant value. Because the experimental results fall on the theoretical curve in figure 4, the shift of the calculated mean size is caused by the effect of the flow velocity. Obviously, this result indicated that it is necessary to correct for the flow effect to reliably determine the particle size using PTA under flow conditions.

3.2 Protocol for determining particle size by FPT method In the FPT method, we estimated the 2D velocity vector profile in the entire area captured by the CCD camera using nanoparticle tracking velocimetry method23 as a linear function of the position r, v = c + Ar ,

(5)

using the 2D vector c and the 2 × 2 matrix A as fitting parameters. The parameters were determined by using the least-squares method, i.e., by minimizing the sum of the differences between the displacement of a bright spot in the time interval ∆t between two frames (33 ms in this study) and the drift caused by the flow velocity, which is denoted as v∆t. Figure 5 shows a flow velocity vector profile in the entire observing area at a flow rate of 0.05 mL/min, which appears to be a uniform flow profile.


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Next, we calculated the MSDs of particles by considering the obtained correction of the flow velocity profile. In the following procedure, we focus on an individual particle trajectory. The number N is defined as follows: The bright spots of the particle were captured in N + 1 images, and ri (i = 0, 1,…, N) represents the particle position vector in each frame. The displacement vector Ri (i = 1, 2,…, N) corrected for the effect of the flow velocity is calculated as

R i = ∆ri − v i ∆t = ri − ri −1 − v i ∆t ,

(6)

r +r  v i = c + A i i −1  , (7)  2  where ∆ri = ri − ri-1 is the actual displacement from ri−1 to ri, and vi is the flow velocity vector at the position halfway between ri and ri−1. We obtained the 2D MSD as

MSD =

1 M

∑R

2 i

,

(8)

i

where we extracted M vectors of an Ri series randomly from all N vectors; we set M to 20 in all the following results. After that, we determined the diffusion coefficient and the size of the particle according to the Stokes–Einstein assumption; incidentally, we considered the error due to the finite exposure time of the camera. When the bright spot is imaged at an exposure time τ, the MSD is related to D as

τ  MSD = 4 D ∆t −  , (9) 3  which replaces equation 1;27,28 therefore, we calculated d using the following equation:

d=

4k BT  τ  ∆t −  . 3πη ⋅ MSD  3

(10)


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Figure 6 shows a flowchart of this FPT procedure. Because the flow velocity depends on the observation position in the flow channel in many cases, it is important to determine both the flow velocity profile and size using the FPT method.

3.3 Size determination of nanoparticle suspensions by FPT method We determined the size of the PSL100 sample at various flow velocities (0–40 µm/s) using the FPT procedure. Figure 7 shows the mean sizes of the PSL100 particles under flow conditions obtained using our FPT method, in which we calculated the combined standard uncertainties of the size determination according to the literature24 and represented them by error bars. In contrast to the sizes determined by the non-flow PTA method, which were affected by the flow velocity and became smaller according to equation 4, as shown in figure 4, the calculated mean sizes under the flow velocities shown in figure 7 were considered to be equivalent to the official value of 92.4 nm within the standard uncertainties. Therefore, it is obvious that the FPT method can reliably determine the particle sizes under flow conditions. Next, we present the results of the FPT method for the Gold30, Gold50, Gold100, and Silica100 samples. Table 2 shows the obtained mean sizes of the samples under a non-flow condition and at a flow velocity of 30 µm/s. For each particle, the obtained mean size under flow conditions was equal to that under the non-flow condition within the standard uncertainty. In addition, the FTP method yielded mean particle sizes of approximately 100 nm for the three roughly 100-nm materials, in agreement with the official values; namely, the observed values were 93.2 ± 3.1 nm (PSL100, where the official value is 92.4 nm), 103.7 ± 5.2 nm (Silica100), and 96.4 ± 4.2 nm (Gold100). This result indicates that the FPT method can be used to reliably determine the size of nanoparticles regardless of their composition, in contrast to the LSLPC method. This is an obvious advantage of the FPT method for monitoring the particle size in online process characterization.

3.4 Size determination under asymmetrical flow profile condition ACS Paragon Plus Environment

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To make it clear the necessity of considering 2D flow velocity profile, not mean flow velocity, we performed the size determination using FPT method under asymmetrical flow profile condition, since this case would be occurred real measurement system. Figure 8 shows observed 2D flow velocity profile in the observation area captured by the CCD sensor at a flow rate of 0.1 mL/min. In contrast to figure 5, the 2D flow velocity profile shown in figure 8 are obviously spatial-gradient; the values of the difference between slowest and fastest flow velocity in the entire observation area is approximately 4 times. Figure 9 shows the size of the PSL100 sample under the asymmetrical flow conditions at mean flow velocities of 0 – 25 µm/s, determined with non-corrected flow effect (filled circles), determined by analytical software NTA 3.2 (filled squares), and the result using our investigated FPT method (open circles). As shown in figure 9, non-corrected result indicated the determined particle size is dramatically changed being dependent on the flow velocity. In contrast to this result, the result by analytical software NTA 3.2 gave better values than that by non-correction; however, the determined values were deviated from true particle size at faster flow velocities than 10 µm/s. Compared to those two results, the determined sizes at various flow rates using our investigated FPT method considering 2D flow velocity profile successfully gave the appropriate size of particles being independent on the flow rates. Figure 9 therefore represented our investigate FPT methods considering 2D flow velocity profile can reliably determine particle size under various flow conditions such as particle transportation in microstructured channels.

4. Conclusion We developed the FPT method, which is based on the PTA principle, to reliably determine the size of nanoparticles under flow conditions. To reliably determine the sizes after correcting for the effect of the flow velocity to estimate the Brownian self-diffusion coefficients under flow conditions, we simultaneously determined both the flow velocity profile and mean particle size by analyzing the motion of bright spots resulting from light scattering by the particles after laser irradiation. This method can ACS Paragon Plus Environment

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determine number-based mean particle sizes in an online flow channel for any type of material without calibration by a particle size standard; therefore, it should play an important role in improving nanomaterial development and inspection of nanomaterials for process control. Acknowledgments

The financial support of the Nanotechnology Material Metrology Project of the New Energy and Industrial Technology Development Organization is gratefully acknowledged.


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(14) International Standardization Organization. ISO 21501-2; Geneva, 2007. (15) Filipe, V.; Hawe, A.; Jiskoot, W. Pharm. Res. 2010, 27, 796–810. (16) Dragovic, R.A.; Gardiner, C.; Brooks, A.S.; Tannetta, D.S.; Ferguson, D.J.P.; Hole, P.; Carr, B.; Redman, C.W.G.; Harris, A.L.; Dobson, P.J.; Harrison, P.; Sargent, I.L. Nanomedicine 2011, 7, 780– 788. (17) Patrick, H.; Sillence, K.; Hannell, C.; Maguire, C.M.; Roesslein, M.; Suarez, G.; Capracotta, S.; Magdolenova, Z.; Horev-Azaria, L.; Dybowska, A.; Cooke, L.; Haase, A.; Contal, S.; Manø, S.; Vennemann, A.; Sauvain, J.J.; Staunton, K.C.; Anguissola, S.; Luch, A.; Dusinska, M.; Korenstein, R.; Gutleb, A.C.; Wiemann, M.; Prina-Mello, A.; Riediker, M.; Wick, P. J. Nanopart. Res. 2013, 15, 2101. (18) Kato, H.; Ouchi, N.; Nakamura, A. Powder Technology, 2017, 315, 68–72. (19) Bartczak, D.; Vincent, P.; Goenaga-Infante, H. Anal. Chem. 2015, 87, 5482–5485. (20) Tong, M.; Brown, O.S.; Stone, P.R.; Cree, L.M.; Chamleya, L.W. Placenta 2016, 38, 29–32. (21) Matsuura, Y.; Ouchi, N.; Banno, H.; Nakamura, A.; Kato, H. Colloids Surf., A 2017, 525, 7–12. (22) Williams, S.J.; Park, C.; Wereley, S.T. Microfluid. Nanofluid. 2010, 8, 709–726. (23) Sato, Y.; Inaba, S.; Hishida, K.; Maeda, M. Exp. Fluids 2003, 35, 167–177. (24) Matsuura, Y.; Nakamura, A.; Kato, H. Sensors and Actuators B: Chemical, 2018, 256, 1078– 1085. (25) Commission Recommendation on the definition of a nanomaterial. European Commission Website. Available online: http://ec.europa.eu/environment/chemicals/nanotech/faq/definition_en.htm (accessed on April 25, 2017). (26) Qian, H.; Sheetz, M.P.; Elson, E.L. Biophys. J. 1991, 60, 910–921.


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Figure and table captions Figure 1. Plot of flow velocities vs. size of silica gel nanoparticles with a diameter of 100 nm obtained by PTA method. The results were obtained by using Nanosight NS300 instrument and an analytical software NTA 3.2 (Malvern, England). The error bars represent the uncertainties for particle size determination by PTA.

Figure 2. Schematic view of FPT. (a) Overview of the measurement setup. (b) Detailed channel structure. The channel has a hexagonal-plate-like sample space. The sample liquid was supplied from the inlet, flowed in the channel, and was discharged from the outlet. A laser beam is incident at the bottom of the channel. Light scattered from particles is observed by an optical microscope and a CCD camera.

Figure 3. Image of bright spots and their trajectory captured by the CCD camera. Particles are observed as micron-sized bright spots, and the size of the spots is approximately 2 µm. The bright spots are transported by the flow with Brownian motion.

Figure 4. Mean particle sizes under flow conditions obtained by non-corrected flow effect. The mean size under flow conditions tends to become smaller than its actual value with increasing flow velocity.

Figure 5. Velocity profile in the entire observing area at a flow rate of 0.05 mL/min. It appears to be a uniform flow profile, and the spatially averaged velocity is 28.9 µm/s. The displacements of bright spots using the flow velocity profile calculation was approximately 25,000.


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Figure 6. Flowchart of the FPT method applied in this study to obtain individual particle sizes.

Figure 7. Mean sizes of PSL100 at 0 and 0.01–0.07 mL/min obtained by the FPT method; error bars represent standard deviations. The size values were equal to each other and agreed well with the official value, 92.4 nm.

Figure 8. Velocity profile near the bottom of the flow channel in the larger observation area than in figure 5 at a flow rate of 0.1 mL/min. The flow profile is asymmetrical, and the values of the difference between slowest and fastest flow velocity in the observation area is approximately 4 times. The mean flow velocity is 22.3 µm/s. The displacements of bright spots using the flow velocity profile calculation was approximately 150,000.

Figure 9. Mean sizes of PSL100 at 0 – 25 µm/s obtained by non-corrected flow effect (filled circles), determined by analytical software NTA 3.2 (filled squares), and the result using our investigated FPT method (open circles).


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Table 1. Detailed information on the five suspensions used in this study. The diameter values were determined by the respective suppliers using DLS or TEM. Sample name

Material

Commercial name

Supplier

Specified diameter [nm]

Gold100 Gold50 Gold30 PSL100 Silica100

Gold Gold Gold Polystyrene latex Silica gel

A11-100-CIT A11-50-CIT A11-30-CIT FK-NA100RD Silica Microspheres Cat#24041

Nanopartz Inc. Nanopartz Inc. Nanopartz Inc. Fujikura Kasei Co. Polysciences Inc.

100 50 30 92.4 100

Table 2. Mean sizes of Gold100, Gold50, Gold30, PSL100, and Silica100 obtained using the FPT method.

Sample

Size in non-flow [nm]

Size at 0.05 mL/min [nm]

Gold100 Gold50 Gold30 PSL100 Silica100

97.2±4.4 53.6±2.6 38.8±1.8 90.7±2.9 105.4±5.3

96.4±4.2 52.8±2.4 38.7±1.9 93.2±3.1 103.7±5.2


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Figure 1.


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Figure 2.


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Figure 3.


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Figure 4.


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Figure 5.


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Figure 6.


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Figure 7.


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Figure 8.


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Figure 9.


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Determination of Nanoparticle Size Using a Flow Particle-Tracking

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