Performance check of particle size standards within and after shelf-life using differential mobility analyzer

Performance check of particle size standards within and after shelf-life using differential mobility analyzer

Author’s Accepted Manuscript Performance Check of Particle Size Standards within and after Shelf-life using Differential Mobility Analyzer Bighnaraj S...

2MB Sizes 0 Downloads 50 Views

Author’s Accepted Manuscript Performance Check of Particle Size Standards within and after Shelf-life using Differential Mobility Analyzer Bighnaraj Sarangi, Shankar G. Aggarwal, Prabhat K. Gupta www.elsevier.com/locate/jaerosci

PII: DOI: Reference:

S0021-8502(16)30075-1 http://dx.doi.org/10.1016/j.jaerosci.2016.10.002 AS5053

To appear in: Journal of Aerosol Science Received date: 3 March 2016 Revised date: 27 September 2016 Accepted date: 11 October 2016 Cite this article as: Bighnaraj Sarangi, Shankar G. Aggarwal and Prabhat K. Gupta, Performance Check of Particle Size Standards within and after Shelf-life using Differential Mobility Analyzer, Journal of Aerosol Science, http://dx.doi.org/10.1016/j.jaerosci.2016.10.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Performance Check of Particle Size Standards within and after Shelf-life using Differential Mobility Analyzer Bighnaraj Sarangi1,2, Shankar G. Aggarwal1,2*, Prabhat K. Gupta2

1

Academy of Scientific and Innovative Research (AcSIR), CSIR-National Physical Laboratory, New Delhi, 110012, India 2

CSIR-National Physical Laboratory, New Delhi 110012, India

(*E-mail: [email protected])

1

ABSTRACT

In aerosol research, differential mobility analyzer (DMA) is widely used for particle size distribution measurements. In general polystyrene latex (PSL) particle size standards are used to calibrate the DMAs. However, these standards are expensive, and their shelf-life is limited. Therefore in this work, we have used PSL particle standard of sizes 60 and 100 nm (SRM® 1964 and SRM® 1963a, respectively) and checked their performance within and after the shelf-life period for 3 consecutive years (2013 – 2015) using a calibrated DMA (i.e., longDMA, TSI 3081). Also the size results are compared using a reference DMA (i.e., nanoDMA, TSI 3085). Using both long- and nano-DMA, the detail results of standard particle size measurements and involved uncertainties are discussed in this paper. We observed a continuous increase in the count mean diameter (CMD) calculated from the size distribution of each standard particle size with the progress of time. Particle size measured for PSL 60 and 100 nm using long-DMA are (56.01±5.56) nm and (92.86±10.81) nm in 2013, (56.37±5.66) nm and (93.71±10.44) nm in 2014, and (57.42±5.84) nm and (94.79±9.68) nm in 2015, respectively. Whereas, these results when nano-DMA was used are (56.82±4.32) nm and (94.62±9.46) nm in 2014, and (58.08±4.98) nm and (95.15±9.12) nm in 2015, respectively. Uncertainty results show that the components which contribute significantly in total uncertainty (at k = 2) in CMD measurements are DMA sheath flow rate, DMA calibration, difference in certified and measured diameter, and variation in the ambient temperature and pressure. The size measurement results obtained from long-DMA and nano-DMA agreed well. Based on the measurement results, we suggest that the increasing trend of particle

sizes (CMDs) with the progress of time from 2013 to 2015 is possibly because of the coating of contaminant residues (present in the standard liquid suspension) on the surface of the PSL particles which is further observed by high resolution tunnelling electron microscope (HRTEM) images of particles. Particles standards are stable and well characterized more than two years even after their shelf-life within the uncertainty limits. Size performance results for PSL 60 nm using calibrated long- and reference nano-DMA show good agreement with the size value given in the certificate, however standard PSL 100 nm size is found to be underestimated by ~7%.

Keyword: Standard PSL particles, DMA size measurement, Uncertainty estimation

2

1. Introduction Particles in nanometer ranges are generally formed from chemical, physical, and biological processes, and have importance in diversified fields. An important physical parameter for characterizing the behaviour of these nanoparticles is the size. In atmospheric aerosol research particle size distribution measurement gives important information linked to particle growth, secondary aerosols, physical and chemical processes, climate and health effects, etc (Hinds, 1999; Nenes & Seinfeld, 2003; Pöschl, 2005; Sarangi et al., 2015). Although a number of instrumental techniques for measuring particle size distributions in submicron size range are available, scanning mobility particle sizers (SMPS) are routinely used for measuring atmospheric particle size distributions in laboratory as well as in the field experiments. Other techniques used for particle size measurements are transmission electron microscopy (TEM), scanning electron microscopy (SEM), atomic force microscopy (AFM), photon correlation spectroscopy (PCS), aerosol time of flight mass spectroscopy, etc (Posfai et al., 1998; Barkay et al., 2005; Dall’Osto et al., 2006; Nikjoo et al., 2012).

A SMPS system consists of electrostatic classifier built in impactor and neutralizer, a differential mobility analyzer (DMA) and coupled to a condensation particle counter (CPC) (Liu & Piu, 1974; McMurry, 2000; Hermann et al., 2007; Stolzenburg & McMurry, 2008). A SMPS receives particles through its impactor, where particles greater than a certain size get separated off. Particles then neutralized by bipolar charger and entered to the DMA. In DMA particles are

segregated in different size bins based on their electrical mobility (Knutson & Whitby, 1975; Keady et al., 1983; Flagan, 1998; Shimada et al., 2005). Further, particles are counted by CPC, where particles undergo condensational growth followed by their detection in the optics. The equation which relates the electrical mobility to the voltage V, sheath flow, and geometrical characteristics of the DMA (length L, and inner and outer tube diameters) is shown in Eq. (1) and the relationship between the mobility and the particle diameter known as Stokes-Einstein expression as shown in Eq. (2). ( (

)

(1)

)

(2)

3

Because of the wider use of DMA technique in particle sizing applications, the calibration of DMA is an important issue for accuracy in the measurement results. Generally, DMA calibration is done using particle size standards. These standard particles are different types, e.g. polystyrene latex (PSL), gold, silicon dioxide, carboxylate modified latex (CML), etc (Mulholland et al., 2006; Anumolu & Pease III, 2012; Kidd et al., 2014). A liquid suspension of standard particles in general is used for the calibration purposes. A diluted solution of these standards is aerosolised to get particles as particle source for DMA calibration.

Further, traceability of particle standard is an important issue for the calibration of DMA, and thus for the accurate and comparable measurement results. Several particle standards are commercially available with SI traceability through a national metrology institute (NMI). For example, National Institute of Standard and Technology (NIST), USA issues particle standards for a wide range of particle sizes including 100 nm (SRM® 1963), 0.3 μm (SRM® 1691), 1 μm (SRM® 1690), 3 μm (SRM® 1692), 10 μm (SRM® 1960), 30 μm (SRM® 1961), etc (Mulholland et al., 2006).These standards are mono-dispersed polystyrene spheres suspended in water at a mass fraction of 0.5 % to 1 %.

There are few more techniques which have been used for DMA calibration. For example, DMA can be calibrated via tandem DMA (TDMA) method which uses two DMA in series (Mun et al., 2011). First DMA (reference) select size and second one is calibrated against first DMA. Similarly, DMA can also be calibrated by comparison of its size results with the size measured by other instruments such as Aerodyne Aerosol Mass Spectrometer (AMS), atomic force microscopy (AFM), etc (Takegawa et al., 2005; Garnaes, 2011).

As compared to other techniques, calibration of DMA using particle standards is a convenient method because particle standards are commercially available (also with some National Metrology Institutes (NMIs)), calibration can be performed more frequent using the same standards, a small amount of standards are required for one time use, shifting of the DMA to other place is not required, etc. However, instead traceable particle standards are expensive and their shelf-life is limited. One of the possible reasons of particle limited shelf-life is contaminants in the standard suspension is coated on the surface of particles as the progress of their storage time. In the certificate of NIST (SRM® 1964 60 nm and SRM® 1963a 100

4

nm) PSL particle standards, it is motioned that this residue layer on the spheres may be 0.03 to 0.3 nm during the time of certification.

In this work we study the particle size variation of the standard particles while storing within and after the shelf-life period. We aerosolized these standards and measured the particle size with a calibrated long-DMA (TSI 3081) for 3 consecutive years (2013 – 2015). We have compared the measurement results with another DMA (i.e., nano-DMA, treated as reference DMA). The measurement and uncertainty involved in size of PSL standards using long- and nano-DMA are discussed here. The observed increasing trend of PSLs size with time is further confirmed using HRTEM images to see any possible coating.

2. Materials and Methods 2.1. PSL standards (60 and 100 nm)

In this study we used two polystyrene latex (PSL) particle standards, which were purchased from National Institute of Standard and Technology (NIST), USA of sizes 60 nm (SRM® 1964) and 100 nm (SRM® 1963a). These standards were first used in December 2012, and stored in refrigerator at (8±2) ºC prior and after the use. Proper care was taken while handling and storing the standards in accordance with instructions given in their certificates. The expiry dates of both of the standards, i.e. SRM® 1964 and SRM® 1963a given in the certificates are 31st December 2013. Whereas the certified modal sphere diameter and expanded uncertainty of SRM 1964 is 60.39±0.63 nm and the count mean diameter is 55.70 nm (uncertainty over mean diameter is not mentioned in the certificate). Similarly the modal sphere diameter and expanded uncertainty of SRM® 1963a is 101.8±1.1 nm and the count mean diameter is 100.6 nm (uncertainty over mean diameter is not mentioned in the certificate).

2.2. Experimental setup

The experimental setup used in this study is shown in Fig.1. For making the working particle solution, 1 ml of standard solution was mixed with ~20 ml of particle free ultra pure water (Fluka 14211-1L-F). This solution was aerosolized using a syringe pump (World Precision

5

Instruments INC, Serial Number116051) connected with atomizer (TSI 3076) using 5N purity N2 gas at 0.7 kg/cm2 pressure. The generated aerosol stream was passed through two diffusion dryers placed in series. This dried particle stream (RH <5%) was then introduced to scanning mobility particle sizer (SMPS). This SMPS consists of an electrostatic classifier (EC, TSI 3080, including an impactor (0.0457 cm, TSI 1502296) and Kr-85 bipolar charger (TSI 3077)), differential mobility analyzer (DMA, TSI 3081 or TSI 3085) and condensation particle counter (CPC, TSI 3788). Particle stream enters through inlet impactor and resulting particles get neutralized through bipolar charger (as per the Fuchs equilibrium charge distribution principle). Then this neutral aerosol stream enters to the DMA, where a varying voltage is applied to the DMA inner electrode so that according to the electrical mobility of particles, respective sized particles exit through the slit at the bottom of the DMA. These size segregated particles then enter to CPC, where particles undergo condensational growth while passing through saturated liquid vapor followed by their detection in optics.

In this study, we used long-DMA (TSI 3081) which was calibrated using PSL 80 particle standard ((count mean diameter with uncertainty 81±3 nm at k = 2 (3080A, Thermo Fisher Scientific)) received from Laboratoire National de Métrologie et d'essais (LNE), France while participating in Versailles Project on Advanced Materials and Standards (VAMAS) inter-comparison 2012. Particle size data obtained from long-DMA were compared with nano-DMA (TSI 3085) which is treated as reference DMA (certified by manufacturer with traceability linkage through NIST, USA). This DMA was used only for comparison purposes and not for routine measurements. Other parameters in the experiments were the tube length between DMA exit and inlet of CPC was 25 cm, the sample flow rate of SMPS was set to 0.3 lpm, and the sheath flow rate of SMPS was set to 3 lpm. Particle size range selected for longDMA was 14-661 nm, whereas for nano-DMA was 3-160 nm.

For comparison of particle size measured using DMA setup, we further sampled the standard PSL particles on carbon coated copper grids (200-mesh). The nominal pore size of 1.2 μm and nominal pore spacing of 1.3 μm (Quantifoil R1.2/1.3 on 200 mesh Cu, Electron Microscopy Sciences, Hatfield, PA, USA). The grid is fixed in a holder and placed before the impactor (see Fig.1). The particles were collected on grid for 30 min. For each standard, we have taken 4 samples. After sampling the grids were stored in a desiccator until high resolution tunnelling electron microscope (HRTEM, TECHNAI G2, TF30) imaging was done. 6

2.3. Particle size distribution and data analysis

The size distribution of PSL standards shows bimodal distribution, where first mode is residual part of liquid in which PSL particles are made suspended (possibly it is overlapped with residual of ultra pure water in which PSL standard is diluted) and the second mode is of standard PSL particles (Fig.2). For particle size distribution analysis, the mode peak observed near the certified size of standard particles is considered assuming negligible influence of the first mode peak (i.e. for PSL60 nm the mode observed in between 49 to 66 nm is selected (Fig.2a), and for PSL 100 nm the mode observed in between 82 to 109 nm (Fig.2b) is considered over a size range 14-661 nm (long-DMA) and 3-160 nm (nano-DMA) of SMPS (Sarangi et al., 2015)). Based on the particle size distribution obtained from SMPS, the observed count mean diameter (CMD) is represented the size peak of standard PSL particles. The experiment was repeated five times each in three different days of a year. Statistical tools such as count mean diameter (CMD), geometric mean diameter (GMD), average mode diameter (MD), repeatability and reproducibility are used to correctly represent the peak diameter of the size distribution. Following are the equations for CMD (dcmd) and GMD (dgmd) (Hinds, 1999): ∑

(3)

∑ ∑(

(

) ∑

)

(4)

where dpi is the particles mobility diameter of size bin i and Ni is the particles number concentrations of size bin i. Mode diameter (MD) of the size distribution was determined by employing curve fitting tool using IGOR Pro. Software (Serial number: 31085; Version 5.0.2.0).

2.4. Uncertainty estimation

We also performed detailed uncertainty estimation in the measurement of particle size using SMPS. The uncertainty due to different components such as repeatability, diffusion loss, multiple charges, aerosol flow rate, certified size of standards, DMA sheath flow rate, DMA inner electrode voltage, DMA calibration, difference in measured diameter and certified diameter, and variation in ambient temperature and pressure were included in the budget

7

estimation of the particle size measurement. These components are shown as cause effective diagram in Fig.3.

The combined standard uncertainty (propagation of the uncertainty, uc,i) can be defined as: √∑ ( ) where

(Taylor and Kuyatt, 1994)

(5)

is the standard uncertainty of the component i.

Each year three sets of combine standard uncertainty were estimated. Therefore pooled uncertainty estimation is followed to express the single combine uncertainty for each year. The pooled combine uncertainty ( (

√ where

,

)

and

(

)

) is calculated as (EURACHEM/CITAC Guide, 2012): (

)

(6)

are the combined standard uncertainty estimated from three sample

sets of CMDs and nc,1, nc,2 and nc,3 are the number of samples (= 6) taken to estimate the pooled uncertainties for each years (2013, 2014 and 2015).

Expanded uncertainty (

) at k (coverage factor, k is taken = 2 at 95% confidence level) is

then calculated using following equation: (7)

3. Results and Discussion There were two objectives of this study. (i) To check the changes in size of standard particles while storing within and after shelf-life. (ii) For better evaluating the results of change in particle size, it is important to estimate the associated uncertainty in the measurement, and therefore we have discussed in details uncertainty calculation of size measurements using SMPS.

3.1. Particle size measurements (mobility diameter)

For accurate size measurement, calibration of DMA is the key issue. Therefore we have used certified reference materials (CRMs) of polystyrene latex (PSL) particles of size (81±3) nm (3080A, Thermo Fisher Scientific) received from Laboratoire National de Métrologie et 8

d'essais (LNE), France while participating in “Versailles Project on Advanced Materials and Standards (VAMAS)” inter-comparison 2012 (Motzkus et al., 2013). The calibration of longDMA was done using proposed SMPS setup (Fig.1). Table 1 shows the results of the calibration of long-DMA, which was performed in 2013 just before the size measurement of standards in this work. For next two years (2014 and 2015), the results of size measurements using long-DMA are also compared with a reference DMA (nano-DMA).

Standard particles were aerosolized, dried and then introduced to SMPS which is equipped with either long-DMA or nano-DMA as discussed above. The particle size distribution results obtained from TSI - Aerosol Instrument Manager® (AIM) software (release version 9.0.0.0, 15:32:53, November 11, 2010) of SMPS are plotted as a function of logarithmic electrical mobility diameter where particle number concentration (dN/dlogDp) is taken in y-axis and particle mobility diameter is in x-axis. While doing measurement, for particles size distribution display using AIM software, 64 channels per decade was selected to represent the uni-modal distribution.

For repeatable results, 5 or 6 time particle size distribution measurements were performed for each standard samples (PSL 60 and 100 nm). For reproducible results, standard PSL samples were analyzed using SMPS (equipped with long-DMA) at least three different days in each year (i.e., 2013, 2014 and 2015) under similar conditions. Particle count mean diameter (CMD), mode diameter (MD) and geometric mean diameter (GMD) are obtained from the particle size distributions of standard PSL particles as discussed above (Eqs.(3)-(4)). In this study, we considered CMD as the tool to calculate the peak diameter of a size distribution (It is observed that the MDs and CMDs particle size measured are not statistically different (p values > 0.05)).The standard deviation for repeatability and reproducibility is always less than 1 nm for all measurements (Table 2).

For comparing the results obtained from long-DMA, the same SMPS setup was used and nano-DMA is replaced with long-DMA. Consistent to the results obtained from long-DMA, the calibration results (CMD, GMD and MD) from nano-DMA also show the similar trend with respect to time (year 2014 and 2015). The average CMD with standard deviation for PSL 60 nm particles using long-DMA were calculated to be 56.01±0.22, 56.37±0.37 and 57.42 ±0.06 nm for the year 2013, 2014 and 2015, respectively. Similarly, the average CMD with standard deviation for PSL100 nm particles were 92.86±0.69, 93.71±0.63 and 94.79 9

±0.28 nm, respectively. On the other hand the average CMD with standard deviation obtained using nano-DMA for PSL 60 nm particles were calculated to be 56.82±0.15 and 58.08±0.01 nm, and for PSL100 nm particles were 94.62±0.27 and 95.15 ±0.28 nm for the year 2014 and 2015, respectively.

CMD measured for PSL 60 nm using long-DMA has good agreement with the CMD measured using nano-DMA (0.9% difference) and mean size reported in the certificate (1.6% difference). Similarly CMD measured for PSL 100 nm using long-DMA has good agreement with CMD using nano-DMA (0.7%) but underestimates the mean size given in certificate by 7.25%. To better compare the CMD results obtained from both the DMAs with certificate values, we perform the uncertainty analysis. The succeeding section discussed the uncertainty in CMD measurements.

3.2. Uncertainty estimation in particles size measurement

The CMD is calculated to represent the peak diameter of particle size distribution. Thus the uncertainty sources which involved in the size distributions of the particles are considered to correctly represent the size of the PSL particles. The uncertainty sources considered are shown in cause effective diagram in Fig.3. These uncertainty sources involved in the measurement of particle size are estimated for PSL 60 and PSL100 nm particles. The combined standard uncertainty for the measurement of particle size is estimated using Eq. (5). Each year, the size of particle standards was measured three times (reproducibility) using long-DMA.

In the following sections we discussed the individual standard uncertainty components involved in the measurements of standard PSLs (60and 100 nm) particle size using SMPS.

3.2.1. Uncertainty estimation in count mean diameter (repeatability)

The CMD of PSL 60 and 100 nm are calculated from the size distributions of each sample j (Hinds, 1999; Sarangi et al., 2016). The mathematical expression for the estimation of standard uncertainty (type A) in CMD of particles (assuming that all influencing factors on CMD values in different measurements are remained constant):

10

√∑

(

)

(8)



where,

is the count mean diameter of PSL particles of samples j.

is the average

count mean diameter determined from n number of samples (n = 6).

Using long-DMA the standard uncertainty due to repeatability is contributed from ±0.04 to ±0.29 nm (PSL 60) and ±0.04 to ±0.4 nm (PSL 100) to particle CMD during three different years of measurements. Similarly using nano-DMA, the repeatability is ranged from ±0.6 to ±0.18 nm (PSL 60) and ±0.07 to ±0.24 nm (PSL 100) for the years 2014 and 2015.

3.2.2. Uncertainty in CMD due to particle diffusion loss

Diffusion loss correction was done using TSI-AIM software which is based on the loss of the particles along the SMPS channel (particle loss on the impactor inlet, the bi-polar neutralizer, internal plumbing, the tubing to DMA and CPC). The corrected number concentration of particles is obtained. Because particle CMD is the function of particle number concentration, the standard uncertainty due to diffusion loss of particles, which affects the CMD of particles is estimated as: (9)



( where

)

(10)

is the percentage of error due to difference in average count mean diameter (

without diffusion correction and average count mean diameter ( of PSL particles.

)

) with diffusion correction

is determined from n number of samples (n = 6), and is calculated

using equation (3).

Uncertainty due to diffusion loss is contributed from ±0.05 to ±0.34 nm (PSL 60) and ±0.06 to ±0.53 nm (PSL 100) to particle CMD measured during 2013, 2014 and 2015 using longDMA. Similarly using nano-DMA, the contribution range is from ±0.01 to ±0.16 nm (PSL 60) and ±0.02 to ±0.25 nm (PSL 100) to particle CMD for the years 2014 and 2015.

3.2.3. Uncertainty in CMD due to particle multiple charges

11

Multiple charges on a particle increase its mobility. Since the TSI-AIM software assumes that a particle has only one charge, the effect of multiple charges on a particle allows the particle to be incorrectly binned into a smaller-sized particle channel. Therefore, multiple charge correction is performed using inbuilt TSI-AIM algorithm (which is based on the theory discussed in Wiedensohler, 1998; Kim et al., 2005) that attempts to correct the sample data from the effects of the multiple charged particles. The mathematical expression for estimation of uncertainty in CMD due to multiple charges is similar to that of the expression of uncertainty due to particles diffusion loss: (11)



( where

)

(12)

is the percentage of error due to difference in average count mean diameter (

without multiple charge correction and average count mean diameter ( charge correction of PSL particles.

)

) with multiple

is determined from n number of samples (n = 6).

Using long-DMA the uncertainty due to particle multiple charge in particle CMD calculations is ranged from ±0.002 to ±0.26 nm (PSL 60) and ±0.02 to ±0.66 nm (PSL 100) for the years 2013, 2014 and 2015. Similarly using nano-DMA, the multiple charge contribution is ranged from ±0.001 to ±0.06 nm (PSL 60) and ±0.001 to ±0.28 nm (PSL 100) to the particle CMD for the years 2014 and 2015.

3.2.4. Uncertainty in CMD due to aerosol flow rate

Aerosol flow rate is equal to CPC inlet flow (0.3 lpm), which is calibrated with the reference flow meter (Gilian Gilibrator2, SP100IZ, World Precision Instruments) which has accuracy of ±1%. Standard uncertainty in flow rate,

at the inlet of CPC during sampling is

estimated using following equation: √∑

(

)

(13)



where

is the CPC inlet flow rate during sample i,

is the mean flow rate measured during

the whole duration of sampling (for n= 6 number of samples). The standard uncertainty (

) of reference flow meter given in certificate is ±1%. Then the

percentage of combined uncertainty in the flow rate to the mean flow rate can be calculated 12

as

)

√(

%. Therefore, the uncertainty contribution in CMD due to CPC inlet flow rate

̅

(corresponding standard uncertainty,

(type A and normal distribution) is estimated using

following equation: √(

)

(14)

̅

Using long-DMA, the standard uncertainty due to aerosol flow rate contribution ranges from ±0.55 to ±0.58 nm (PSL 60 nm) and ±0.55 to ±0.96 nm (PSL 100 nm) to particle CMD for all the measurements performed (during 2013, 2014 and 2015). Similarly using nano-DMA, aerosol flow rate contribution ranges from ±0.57 to ±0.58 nm (PSL 60) and ±0.94 to ±0.95 nm (100 nm) to the particle CMD during 2014 and 2015.

3.2.5. Uncertainty of particle standard (given in the certificates)

The uncertainties reported for PSL 60 and 100 nm in certificate (SRM® 1964 and SRM® 1963a) are ±0.63 nm and ±1.1 nm at coverage factor k = 2, respectively. Therefore, the standard uncertainties (

) involved in particle CMD are ±0.31(

) nm and ±0.55 (

) for both PSL 60 and PSL 100 particles, respectively, when long-DMA and nano-DMA was used in all the measurements.

3.2.6. Uncertainty in CMD due to DMA sheath flow rate

DMA sheath air is known as particle free air which dilutes the inlet aerosol flow at a definite ratio. In this study the ratio between the aerosol flow rate and sheath flow rate is 1:10. Aerosol sheath flow rate (3 lpm) is measured with the reference flow meter (Gilian Gilibrator2, SP100IZ, World Precision Instruments). Then, the sheath flow rate is computed by Eq. (1) and (2) and the computed mean mobility diameter ( ̅̅̅̅ for n = 6 measured flow rates) obtained with respect to reference PSL diameters (d = 60 and 100 nm). Then the percentage error (y) can be calculated as below (

̅̅̅̅ ̅̅̅̅

)

(15)

13

Assuming this percentage error as the standard uncertainty involved in particle size due to sheath flow rate and then the expression for standard uncertainty (

) (type A) is: (16)

The standard uncertainties,

has contribution ranges from ±1.34 to ±1.38 nm (PSL 60) and

±2.2 to ±2.3 nm to the particle CMD using both long-DMA and nano-DMA in all the measurements in different years.

3.2.7. Uncertainty in CMD due to voltage applied to DMA inner-electrode

The inner-electrodes of the DMA models (both long- and nano-DMA) used in this study are connected to negative potential. When particles are entered in to the annular space of DMA and flow along the DMA inner-electrode in a constant electric field then every particle induced their mobility depending on the applied voltage to the DMA electrode, particle flow rate, and DMA geometry. Therefore, any deviation in voltage of DMA inner-electrode would have a significant influence on the particle size distribution. We have input the diameter values (10, 20, 30,…, 150 nm) to the classifier at sheath flow 3 lpm and the corresponding classifier voltage or DMA inner-electrode voltage ( ) is recorded. Simultaneously a reference multimeter (Fluke 8846A 6-1/2 precision, ranged from 0 to 1000V, accuracy is ±0.0024%) is connected to the DMA voltage source and measured the corresponding voltage ( ) of DMA. Fig. 4 shows the calibration plot between

and

.

Then the measured voltage is computed in Eq. (1) and (2) and the computed mean mobility diameter (̅̅̅̅ for n = 6 number of sample flow rate) obtained with respect to reference PSLs diameter (d = 60 and 100 nm). Then the percentage error (y’) can be calculated using Eq. (17) below (

̅̅̅̅ ̅̅̅̅

)

(17)

The percentage contribution of uncertainty ( %) due to DMA electrode voltage to the particles CMD can be expressed by the standard uncertainty,

(type A) is estimated using

following equation: (18) The standard uncertainty,

for 60 nm particles is ±0.05 nm, whereas for 100 nm it is ±0.08

nm in all measurements in different years. Similarly for nano-DMA, these values are ±0.05 nm and ±0.09 nm for PSL 60 and PSL 100 nm particles, respectively. 14

3.2.8. Uncertainty due to DMA calibration

Long-DMA was calibrated using certified reference material, PSL 80 nm (certified diameter 81 ± 3 nm) as discussed above. The measured diameter and estimated uncertainty using long DMA 78.02±3.13 nm (see Table 1). The percentage of difference between certified and measured diameter is 3.7%:

Therefore the standard uncertainty (

) contribution in CMD (type B uncertainty and

rectangular distribution because the true value assume to be lies anywhere within the minimum and maximum value and the standard uncertainty would be divided by the factor √ , (Taylor and Kuyatt, 1994)) is estimated to be ±2.1 nm and it remained constant for all the measurements.

(19)



On the other hand, for nano-DMA the sizing uncertainty considered to be ±3% (Tröstl et al., 2015). Therefore using nano-DMA, the standard uncertainty (

) (type B uncertainty and

rectangular distribution) in CMD of particle size distribution is calculated using mathematical expression similar to Eq. (19). It is ranged from ±1 to ±1.64 nm for PSL 60 and PSL 100 nm, respectively, for all the measurements in different years (2014 and 2015).

3.2.9. Uncertainty due to difference in measured diameter and certified diameter The certified diameter for PSL 60 and 100 nm in certificate (SRM® 1964 and SRM® 1963a) are 60.39±0.63 nm (count mean diameter 55.70 nm) and 101.8±1.1 nm (count mean diameter 100.6 nm). The standard uncertainty (

) (type B and rectangular distribution) involved in the

measurement of CMD is shown in Eq. (20): (20)



(

)

(21)

15

Where

is the percentage of error due to difference in measured average count mean

diameter (

) and count mean diameter (

) mentioned in SRM certificate of PSL

particles.

The

is calculated in the range between ±0.06 to ±1.05 nm (PSL 60) and ±3 to ±4.1 nm

(PSL 100) when long-DMA was used in all the measurements during the years 2013, 2014 and 2015. Similarly using nano-DMA, the uncertainty is calculated in the range between ±0.5 to ±1.4 nm (PSL 60) and ±3 to ±3.3 nm (PSL 100) to the particle CMD in all the measurements.

3.2.10. Uncertainty due to variation in ambient temperature and pressure

The ambient pressure is important because the flow meter used in the DMA is a mass flow meter rather than a volumetric flow meter. The DMA depends on the volumetric flow. The variation in ambient pressure can be up to about 3%, which may be significant in regard to these measurements. The pressure and temperature also affect the mean free path in the Stokes-Einstein expression but this would be a lower order effect. However, we measured the particle diameter using SMPS setup in temperature and pressure controlled room (variations are about ± 2% for both the parameters). The variations could be resulted in an undetected drift in the flow meter which assumed to be affected as much as 3% changes in the apparent diameter. Assuming this percentage as an uncertainty (

) (type B, rectangular distribution)

contribution in particle CMD, then (22)



the standard uncertainty,

for 60 nm particles is ±0.98 nm, whereas for 100 nm it is ±1.6

nm in all measurements using both long and nano-DMA in different years.

3.3. Combined and expanded uncertainty

The combined standard uncertainty,

including type A and type B uncertainty sources can

be defined as: √∑ (

)

∑(

)

(23)

16

Equation (23) is the modified form of Eq. (5). Where

and

are the standard

uncertainty sources from type A and type B, respectively as discussed in the above sections.

Once the combined standard uncertainties in CMD of PSL 60 and PSL 100 nm are estimated for each measurement in a year, then using these combine uncertainties, the pooled and expanded uncertainties (Eqs. (6)-(7)) in particle size measurement for a year are calculated and shown in Table 3. The expanded uncertainty in the measurement of PSL 60 nm particle size is ranged from ±5.56 to ±5.84 nm, and for PSL 100 nm from ±9.68 to ±10.81 nm using long-DMA. Similarly for nano-DMA these values ranged from ±4.32 to ±4.98 nm and ±9.12 to ±9.46 nm for PSL 60 and PSL 100 nm, respectively.

Figure 5a and 5b shows the trend of CMD with corresponding expanded uncertainty of PSL 60 and 100 nm using long-DMA. The certified modal sphere diameter and expanded uncertainty of PSL 60 and 100 nm are 60.39±0.63 nm (with CMD 55.7 nm) and 101.8±1.1 nm (with CMD 100.6 nm), respectively. In all measurements in different years, the measured CMD (with expanded uncertainty) of PSL particles (60 and 100 nm) using long-DMA is comparable to the certificate value of each standard.

Similar results were also obtained when these PSL standards were measured using nanoDMA (Fig.6a and 6b). We preformed these measurements using nano-DMA in 2014 and 2015. The results of size measurement of standard particles using nano-DMA are comparable to long-DMA.

Among different uncertainty sources, the major uncertainty component is the DMA sheath flow rate. The uncertainty due to sheath flow rate contributes 22% and 24% in total uncertainty (expanded) of PSL 60 and PSL 100 nm particles, respectively. Almost same contribution (25 to 29%) of uncertainty due to DMA sheath flow rate is observed for PSL 60 and 100 nm using nano-DMA.

The next major uncertainty source is uncertainty due to DMA calibration. The difference in DMA sizing is about 3.7% as discussed above, therefore using long-DMA it contributes 37% and 20% in the expanded uncertainty in PSL 60 and PSL 100 nm particle size measurements,

17

respectively. On the other hand, this component contributes 22 and 18 % to total uncertainty for PSL 60 and 100 nm, respectively, using nano-DMA.

Third major uncertainty source is uncertainty due to difference in certified and measured diameter. Using long-DMA or nano-DMA it contributes maximum about 18% and 39% in the expanded uncertainty of CMD measurement for PSL 60 and PSL 100 nm particles, respectively.

The fourth major uncertainty source is uncertainty due to variation in temperature and pressure. Using long-DMA or nano-DMA it contributes maximum about 21% and 16% in the expanded uncertainty of CMD measurement for PSL 60 and PSL 100 nm particles, respectively.

In this study we found that other uncertainty components (repeatability, diffusion correction, charge correction, certified size of standards, DMA electrode voltage, etc.) are the minor contributor (individual contribution is <10%) to total expanded uncertainties in CMD measurement of particle standards.

It is important to note that the total uncertainties (expanded) involved in particle size measurements using long- and nano-DMA are observed to be similar in this study. It is consistent throughout the study period.

3.4. Mobility diameter of standards with time

From the size measurement results of long-DMA as shown in Table 2, it can be seen that the particle diameters (CMD) of PSL standards (60 and 100 nm) are increased by average 0.8 nm (ranged 0.4 to 1.1 nm) for each year from 2013 to 2015 (percentage variation of CMD shown in Fig. 5a and 5b). Similar trend (on average 0.9 nm) is also observed when nano-DMA is used for two consecutive years (i.e. 2014 and 2015, percentage variation of CMD shown in Fig. 6a and 6b). The observed mobility size trend measured by both the DMAs is similar and suggesting morphological changes (possible coating on the PSL standards) in particle size. To see any possible coating on the surface of the particle standards (PSL 60 nm and PSL 100 nm), PSL standards were aerosolized, dried and sampled on the carbon coated copper grids for HRTEM imaging. 18

3.5. HRTEM images of standard particles

To determine the size of the standards (PSL 60 and PSL 100 nm), the particle images were taken using HRTEM (Fig.7). The diameters obtained by HRTEM images are 54.98±2.55 nm and 93.6±2.8 nm on average for standard PSL 60 and 100 nm particles. The mean diameter is obtained based on the direct observation of the sphere dispersed (either in single (Fig.7a and 7c) or cluster (Fig.7b and 7d)) on the grids. However, it should be noted that the smaller apparent size obtained by HRTEM compared to the certified values may be caused by the exposure of the polystyrene spheres to the electron beam (Yamada et al., 1985; Jung et al., 2006). In general, the mean diameter of PSL 60 nm sphere size by HRTEM shows good agreement with the size (CMD) determined by both the DMAs (long-DMA and nano-DMA) and their mean diameter (55.70 nm) given in the certificate, and the percentage size difference lies between 1.3 and 4.5%. Also for PSL 100 nm particles, the average size obtained by HRTEM has good agreement with the mean size (CMD) determined by DMAs (0.2% difference with long-DMA and 1.4% difference with nano-DMA). However consistent with the DMA results, size determined by HRTEM images also underestimate the particle size given in certificate (100.6 nm) by 7.5%.

From the HRTEM images (Fig.7a and 7c) it was observed that the particles are coated with a thin layer on their surface of varying thickness. The reason of this coating is possibly the contaminants in the suspension which result in a residue layer on the spheres. The hypothesis that the mean size of both the standards are increasing with the time is confirmed by HRTEM images mapping. The exact chemistry on the surface due to longer time storage is yet to be understood, and is limited to the scope of this study.

4. Summary and Conclusion We have presented a detailed analysis on the performance of standard particles for three consecutive years (i.e. 2013, 2014 and 2015) using long-DMA (TSI 3081) and for two consecutive years (i.e. 2014 and 2015) using nano-DMA (TSI 3085). These standards are reference material PSL 60 and 100 nm traceable to SI through NIST. Using long-DMA, the average CMDs with estimated expanded uncertainty of PSL 60 are 56.01±5.56, 56.37±5.66

19

and 57.42 nm±5.84 nm for the year 2013, 2014 and 2015, respectively. For PSL 100 nm the average CMDs with estimated expanded uncertainties are 92.86±10.81, 93.71±10.44 and 94.79 nm±9.68 nm, respectively. On the other hand using nano-DMA for PSL 60 nm particles, the values are 56.82±4.32 and 58.08 nm±4.98 nm, and for PSL-100 nm particles are 94.62±9.46 and 95.15 nm±9.12 nm for the year 2014 and 2015, respectively.

The performance results (CMDs) of these standards using long-DMA are compared with the results obtained using nano-DMA (TSI 3085). Size measured by long-DMA (calibrated by other particle CRM) has good agreement with the nano-DMA (0.9% and 0.7% difference in the size results of standard PSL 60 and PSL 100 nm particles, respectively).While comparing the sizes measured using DMAs with the size given in certificates of standards, also a good agreement is obtained, i.e. 1.6% differences in the case of PSL 60 nm. But DMA results underestimate the size of PSL 100 nm by 7.25%. The size of the standards is further analysed using HRTEM images. Further PSL 60 nm has good agreement with the size determined by DMAs and size given in its certificate. Similarly PSL 100 nm has good agreement with the mean size determined by DMAs, but underestimates the size given in certificates by 7.5%.The uncertainty of about 5.6 nm for PSL 100 nm is comparable with the value of about 13 nm reported for 500 nm standard particles elsewhere (Lin et al.,2010). In our observation every technique used in this study underestimate the particle size (PSL 100 nm i.e. in certificate and based on mean diameter) and the size measured by each instruments are comparable.

The major standard uncertainty sources in the expanded uncertainty of CMD measurements are sheath flow rate, DMA calibration, difference in certified and measured diameter, and variation in the ambient temperature and pressure as compared to that of repeatability, diffusion correction, charge correction, DMA inner electrode voltage, certified size of standards, etc. While comparing uncertainty source types, most of the major uncertainties sources are type A uncertainties and shows close agreement between long- and nano- DMA. Therefore it may be concluded that both long-DMA and nano-DMA capable to measure the particle size within the estimated uncertainty in this study. As of our best knowledge, first time a detailed uncertainty on particle CMD measurements using both long- and nano-DMA are discussed.

20

It was observed that there is an increasing trend in CMD of PSL standards (60 and 100 nm) with time by on average 0.8 nm using long-DMA and 0.9 nm using nano-DMA. Our results suggest that the increasing trend in CMD of PSL standards (60 and 100 nm) with respect to measurement performed in consecutive years 2013, 2014 and 2015 possibly because of the coating which is also confirmed by HRTEM imaging of particles. The reason for this coating is possibly the contaminants in the suspension which resulted in a residue layer on the spheres because of long duration of storage. However, we cannot rule out a drift in the DMA (likely the sheath flow meter) is a possible cause for the apparent shift in the particle CMD. Based on our experience, the standards (SRM®1963a and 1964) supplied by NIST are stable and well characterized more than two years (2014 and 2015) even after the expiry date (31st December, 2013 for both of the standards).

Acknowledgements Authors thank Director, CSIR-National Physical Laboratory, New Delhi for providing all instrumental facilities and support. BS thanks Department of Science and Technology, Government of India, New Delhi for awarding him INSPIRE fellowship (DST/INSPIRE FELLOWSHIP/2011/148). A part of funding from CSIR-MIST project (PSC 0111) is highly appreciated. Authors also thank to two anonymous reviewers for their valuable comments which helped to improve the quality of this paper.

21

References

Hinds,W. C.(1999). Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles, second ed. John Wiley & Sons, New York.

Nenes, A & Seinfeld, J. H. (2003). Parameterization of cloud droplet formation in global climate models. Journal of geophysical research, D14, 108, 4415.

Pöschl, U. (2005). Atmospheric Aerosols: Composition, Transformation, Climate and Health Effects. Angewandte Chemie International Edition, 44, 7520–7540.

Sarangi, B., Aggarwal, S. G., and Gupta, P. K. (2015). A Simplified Approach to Calculate Particle Growth Rate Due to Self-Coagulation, Scavenging and Condensation Using SMPS Measurements during a Particle Growth Event in New Delhi. Aerosol Air Quality Research,15.166–179.

Posfai, M., Xu, H. F., Anderson, J. R., and Buseck, P. R. (1998). Wet and dry sizes of atmospheric aerosol particles: An AFM-TEM study. Geophysical Research Letters, 25, 1907–1910.

Barkay, Z., Teller, A., Ganor, E., Levin, Z., and Shapira, Y. (2005). Atomic force and scanning electron microscopy of atmospheric particles. Microscopy Research and Technique, 68, 107–114. Dall’Osto, M., Harrison, R. M., Beddows, D. C. S., Freney, E. J., Heal, M. R., and Donovan, R. J. (2006). Single particle detection efficiencies of Aerosol Time-of-Flight mass spectrometry during the North Atlantic Marine Boundary Layer Experiment. Environmental Science & Technology, 40, 5029–5035.

Nikjoo, K., Aliahmad, M, Sharifi, S., and Sargazi, M. (2012). Photon Correlation Spectroscopy and SAXS Study of Cylindrical to Spherical Transition in the AOT Microemulsion by Changing Solvent. Soft Nanoscience Letters, 2, 17–21.

22

Liu, N. Y. H. and Pui, D. Y. H. (1974). A Submicron Aerosol Standard and the Primary, Absolute Calibration of the Condensation Nuclei Counter. Journal of Colloid and Interface Science, 47, 155–171.

McMurry, P. H. (2000). A review of atmospheric aerosol measurements. Atmospheric Environment, 34, 1959–1999.

Hermann, M., Wehner, B., Bischof, O., Han, H.–S., Krinke, T., Liu, W., Zerrath, A., and Wiedensohler, A. (2007). Particle counting efficiencies of new TSI condensation particle counters. Journal of Aerosol Science, 38,674–682.

Stolzenburg, M. R. and McMurry, P. H. (2008). Equations Governing Single and Tandem DMA Configurations and a New Lognormal Approximation to the Transfer Function. Aerosol Science and Technology, 42, 421–432.

Flagan, R. C. (1998). History of electrical aerosol measurements. Aerosol Science and Technology, 28, 301–380.

Knutson, E. O. and Whitby, K. T. (1975). Aerosol Classification by Electrical Mobility: Apparatus Theory and Applications. Journal of Aerosol Science, 6, 443–451.

Keady, P. B., Quant, F. R., and Sem, G. J. (1983). Differential mobility particle sizer: a new instrument for high resolution aerosol size distribution measurement below 1 µm. TSI Quarterly, 9, 3–11.

Shimada, M., Lee, H. M., Kim, C. S., Koyama, H., Myojo, T., and Okuyama, K. (2005). Development of an LDMA-FCE system for the measurement of submicron aerosol particles.Journal of Chemical Engineering of Japan, 38, 34–44.

Mulholland, G. W., Donnelly, M. K., Hagwood, C. R., Kukuck, S. R., Hackley, V. A., and Pui, D. Y. H. (2006). Measurement of 100 nm and 60 nm particle standards by differential mobility analysis. Journal of Research of the National Institute of Standards and Technology,111, 257–312.

23

Anumolu, R. and Pease III, L. F. (2012). Rapid nanoparticle characterization. The Delivery of Nanoparticles,17, 347–376.

Kidd, C., Perraud, V., Wingen, L. M., and Finlayson-Pitts, B. J. (2014). Integrating phase and composition of secondary organic aerosol from the ozonolysis of α-pinene. Proceedings of the National Academy of Sciences,111, 7552–7557.

Mun, J. H., Cho, D. G., Kim, Y. J., Choi, J. B., Kang, S. W., Yun, J. Y., Shin, Y. H., and Kim, T. S. (2011). Development and Calibration of Differential Mobility Analyzer for 20 to 80 nm Particles Under Low Pressure Conditions. Journal of Nanoscience and Nanotechnology, 11, 6275–6282.

Garnaes, J. (2011). Diameter measurements of polystyrene particles with atomic force microscopy. Measurement Science and Technology, 22, 1–8.

Takegawa, N., Miyazaki, Y., Kondo, Y., Komazaki, Y., Miyakawa, T., Jimenez, J. L., Jayne, J. T., Worsnop, D. R., Allan, J. D., and Weber, R. J.(2005). Characterization of an Aerodyne Aerosol Mass Spectrometer (AMS): Intercomparison with other aerosol Instruments. Aerosol Science and Technology,39,760–770.

EURACHEM/CITAC Guide CG 4. (2012). Quantifying Uncertainty in Analytical Measurement, 1–133.

Motzkus, C., Macé, T., Gaie-Levrel, F., Ducourtieux, S., Delvallee, A., Dirscher, K., Hodoroaba, V. –D., Popov, I., Popov, O., Kuselman, I., Takahata, K., Ehara, K., Ausset, P., Maillé, M., Michielsen, N., Bondiguel, S., Gensdarmes, F., Morawska, L., Johnson, G. R., Faghihi, E. M., Kim, C. S., Kim, Y. H., Chu M C., Guardado, J. A., Salas, A., Capannelli, G., Costa, C., Bostrom, T., Jämting, Å. K., Lawn,M. A., Adlem, L., and Vaslin-Reimann, S. (2013). Size characterization of airborne SiO2 nanoparticles with online and off-line measurement techniques: an interlaboratory comparison study. Journal of Nanoparticle Research, 15, 1–36.

24

Sarangi, B., Aggarwal, S. G., Sinha, D., and Gupta, P. K. (2016) Aerosol effective density measurement using scanning mobility particle sizer and quartz crystal microbalance with the estimation of involved uncertainty. Atmospheric Measurement Techniques, 9, 859875.

Wiedensohler, A. (1988). An approximation of the bipolar charge distribution for particles in the submicron size range. Journal of Aerosol Science, 19, 387–389.

Kim, S., Woo, K., Liu, B., and Zachariah, M. (2005). Method of measuring charge distribution of nano sized aerosols. Journal of Colloid and Interface Science, 282, 46–57.

Tröstl, J., Tritscher, T., Bischof, O. F., Horn, H.–G., Krinke, T., Baltensperger, U., and Gysel, M. (2015). Fast and precise measurement in the sub-20 nm size range using a Scanning Mobility Particle Sizer. Journal of Aerosol Science, 87, 75–87.

Collins, D. R., Cocker, D. R., Flagan, R. C., and Seinfeld, J. H. (2004).The scanning DMA transfer function, Aerosol Science and Technology, 38, 833–850.

Wiedensohler, A., Birmili, W., Nowak, A., Sonntag, A., Weinhold, K., Merkel, M., Wehner, B., Tuch, T., Pfeifer, S., Fiebig, M. A., Fjäraa, M., Asmi, E., Sellegri, K., Depuy, R., Venzac, H., Villani, P., Laj, P., Aalto, P., Ogren,J. A., Swietlicki, E., Williams, P., Roldin, P., Quincey, P., Hüglin, C., Fierz-Schmidhauser, R., Gysel, M., Weingartner, E., Riccobono, F, Santos, S, Grüning, C., Faloon, K., Beddows, D., Harrison, R., Monahan, C., Jennings, S. G., O'Dowd, C. D., Marinoni, A., Horn, H.–G., Keck, L., Jiang, J., Scheckman, J., McMurry, P. H., Deng, Z., Zhao, C. S., Moerman, M., Henzing, B., de Leeuw, G., Löschau, G., and Bastian, S. (2012). Mobility particle size spectrometers: harmonization of technical standards and data structure to facilitate high quality long-term observations of atmospheric particle number size distributions. Atmospheric Measurement and Techniques, 5, 657-685.

Hiranuma, N., Augustin-Bauditz, S., Bingemer, H., Budke, C., Curtius, J., Danielczok, A., Diehl, K., Dreischmeier, K., Ebert, M., Frank, F., Hoffmann, N., Kandler, K., Kiselev, A., Koop, T., Leisner, T., Möhler, O., Nillius, B., Peckhaus, A., Rose, D., Weinbruch, S., Wex, H., Boose, Y., DeMott, P. J., Hader, J. D., Hill, T. C. J., Kanji, Z. A., Kulkarni, G., 25

Levin, E. J. T., McCluskey, C. S., Murakami, M., Murray, B. J., Niedermeier, D., Petters, M. D., O’Sullivan, D., Saito, A., Schill, G. P., Tajiri, T., Tolbert, M. A., Welti, A., Whale, T. F., Wright, T. P., and Yamashita, K.(2015). A comprehensive laboratory study on the immersion freezing behavior of illite NX particles: a comparison of 17 ice nucleation measurement techniques. Atmospheric Chemistry and Physics, 15, 2489–2518.

Lin, C.-M., Yu, T.-C., Lai S.-H., Ho, H.-C., Weng F. H., Chen C.-J. (2010). Evaluation of uncertainty in nanoparticle size measurement by differential mobility analysis. NSTINanotech, 1, 160–163.

Yamada, Y., Miyamoto, K., and Koizumi, A. (1985). Size Determination of Latex Particles by Electron Microscopy. Aerosol Science and Technology, 4 (2), 227-232.

Jung, K. Y., Park, B. C., Song, W. Y., Eom, T. B., Eom, B.-H. O. (2002). Measurement of 100-nm Polystyrene Sphere by Transmission Electron Microscope. Powder Technology, 126, 255–265.

Taylor, B. N and Kuyatt, C. E. (1994). Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results. NIST Technical Note 1297, 1–20.

26

Table 1 Uncertainty results of DMA calibration using standard PSL 80 nm particles during VAMAS inter-comparison 2012. DMA type

Long- DMA

Sample flow

Impactor

Average

Certified diameter

Estimated

(lpm) to sheath

type (cm)

mode

(nm)±uncertainty(

diameter

flow (lpm)

diameter

nm)

(nm)±uncertainty(n

ratio

(nm)

0.3:3

0.0457

m)

78.02±1.39

78.02±3.13*

81 ± 3

(TSI 3081) *The combine uncertainty estimated as

√(

)

(

)

where

and

are the certified

uncertainty and estimated uncertainty. The expanded uncertainty then calculated at coverage factor k=2.

27

Table 2 Averaged count mean diameter (CMD), geometric mean diameter (GMD) and mode diameter (MD) with standard deviations of PSL 60 and 100 nm particles measured in different years using long- and nano-DMA. Year

Sample set code

2013

2013SET-L1 2013SET-L2 2013SET-L3 2014SET-L1 2014SET-L2 2014SET-L3 2015SET-L1 2015SET-L2 2015SET-L3

2014

2015

2014

2015

2014SET-N1 2014SET-N2 2015SET-N1 2015SET-N2

Long-DMA (TSI 3081) PSL 60 PSL 100 Diameter type (nm) Diameter type (nm) CMD MD GMD CMD MD GMD 55.81±0.58 54.67±0.68 55.84±0.55 92.13±0.64 91.51±0.65 91.97±0.65 56.24±0.22

55.15±0.60

56.08±0.22

93.49±0.48

92.71±0.59

93.20±0.47

55.99±0.20

54.61±0.46

54.61±0.22

92.98±0.08

92.98±0.08

92.65±0.83

55.98±0.20

53.72±0.04

55.30±0.57

92.98±0.08

92.81±0.63

93.57±0.24

56.46±0.51

55.45±0.27

56.31±0.41

94.05±0.25

93.08±0.55

93.73±0.25

56.69±0.16

55.97±0.31

56.50±0.15

94.10±0.34

92.58±0.80

93.77±0.39

57.35±0.19

56.67±0.53

57.14±0.19

94.69±0.08

93.72±0.30

94.35±0.08

57.47±0.07

56.89±0.80

57.26±0.07

94.58±0.98

94.14±0.83

94.22±0.99

57.45±0.13

56.70±0.09

57.13±0.12

95.12±0.40

94.31±0.77

94.75±0.40

56.69±0.13

Nano-DMA (TSI 3085) 55.89±0.45 56.51±0.16 94.42±0.55

91.96±0.03

94.60±0.03

56.94±0.15

56.05±0.19

56.74±0.15

94.81±0.17

91.93±0.07

94.21±0.52

58.07±0.14

59.75±0.52

56.51±0.16

95.12±0.52

96.77±0.98

94.75±0.51

58.08±0.36

60.02±1.10

56.74±0.15

95.17±0.20

96.59±0.53

94.62±0.20

28

Table 3 The detailed uncertainties in count mean diameter (CMD) measured for standard PSL60 and 100 nm particles using long- and nano-DMA. Uncertainty Type

Long-DMA (PSL 60 nm) ±Uncertainty (nm) 2013

Combine uncertainty Pooled uncertainty Expanded uncertainty Uncertainty Type

2.78

2.79

±Uncertainty (nm)

2014

2.78

2.79

2.83

2015

2.87

2.97

2013

2.78

2.99

5.46

5.29

2014

5.46

5.53

5.10

2015

5.02

4.89

4.94

2.78

2.83

2.92

5.41

5.22

4.84

5.56

5.66

5.84

10.81

10.44

9.68

Nano-DMA (PSL 60 nm)

Nano-DMA (PSL100 nm)

±Uncertainty (nm) 2014

Combine uncertainty Pooled uncertainty Expanded uncertainty

Long-DMA (PSL100 nm)

2.13

±Uncertainty (nm) 2015

2.19

2.48

2014

2.49

4.80

2015

4.66

4.58

4.56

2.16

2.49

4.73

4.57

4.32

4.98

9.46

9.12

29

4.69

Fig.1. Experimental setup used for particle size measurements.

30

Fig.2. Typical particle size distributions for total size range (a) PSL 60 nm and (b) PSL 100 nm and selected size range (c and d for PSL 60 and 100 nm) obtained using long- DMA.

31

Fig.3. Cause effective diagram to represent the uncertainty components considered in total uncertainty estimation in the measurement of CMD for PSL standards using long- and nanoDMA.

32

Fig.4. Calibration curve shows the plot between classifier voltage and reference multimeter voltage.

33

Fig.5. Averaged CMD with the expanded uncertainty of particles standards and the percentage of variation over time, (a) PSL 60 nm and (b) PSL 100 nm measured in different years (2013, 2014 and 2015) using long-DMA.

34

Fig.6. Averaged CMD with the expanded uncertainty of particles standards and the percentage of variation over time, (a) PSL 60 nm and (b) PSL 100 nm measured in different years ( 2014 and 2015) using nano-DMA.

35

Fig.7. Polystyrene latex spheres imagestaken using HRTEM (a) 60 nm in single, (b) 60 nm in cluster, (c) 100 nm in single and (d) 100 nm in cluster.

36

Highlight 1. Performance of particle standards was checked 2. Particle size standards are stable during and even after their shelf-life within the uncertainty limits 3. Size performance results for PSL 60 nm show good agreement with the mean size given in the certificate 4. However, standard PSL 100 nm size is found to be underestimated by ~7%

37