Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometry

STOTEN-18997; No of Pages 12 Science of the Total Environment xxx (2016) xxx–xxx

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometry Yuan Yang a, Chen-Lu Long a, Hai-Pu Li a, Qiang Wang a,⁎, Zhao-Guang Yang a,b,⁎⁎ a b

Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University, No. 392 Lushan Nan Road, Yuelu District, Changsha 410083, PR China Shenzhen Research Institute of Central South University, B406 Virtual University, Shenzhen High-Tech Industrial Pk, Shenzhen, Guangdong 518057, PR China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Fast method of SP-ICPMS was studied to detect AgNPs and AuNPs size distribution. • AgNPs and AuNPs were spiked into filtrated water to study their stability. • SP-ICPMS was used to detect AgNPs and AuNPs at environmental samples.

a r t i c l e

i n f o

Article history: Received 7 November 2015 Received in revised form 29 December 2015 Accepted 29 December 2015 Available online xxxx Editor: D. Barcelo Keywords: Single particle ICP-MS Engineering nanoparticles Stability Environmental water

a b s t r a c t The production and use of engineering nanomaterials (ENMs) leads to the release of manufactured or engineered nanoparticles into environment. The quantification and characterization of ENMs are crucial for the assessment of their environmental fate, transport behavior and health risks to humans. To analyze the size distribution and particle number concentration of AgNPs and AuNPs in environmental water and track their stability at low number concentration, a systematic study on SP-ICPMS was presented. The Poisson statistics was used to discuss the effect of dwell time and particle number concentration theoretically on the detection of NPs in solution by SPICPMS. The dynamic range of SP-ICPMS is approximately two orders of magnitude. The size detection limits for silver and gold nanoparticle in ultrapure water are 20 and 19 nm respectively. The detection limit of nanoparticle number concentration is 8 × 104 particles L−1. Size distribution of commercial silver and gold nanoparticle dispersions is determined by SP-ICP-MS, which was in accordance with the TEM results. High particle concentration recoveries of spiked AgNPs and AuNPs are obtained (80–108% and 85–107% for AgNPs and AuNPs respectively in ultrapure and filtered natural water). It indicates that SP-ICPMS can be used to detect AgNPs and AuNPs. The filtration study with different membranes showed that filtration might be a problematic pre-treatment method for the detection of AgNPs and AuNPs in environmental water. Furthermore, the stability of citrate-coated AgNPs and tannic acid-coated AuNPs spiked into filtrated natural and waste water matrix was also studied at low concentration using SP-ICP-MS measurements. Dissolution of AgNPs was observed while AuNPs was stable during a ten day incubation period. Finally SP-ICPMS was used to analyze NPs in natural water and waste water. The results indicate that SP-ICPMS can be used to size metallic nanoparticles sensitively of low concentration under realistic environmental conditions. © 2016 Elsevier B.V. All rights reserved.

⁎ Correspondence to: Q. Wang, College of Chemistry and Chemical Engineering, Central South University, No. 392 Lushan Nan Road, Yuelu District, Changsha 410083, PR China. ⁎⁎ Correspondence to: Z.-G. Yang, Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University, No. 392 Lushan Nan Road, Yuelu District, Changsha 410083, PR China. E-mail addresses: [email protected] (Y. Yang), [email protected] (C.-L. Long), [email protected] (H.-P. Li), [email protected] (Q. Wang), [email protected] (Z.-G. Yang).

http://dx.doi.org/10.1016/j.scitotenv.2015.12.150 0048-9697/© 2016 Elsevier B.V. All rights reserved.

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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1. Introduction With amazing development of nanoscale science and technology, engineering nanomaterials (ENMs) are being applied in a wide variety of fields, including textiles, cosmetics, food packaging, pesticides, sporting goods, paint, optics and medical devices (Nel et al., 2006). According to the report of Emerging Nanotechnologies Program, there are over 1800 types of commercial nanomaterials introduced to the market (Vance et al., 2015). Due to special antimicrobial activities and partly antiviral properties, silver nanoparticles (AgNPs) are one of the most promising ENMs which is widely used in the fields of textile, water purification instruments, cosmetics, etc. Gold nanoparticle (AuNP) has also been widely applied in the fields of medical, drug delivery, films, biomolecule detection and catalysts. While the nanoscale dimensions give ENMs new characteristics, the potential for their release in the environment and subsequent effects on ecosystem health is becoming an increasing concern (Gottschalk and Nowack, 2011; Tiede et al., 2009b). The predicted environmental concentrations of AgNPs in surface waters and soil were 0.01 μg/L and 0.43 μg/kg respectively, while the predicted environmental concentrations of AuNPs in surface water and soil may reach 0.14 μg/L and 5.99 μg/kg (Zanker and Schierz, 2012). Although the environment concentration of engineering nanoparticles (ENPs) is low, their ecological risk can't be ignored. The fate, transport, stability and potential risks of ENMs highly depend on their physic-chemical properties, including chemical composition, crystal structure, particle size and shape, surface chemistry and agglomeration/aggregation state (Chinnapongse et al., 2011). In recent years, characterization techniques of dynamic light scattering (DLS), nanotracking analysis (NTA) and electron microscopy are commonly used to investigate the factors that control the fate and transport of ENPs, but these techniques cannot be applied to environmental sample analysis for their high detection limits (Farré et al., 2011; Gallego-Urrea et al., 2011; von der Kammer et al., 2012). It is not evident whether the facts that affect the behavior of high concentration nanoparticle particle are the same of that affecting the behavior of low environmental concentration nanoparticle particle. Meanwhile it is difficult to obtain quantitative assessments of the particle number size distribution with electron microscopy (von der Kammer et al., 2012). Now many separation technologies for metallic ENPs have been developed, including field flow fractionation (FFF) (Hagendorfer et al., 2012; Meisterjahn et al., 2014), capillary electrophoresis (CE) (Liu et al., 2014b; Qu et al., 2014), hydrodynamic chromatography (HDC) (Tiede et al., 2009a, 2010) and reversed-phase liquid chromatography (RP-chromatography) hyphenated with inductively coupled plasma mass spectrometry (ICP-MS) (Soto-Alvaredo et al., 2013; Zhou et al., 2014). However there are obvious limitations of the current separation analysis technology for characterizing nanoparticles at environmentally relevant concentrations (in the sub-μg/L range). A good understanding of ENPs' size, dissolution, agglomeration and environmental behavior in different matrix is the basis of investigating their environmental health risks. Therefore it

Table 1 Instrumental parameters for SP-ICPMS data acquisition. Agilent 7700× ICP-MS

Value

RF power (W) Carrier gas (L/min) Making up gas (L/min) Spray chamber temperature (°C) Nebulizer pump rate (mL/min) Data acquisition mode Integration time (ms) Acquisition time (s) Mass monitored Sample depth(mm)

1550 1.05 0.1 2 0.36 TRA 3 60 107 Ag or 197Au 8

is crucial to establish robust methods for the characterization and quantitative determination of nanoparticles in aquatic environment. Single-particle inductively coupled plasma mass spectrometry (SPICPMS) is an emerging powerful analytical tool of characterizing and detecting metallic engineering nanoparticle at low concentration in aquatic environment. All data were collected using time resolved analysis (TRA) mode during a SP-ICPMS analysis. If a metallic nanoparticle in water diluted sufficiently is introduced into the plasma, a burst of ion is generated after vaporizing, atomizing and ionizing of the discrete particle, then single strong pulse is generated in short time (b1 ms). The frequency and the intensity of the pulse signal are related to the number concentration and the mass of the nanoparticle respectively, so it can provide the information of nanoparticle size distribution and number concentration in aqueous solution (Degueldre and Favarger, 2004; Degueldre et al., 2004, 2006). A protocol for measuring the transport efficiency of ICP-MS was established to count and size the metallic nanoparticles (Pace et al., 2011). And an iterative algorithm was developed to discriminate the nanoparticle from background signal (Mitrano et al., 2012). Based on that, the SP-ICPMS technique was established for the detection of metallic nanoparticles and has been applied in different matrices (Gray et al., 2013; Hadioui et al., 2014; Laborda et al., 2011; Loeschner et al., 2013; Yang et al., 2014; Dan et al., 2015; Liu et al., 2014a; Donovan et al., 2015; Kim et al., 2013; Peters et al., 2014; Hadioui et al., 2015). However very few researches have been carried out on the quantitative characterization of AgNPs and AuNPs in environmental water. Meanwhile the stability of metallic nanoparticle at the mg/L range has been studied (Diegoli et al., 2008; Hitchman et al., 2013; Lee and Ranville, 2012), but the stability of metallic nanoparticles at environmental concentration has not been reported yet. The objectives of this study were (1) to develop methodology for characterization and detection of AuNPs and AgNPs size distribution and particle number concentration in environmental water, (2) to track the stability of AuNPs and AgNPs at environmentally relevant concentrations, and (3) to analyze AuNPs and AgNPs in environmental samples. In this study, we chose AuNP and AgNP as the representative metallic nanoparticle because of their broad applications and potential release into the environment. 2. Experimental section 2.1. Sampling collection Three natural water samples and one waste water sample were collected. The waste effluent water was collected from the sewage treatment plant of the HuNan University, China (N 28°10′52″E 112°56′ 21″). The natural water sampling locations include Xiangjiang River (N 28°10′28″E 112°52′54″), Guiyu River (N 23°19′58″E 116°21′42″) and Chendian Lake water (N23°17′48″E116°19′43″). Xiangjiang River is located in the HuNan Province, China, Guiyu River and Chendian Lake are located in the Guiyu town, Guangdong province, China, which was one of the largest electronic waste sites in the world. The natural water samples were collected at a depth of approximately 0.4 m below the water surface and 4 m away from the river or lake bank, and all the samples were immediately transported to the laboratory and stored at 4 °C before use. 2.2. Sample preparation and determination After dilution, all samples were placed in ultrasonic water for 15 min to obtain homogeneously dispersed ENPs in the water. Ice bags were added to the bath to avoid the temperature increase during sonication. The nanoparticle signal numbers were in the range of 100 to 1800 particles min−1 (i.e. nanoparticle signal numbers were less than 10% of the total reading) (Pace et al., 2012). ICP-MS was tuned with tuning solution to obtain high sensitivity, minimum oxide and doubly charged species before analysis. For the analysis of AgNPs and AuNPs suspension

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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Fig. 1. Effects of dwell time on the signal intensity-frequency.

samples, the sample introduction system was rinsed thoroughly with 1% (v/v) HNO3 and 1% (v/v) HCl respectively before each analysis in sequence. Fresh AgNP (50 and 100 nm) and AuNP (60 and 100 nm) suspensions certified by TEM as quality control samples were also prepared daily during each analysis to ensure the accuracy of SP-ICPMS. For the stability analysis of the ENPs in natural and effluent water, all natural and effluent water samples were firstly centrifuged at 5000 rpm for 10 min, and then be filtered through 0.45 μm and 0.22 μm nylon membrane in sequence to remove native silver and gold particles. The silver nanoparticle capped with citrate and gold nanoparticle capped with tannic acid suspension were spiked into filtered water as follow concentration: 10 ng/L (50 nm AgNPs, 12,500 particles/mL), 40 ng/L (100 nm AgNPs, 7400 particles/mL), 25 ng/L (60 nm AuNPs, 11,000 particles/mL) and 50 ng/L (100 nm AuNPs, 4900 particles/mL). All nanoparticle samples were kept in dark at 25 °C during the whole incubation period. 2.3. Chemical analysis All reagents in this experiment were of high purity. AgNP and AuNP suspensions were obtained from nanoComposix (San Diego, CA, USA) (nominal sizes of 30 nm, 50 nm, 80 nm, 100 nm for AgNPs and 30 nm, 60 nm, 80 nm, 100 nm for AuNPs). AgNP suspensions were stabilized with 2 mM citrate and the mass concentration of AgNP suspension was 0.02 mg/mL. AuNP suspensions were stabilized with tannic acid and mass concentration of AuNPs was 0.05 mg/mL. Moreover AuNPs suspension with 60 nm nominal diameter capped with tannic acid (particle number concentration: 2.6 × 1010 particles/mL) was purchased

from BBI Solution (Cardiff, U.K.) to detect nebulizer transport efficiency of ICPMS daily. Transmission electron microscope provided by manufacturer has proved near-spherical shape and monodisperse for all nanoparticles. The TEM diameters were 32.3 ± 3.2, 53.4 ± 4.4, 79.0 ± 8.7, and 99.4 ± 7.0 nm for AgNPs and 28.6 ± 3.2, 61.2 ± 6.0, 75.4 ± 9.5, 101.4 ± 14.5 nm for AuNPs, respectively. AgNP number concentrations were 1.1 × 1011 particles/mL (32.3 ± 3.2), 2.5 × 1010 particles/mL (53.4 ± 4.4), 8.2 × 109 particles/mL (79.0 ± 8.7), 3.7 × 109 particles/ mL (99.4 ± 7.0) and AuNP number particle concentrations were 2.1 × 1011 particles/mL (28.6 ± 3.2), 2.2 × 1010 particles/ mL (61.2 ± 6.0), 1.2 × 1010 particles/mL (75.4 ± 9.5), 4.9 × 109 particles/mL (101.4 ± 14.5). All of TEM values and particle number concentration were provided by nanoComposix. The dissolved standards for Ag and Au were obtained from China Iron & Steel Research institute (spectroscopy standard, China, 1000 mg/L). Optima grade HCl (BICR, Beijing, China) and HNO3 (Aladdin, Shanghai, China) was used for Au and Ag standard preparation. Dissolved Ag and Au standards for calibration curve were diluted freshly as follows: 0, 0.05, 0.2, 0.5, 2 μg/L (0.2% v/v HNO3 solution for Ag and 2% v/v HCl solution for Au). Ultrapure water (PERSEE, resistance = 18.2 MΩ, China) was used to dilute samples. All polypropylene volumetric flasks and other vessels were soaked in 20% v/v HNO3 solution at least 24 h, and then washed with ultrapure water. Humic acid (HA) dry powder (Fulvic acid N 90%) obtained from Aladdin (Shanghai, China) was added to ultrapure water to simulate natural organic matter (NOM). 0.22, 0.45, 1, 5 μm nylon filters and 0.45 μm polyether sulfone filters were purchased from ANPEL (Shanghai, China). 5 μm hydrophilic

Table 2 Possibilities of particle coincidence for different dwell time. Dwell time (ms)

3 5 10

P(0)

P(1)

P(N1)

Percentage of coincidence p(N1)/((p(1) + p(N1)) × 100

Calculated

Measured

Calculated

Measured

Calculated

Measured

Calculated

Measured

91.42 82.90 75.84

91.72 85.31 79.19

8.20 15.55 20.98

7.93 13.55 18.48

0.38 1.55 3.19

0.35 1.14 2.33

0.044 0.091 0.13

0.043 0.077 0.11

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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Fig. 2. Effects of different number concentrations on signal intensity–frequency (dwell time: 3 ms).

cellulose ester, glass fiber and polypropylene membranes were purchased from HaiNing medical chemical plant (Zhejiang, China). An Agilent 7700 × quadrupole ICP-MS with MicroMist nebulizer (Agilent, USA) was used for data acquisition. Sample detection was set in time resolved analysis (TRA) mode with 3 ms as dwell time and 60 s as acquisition time for each sample. Instrumental parameters for SP-ICPMS were described at Table 1. The instrument was tuned daily using tune solution (1 μg/L Li, Co, Y, Tl, Ce and Ba, Agilent, USA in 2% v/ v HNO3 solution) to obtain maximum sensitivity and minimum double oxide level (b 2%) before analysis. All SP-ICPMS acquisition data were recorded by MassHunter Workstation (Agilent, USA) and exported into worksheet and excel (Microsoft, USA) was used for further processing. The concentration of common dissolved elements was measured using Inductively Coupled Plasma Optical Emission Spectroscopy (ICPOES) (Perkin Elmer 8000). Samples were digested according to U.S.EPA method 3010a. Anion concentrations were determined using ion chromatography (Dionex ICS-90) according to the method of U.S.EPA method 300.1. Water composition of samples can be seen from Table SI-1. 2.4. Date analysis The first step for determining particle size distribution was to separate the nanoparticle pulse from background signal. An iterative algorithm based on 5σ criterion (σ was the standard deviation of dataset) was used to discriminate particle pulse and background signal. The

steps of the iterative algorithm were described as below: the average intensity (μ) and standard deviation (σ) of the whole dataset was calculated, the data points exceeding (μ + 5σ) were considered nanoparticle signals and were removed. Calculation of (μ + 5σ) was then repeated multiple times, and the data above the new (μ + 5σ) value were removed until no further data points were removed. The remaining dataset was considered as background signal. Tuoriniemi et al. pointed out that 5σ iterative algorithm criterion can ensure that contribution of false positives is less than 0.1% of the actual particle events and retain a sufficiently high particle count over a broad concentration range (Tuoriniemi et al., 2012). The same dissolved metal standards were also determined using ICP-MS in single particle mode to establish a calibration curve. The relationship between nanoparticle intensity and size distribution can be explained as follows (Pace et al., 2011): f NP ¼ NNP  Q sam  ηn

ð1Þ

W ¼ ηn  Q sam  tdwell C

ð2Þ

mNP ¼

Spulse −Sbkgd  ηi m  fm

ð3Þ

Table 3 Possibilities of particle coincidence for different nanoparticle number concentration. Nanoparticle number concentration

P(0)

P(1)

P(N1)

p(N1)/((p(1) + p(N1)) ×

(particles/mL)

Calculated

Measured

Calculated

Measured

Calculated

Measured

Calculated

Measured

5500 11,000 22,000 44,000 88,000 220,000

98.88 97.78 95.61 91.42 83.57 63.84

98.97 97.71 95.94 91.72 85.06 70.46

1.11 2.19 4.29 8.20 15.00 28.65

1.03 2.26 3.97 7.93 13.76 24.67

0.0062 0.025 0.098 0.38 1.43 7.51

0.0053 0.026 0.083 0.35 1.18 4.87

0.0056 0.011 0.022 0.044 0.087 0.21

0.0052 0.012 0.021 0.043 0.079 0.16

100

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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Table 4 Determination of commercial silver and gold particle sizes (n = 3).

AgNPs 30 nm AgNPs 50 nm AgNPs 80 nm AgNPs 100 nm AuNPs 30 nm AuNPs 60 nm AuNPs 80 nm AuNPs 100 nm

Average size (nm)

Median size (nm)

TEM (nm) (manufacturer)

33.37 ± 1.13 53.69 ± 1.14 70.23 ± 0.57 101.35 ± 2.14 33.49 ± 0.27 61.58 ± 1.00 80.63 ± 0.45 98.16 ± 0.42

32.69 ± 0.91 54.11 ± 1.11 71.30 ± 0.54 106.83 ± 2.10 32.48 ± 0.53 62.10 ± 0.78 80.53 ± 0.70 98.03 ± 1.09

32.3 ± 3.2 53.4 ± 4.4 79.0 ± 8.7 99.6 ± 7.0 28.6 ± 3.2 61.2 ± 6.0 75.4 ± 9.5 101.4 ± 14.5

3. Results and discussion 3.1. Optimization of SP-ICPMS Fig. 3. 60 nm AuNPs number concentration VS number of events(■ represented 200 ng/L 60 nm AuNPs □ represented 500 ng/L 60 nm AuNPs).

dNP ¼

sffiffiffiffiffiffiffiffiffiffiffiffi 3 6mNP πρ

ð4Þ

Where ηn is the transport efficiency, Qsam is the sample flow rate (mL min−1), tdwell is the dwell time (ms per event), fNP is the average number of standard nanoparticle pulses (no. of pulses per event) and NNP is the number concentration of nanoparticles. The transport efficiency ηn can be calculated based on Eq. (1) if the number concentration of standard AuNPs is known. C is the analytical metallic calibration curve and the mass flux calibration curve W can be calculated based on Eq. (2). Spulse corresponds to the nanoparticle intensity, Sbkgd is the background intensity, fm is the mass fraction of analytical element (fm = 1 when the analytical element is Ag or Au) and m is the slope of mass flux calibration curve W. ηi is the particle ionization efficiency, and the ηi for silver and gold nanoparticle is 100% (Laborda et al., 2011). The ionization efficiency will need to be determined if the particles are not ionized fully into the plasma. One way to detect the ionization efficiency ηi is to compare the mass concentration of acid-digested samples with the undigested ones. The nanoparticle mass mNP is calculated based on Eq. (3). Assuming that nanoparticles are monodisperse and spherical, the diameter of the nanoparticle dNP (nm) can be determined using the nanoparticle mass (Eq. (4)) where ρ is the particle density (g/cm3).

3.1.1. The effect of dwell time The theoretical basis of SP-ICPMS is illustrated as below: the metalbased nanoparticles are distributed randomly in the diluted aqueous solution and every nanoparticle contains hundreds to thousands of metal atoms. When one nanoparticle is introduced into plasma, a cluster of metal ion will be generated and produced signal pulse in the short dwell time. Therefore, short dwell time and low nanoparticle number concentration are the two key factors to make sure that only one nanoparticle enter into plasma. If two or more particles enter into plasma, the signal produced could not be distinguish from that of other large nanoparticle. The 60 nm AuNP solution at concentration of 100 ng/L (number concentration approximately at 4.4 × 104 particles/mL) was chosen for analysis with dwell time of 3 ms, 5 ms, 10 ms and 20 ms. The signal distribution of 60 nm AuNPs with different dwell time was illustrated at Fig. 1a–d. Longer dwell times will increase the probability of more than one particle reaching the ICP in one dwell time/integration window (“particle coincidence”). Signal distribution of 400–800 counts per dwell time was observed in Fig. 1c (10 ms) and Fig. 1d (20 ms). However this signal distribution was small in Fig. 1a, it indicated that the possibility of particle coincidence was smallest at the dwell time of 3 ms. The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time, if these events occur with a known average rate and independently of each other. In SP-ICPMS analysis, one nanoparticle arrived at plasma randomly during a fixed dwell time. The Poisson statistics was used to estimate the possibility of particles reaching plasma during a dwell time (Laborda et al., 2013; Liu et al., 2014a; Olesik and Gray, 2012; Pace et al., 2012). The possibility P(x) of obtaining one signal from x nanoparticles in a fixed dwell time was outlined as below equation: Px ¼

Fig. 4. Raw signal of the mixed solution of 25 ng/L 60 nm AuNPs and 1 μg/L dissolved Au ion at different dwell time.

λx −λ e x!

ð5Þ

where λ was the average number of nanoparticle pluses reaching plasma per dwell time and calculated by λ = f(p) × tdwell. Nanoparticle flux f(p) was calculated by f(p) =NP × Qsam × ηn° Np is the standard nanoparticle number concentration (particles/mL). Qsam is the sample flow rate (mL/min). x is the number of nanoparticles. The possibility of zero nanoparticle entering into plasma per dwell time p(0), one nanoparticle per dwell time p(1) and more than one nanoparticles per dwell time p(N1) were calculated according to Eq. (5). The 60 nm AuNP solution of 100 ng/L (number concentration was approximately 4.4 × 104 particles/mL) was used to calculate the Poisson distribution and the dwell time was fixed at 3 ms, 5 ms, 10 ms respectively. Table 2 gives the estimated possibilities of particles coincidence at dwell time of 3 ms, 5 ms and 10 ms. Results show that the value of p(N1)/((p(1) + p(N1)) × 100 went up with the increase of dwell

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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Fig. 5. Size distribution of the silver nanoparticle and gold nanoparticle (a: 10 ng/L 50 nm AgNPs mixing with 20 ng/L 100 nm AgNPs, b: 12.5 ng/L 60 nm AuNPs mixing with 25 ng/L 100 nm AuNPs).

time. So the short dwell time of 3 ms was selected to reduce the possibility of particles coincidence. 3.1.2. Upper limit of the concentration The nanoparticle coincidence means two or more nanoparticles reaching into plasma at the same dwell time. The framework of SPICPMS theory was built on the hypothesis that only one nanoparticle entering into plasma during a dwell time. Therefore nanoparticle number concentration should be low enough to make sure that only one nanoparticle is detected by mass detector during the analysis process of SPICPMS. In other words, the upper limit of particle number concentration should be high enough to ensure that no particle coincidence happens. The signal distribution of 60 nm AuNPs for different number concentrations was showed in Fig. 2 (a)–(d). The signal distribution between 400 counts and 600 counts of AuNPs was more obvious at the number concentration of 2.2 × 108 particles/L (mass concentration: 500 ng/L), indicating that the possibility of particle coincidence increased at this number concentration. As each nanoparticle was supposed to disperse randomly in diluted solution and each particle pulse represented an individual or discrete event occurring during a fixed dwell time, the Poisson statistics was also used to estimate the upper limit of number concentration. When the dwell time was fixed at 3 ms and the acquisition time was set at 60 s, 18,751 data points was got for each analysis. The possibility of zero nanoparticle entering into plasma per dwell time P(0) and one

nanoparticle per dwell time P(1) can be calculated according Eq. (5). The possibility of two or more particles entering into plasma (i.e. P(N1)) was equal to one minus the sum of P(0) and P(1). Table 3 showed the possibilities of particle coincidence for different nanoparticle number concentrations. The results indicated that the sum of P(0) and P(1) was close to 1 at low nanoparticle number concentration. The percentage of coincidence p(N1) / ((p(1) + p(N 1)) × 100 went up with the increase of number concentration. Therefore the number concentration of nanoparticle should be low enough to guarantee less possibilities of the particle coincidence. It suggests that the nanoparticle pulse numbers should be less than 10% of total readings for each dwell time (Pace et al., 2012). Actually, deviation will be observed as the particle concentration increases (Eq. (1)) if nanoparticle coincidence becomes obvious. A calibration curve of 60 nm AuNPs was established by plotting the numbers of nanoparticle pulses as a function of the particle number concentration (particle concentration was as follows: 800, 5500, 11,000, 22,000, 44,000 particles/mL and the mass concentration was 2, 12.5, 25, 50, 100 ng/L respectively). As can be seen from Fig. 3, good linear regression was obtained (correlation coefficient R2 = 0.9992). The measured results of particle number concentration of 88,000 and 220,000 particles/mL (200 and 500 ng/L) were 81,507 ± 162.56 and 175,929 ± 1921 particles/mL and the biases were −7.38 ± 0.18% and −20.03 ± 0.87% respectively. The measured result of 220,000 particles/mL AuNPs was deviated from calibration curve obviously. Results showed

Table 5 Determination of the nanoparticle number concentration of AgNPs and AuNPs (n = 3). Suspensions

Nominal (particles L−1)

Measured (particles L−1)

Recoveries (%)

50 nm + 100 nm AgNPs 60 nm + 100 nm AuNPs

1.62 × 107 1.04 × 107

1.43 × 107 ± 0.04 × 107 9.58 × 106 ± 0.49 × 106

88.27 ± 2.62 92.14 ± 4.75

Fig. 6. The raw signal intensity of 10 ng/L 50 nm Ag nanoparticle suspension (a) and 12.5 ng/L 60 nm Au nanoparticle suspensions (b).

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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Fig. 7. The signal intensity of 10 ng/L silver nanoparticle suspensions after filtrating through 0.22 μm (a), 0.45 μm (b),1 μm (c), 5 μm (d) nylon membrane respectively.

that particle coincidence where two or more particles was wrongly counted as one pulse as particle number concentration increasing. It indicated that the dynamic range of SP-ICPMS was approximate two orders of magnitude (2–200 ng/L, 60 nm AuNPs). The dynamic range of SP-ICPMS was approximate two orders of magnitude (5–500 ng/L, 100 nm AgNPs) (Fig. SI-1). 3.1.3. Identification of nanoparticle signal After nebulization of nanoparticle solution mixing with the metal ion, the dissolved metal ion was distributed homogeneously in the same aerosol drop. The background signal was effected by them (Laborda et al., 2011). 5σ iterative algorithm criterion and short dwell

time are two ways to discriminate the nanoparticle signal from the dissolved metal ion. The raw signals of the mixed solution of 25 ng/L 60 nm AuNPs and 1 μg/L Au+ under different dwell time were shown in Fig. 4. When the dwell time was fixed at 20 ms or 10 ms, AuNPs and dissolved Au signal was not distinguished well with the 5σ iterative algorithm criterion. It showed that 5σ iterative algorithm criterion could not distinguish the signal of nanoparticle and dissolved metal ion effectively when the dwell time was long. As the dwell time being reduced to 3 ms, the background signal decreased while the AuNPs signal changed only a little. It means that the nanoparticle and background signal can easier be discriminated based on 5σ iterative algorithm criterion with a short dwell time. Hence the choice of short dwell time was a better

Fig. 8. The signal intensity of 12.5 ng/L gold nanoparticle suspensions after filtrating through 0.22 μm (a), 0.45 μm (b), 1 μm (c), 5 μm (d) nylon membrane respectively.

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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Table 6 Recoveries of AgNP and AuNP number concentration after filtrating through different materials of membrane(n = 3). Different materials of membrane

12.5 ng/L 60 nm AuNPs

10 ng/L 50 nm AgNPs

5 μm hydrophilic cellulose ester membrane 5 μm glass fiber membrane 5 μm polypropylene membrane 5 μm nylon membrane 0.45 μm polyether sulfone membrane

bMDL 61.19 ± 1.07% 75.7 ± 0.92% bMDL bMDL

bMDL 20.01 ± 4.00% 16.98 ± 0.22% bMDL bMDL

way to identify dissolved metal ion and nanoparticle signal due to the decreased intensity of dissolved analytical element (Montaño et al., 2014). The detection limit of nanoparticle size (LODsize) was calculated according to the background signal plus 5 times standard deviation. The LODsize of AgNPs and AuNPs in ultrapure water are 20 nm and 19 nm at the dwell time of 3 ms. The signal of dissolved ion interfered the nanoparticle signal. The LODsize became higher due to the existence of high concentration dissolved ion. 3.2. Nanoparticle size characterization Assuming that the nanoparticle shape is spherical, the mass distribution can be calculated from the net intensity according to the Eq. (3). Then the size distribution of nanoparticles was transformed according to the Eq. (4). The size distribution of gold and silver nanoparticle determined by SP-ICPMS using optimal dwell time of 3 ms was showed in Table 4. The measured size results were consistent with the TEM value provided by the manufacturer. Fig. 5a displayed the size distribution of the mixtures of 50 nm AgNPs (10 ng/L) and 100 nm AgNPs (20 ng/L). And Fig. 5b showed the size distribution of the mixtures of 60 nm AuNPs (12.5 ng/L) and 100 nm AuNPs (25 ng/L). As can be seen from Fig. 5, the SP-ICPMS method had good resolution to discriminate nanoparticles of different diameter and no aggregation was significantly observed, demonstrating that the SP-ICPMS is a useful method for the characterization of polydisperse NP systems. 3.3. Nanoparticle number concentration By rearranging Eq. (1), AgNP and AuNP number concentration (NNP) was calculated using the transport efficiency (ηn), sample flow rate Qsam (mL min−1), and numbers of Ag and Au particle pulses (fNP), based on Eq. (6). The transport efficiency (ηn) could be measured using gold reference nanoparticles with known particle number concentration, ηn can also be applied to the characterization of AgNPs.

NNP ¼

f NP Q sam  ηn

ð6Þ

Table 5 showed detecting results of number concentration of the mixtures of nanoparticles of different sizes in ultrapure water. A good agreement with the nominal particle number concentration and good recoveries were obtained. Table 7 Average diameters of AgNPs and AuNPs spiked in natural and effluent water in the first day (n = 3).

AgNPs 50 nm AgNPs 100 nm AuNPs 60 nm AuNPs 100 nm

TEM(nm) (manufacturer)

Average diameters(nm) HuNan University effluent

Xiangjiang River

53.4 ± 4.4 99.4 ± 7.0 61.2 ± 6.0 101.4 ± 14.5

52.92 ± 0.46 97.01 ± 0.78 62.03 ± 0.92 101.53 ± 1.41

52.18 ± 0.10 97.11 ± 1.25 63.24 ± 0.77 99.16 ± 1.52

LODNP was the detection limit of nanoparticles number concentration. Laborda et al. (2013) proposed that LODNP was related to the ability of three nanoparticle events. The theoretical concentration detection limit of SP-ICPMS in ultrapure water was 8 × 104 particles L−1. It is better to qualify NPs number concentration 5 to 10 times of LODNP. 3.4. Influence of filtration on nanoparticle Environmental water is a complex matrix that contains innumerable natural nanoparticles of different sizes, compositions and shapes. The microfiltration with greater than 0.1 μm pore sizes was widely used to remove large colloids to prevent nebulizer jamming in the ICP-MS analysis. A series filter membranes with different pore sizes were used to investigate the influence of filtration on the determination of nanoparticle's size distribution at low number concentration. It could indicate whether filtration is a proper way to detect nanoparticle in the real environmental waters with SP-ICPMS. The samples of 10 ng/L 50 nm AgNPs and 12.5 ng/L 60 nm AuNPs were filtered through 0.22, 0.45, 1, 5 μm membranes respectively and analyzed immediately using SP-ICPMS. The raw signal intensity of 10 ng/L 50 nm AgNPs and 12.5 ng/L 60 nm AuNPs obtained as a function of time by SP-ICPMS is illustrated in Fig. 6. A large number of pulses of the silver and gold nanoparticles larger than the baseline were observed. This is because that a cluster of metal ions were generated and analyzed by the mass spectrometer when the NPs in diluted solution were introduced into the plasma. However, the signal distribution was small and homogeneous after filtration; few strong pluses were observed in Fig. 7 and Fig. 8. The results in Table 6 showed that low recoveries of AgNPs and AuNPs were obtained with these five types of filters. It indicated that the NPs could not pass through these filters, although the NPs diameter was smaller than the pore size of the nominal filter membrane. The NPs were blocked to the membrane surfaces due to electrostatic attraction (Hassellov et al., 2008; Mitrano et al., 2012). The nanoparticles which were smaller than the pore size transported through the membrane more slowly than the liquid did. This was because that the electrostatic repulsion could lead to higher collision rates between particles and aggregation. As a result high-efficiency trapping of nanoparticles and aggregates occurred on the membranes (Hassellov et al., 2008). This study indicated that filtration might be a problematic pre-treatment method and it can't be applied in the sample preparation for the detection of nanoparticle in natural water. 3.5. Stability of AgNPs and AuNPs in the water To study the stability of NPs in the water, 50 nm and 100 nm citratecapped AgNPs and 60 nm 100 nm tannic-capped AuNPs were spiked in the filtered natural water and effluent water respectively, and the NPs were investigated during a ten-day incubation period. The average diameters of AgNPs and AuNPs after first day were presented in the Table 7. Table SI-3 showed particle number concentration recoveries of AuNPs and AgNPs spiked into filtered HuNan University effluent and Xiangjiang River after first day. It indicated that silver and gold nanoparticles in complex matrix were stable at the first day which were in accordance with Telgmann's study (Telgmann et al., 2014). The variations of silver and gold nanoparticles in the environmental waters were showed in Fig. 9 (a)–(b). The particle diameters of AgNPs decreased approximately 10% during a ten-day period in both of the natural water and effluent water (Fig. 9(a)). It indicated that dissolution occurred when AgNPs were dispersed in environmental water at low number concentration. This was consistent with the results of Furtado's study (Furtado et al., 2014). The interactions between particles were infrequent at low concentrations limiting the rate of homoagglomeration (Furtado et al., 2015). Ag+ was then released into solution through the oxidation and dissolution of AgNPs (Li and Lenhart, 2012). Dissolution happened when AgNPs were dispersed in synthetic water (Table SI-3). As for AuNPs, both of the 60 nm and 100 nm AuNPs spiked into the

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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Fig. 9. Evolution of average diameter during a ten-day incubation period for suspensions of (a) ◆ 40 ng/L 100 nm AgNPs spiked into Xiangjiang River, ◇ 40 ng/L 100 nm AgNPs spiked into HuNan University effluent, ● 10 ng/L 50 nm AgNPs spiked into Xiangjiang River, ○ 10 ng/L 50 nm AgNPs spiked into HuNan University effluent (b) ▲ 50 ng/L 100 nm AuNPs spiked into HuNan University effluent, △ 50 ng/L 100 nm AuNPs spiked into Xiangjiang River,▼ 25 ng/L 60 nm AuNPs spiked into HuNan University effluent, ▽ 25 ng/L 60 nm AuNPs spiked into Xiangjiang River. Error bars represent the standard deviation of triplicate experiments.

two waters were relatively stable (Fig. 9(b)). It indicated that no dissolution or aggregation happened when gold nanoparticles were spiked into the environmental water at low number concentration. AuNPs was also stable in synthetic matrix (Table SI-4). Dan (Dan et al., 2015) pointed out that AuNPs can be extracted from tomato tissues as intact particles and no dissolution or aggregation happened after tomato plants being exposed to 5 mg/L of 40 nm AuNPs for 4 days. And AuNPs capped with PVP do not aggregate at environmental relevant

conditions and do not interact significantly with natural organic macromolecules (Hitchman et al., 2013). 3.6. Analysis of AgNPs and AuNPs in environmental samples using SPICPMS Samples were placed in 4°C for several hours prior to analysis to deposit the large colloids. Before analysis, the Guiyu River and Chendian

Fig. 10. 107Ag raw signal of (a) Guiyu River (b) Chendian Lake (c) HuNan University effluent (d) Xiangjiang River.

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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Y. Yang et al. / Science of the Total Environment xxx (2016) xxx–xxx

Fig. 11. 197Au raw signal of (a) Guiyu River (b) Chendian Lake (c)HuNan University effluent (d)Xiangjiang River.

Lake samples were diluted 20 times to make sure that the particle events less than 10% of the total reading. The raw data of 107Ag and 197 Au in the water samples were showed in Fig. 10 (a)–(d) and Fig. 11 (a)–(d) respectively. Numerous NP pulses of Ag and Au were observed in surface water (Guiyu River # and Chendian Lake #). Assuming that the nanoparticle shape was spherical, the 107Ag and 197Au of size distribution histograms of Guiyu River # and Chendian Lake # would be calculated based on optimized SP-ICPMS theory, results were shown in

Fig. 12(a)–(d). The particle number concentrations of AgNPs were 7.04 × 107 ± 0.42 × 107 and 4.04 × 107 ± 0.11 × 107 partilces/L for Chendian Lake and Guiyu River respectively, and particle number concentrations of AuNPs were 6.38 × 107 ± 1.12 × 107 and 6.48 × 107 ± 0.4 × 107 partilces/L for Chendian Lake and Guiyu River respectively. It confirmed that AgNPs and AuNPs were existed in Guiyu River and Chendian Lake, due to the open burning, acid baths and toxic dumping pour pollution of dismantle old electronics. Meanwhile, few pulses were

Fig. 12. (a) AgNPs size distribution histograms of Guiyu River (b) AgNPs size distribution histograms of Chendian Lake (c) AuNPs size distribution histograms of Guiyu River (d) AuNPs size distribution histograms of Chendian Lake.

Please cite this article as: Yang, Y., et al., Analysis of silver and gold nanoparticles in environmental water using single particle-inductively coupled plasma-mass spectrometr..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2015.12.150

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found in the samples of HuNan effluent University and Xiangjiang River samples. It indicated that few particles existed in the Xiangjiang River and effluent water of the HuNan University. 4. Conclusion It is crucial to develop an accurate and fast method for the characterization and quantitative determination of nanoparticles to assess their environmental risk. In this work, SP-ICPMS technique was carried out to analyze the size distribution and particle number concentration of AgNPs and AuNPs in natural water. Short dwell time and low particle number concentration were adopted to guarantee a small possibility of the particle coincidence. The concentration of dynamic range of SPICPMS was two orders of magnitude. The detection limit of nanoparticles size for AgNPs and AuNPs in ultrapure water was 20 nm and 19 nm respectively and the detection limit of particle number concentration in ultrapure water was 8 × 104 particles/L. The stability of AgNPs and AuNPs under realistic conditions was studied and dissolution occurred when the AgNPs was dispersed in synthetic and natural water at low particle number concentration. Findings of this work provided insight for the development of a robust technique for the detection of metallic nanoparticles in natural environment. Conflict of interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in or the review of the manuscript entitled. Acknowledgments This study was financially supported by the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20120162110019), the National Natural Science Foundation of China (Grant No. 21407182 and 21277175), and Shenzhen Special Fund for Development of Strategic Emerging (Grant No. JCYJ20120618164317119). Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2015.12.150. References Chinnapongse, S.L., MacCuspie, R.I., Hackley, V.A., 2011. Persistence of singly dispersed silver nanoparticles in natural freshwaters, synthetic seawater, and simulated estuarine waters. Sci. Total Environ. 409, 2443–2450. Dan, Y., Zhang, W., Xue, R., Ma, X., Stephan, C., Shi, H., 2015. Characterization of gold nanoparticle uptake by tomato plants using enzymatic extraction followed by singleparticle inductively coupled plasma-mass spectrometry analysis. Environ. Sci. Technol. 49, 3007–3014. Degueldre, C., Favarger, P.Y., 2004. Thorium colloid analysis by single particle inductively coupled plasma-mass spectrometry. Talanta 62, 1051–1054. Degueldre, C., Favarger, P.Y., Bitea, C., 2004. Zirconia colloid analysis by single particle inductively coupled plasma-mass spectrometry. Anal. Chim. Acta 518, 137–142. Degueldre, C., Favarger, P.Y., Wold, S., 2006. Gold colloid analysis by inductively coupled plasma-mass spectrometry in a single particle mode. Anal. Chim. Acta 555, 263–268. Diegoli, S., Manciulea, A.L., Begum, S., Jones, I.P., Lead, J.R., Preece, J.A., 2008. Interaction between manufactured gold nanoparticles and naturally occurring organic macromolecules. Sci. Total Environ. 402, 51–61. Donovan, A.R., Adams, C.D., Ma, Y., Stephan, C., Eichholz, T., Shi, H., 2015. Single particle ICP-MS characterization of titanium dioxide, silver, and gold nanoparticles during drinking water treatment. Chemosphere 144, 148–153. Farré, M., Sanchís, J., Barceló, D., 2011. Analysis and assessment of the occurrence, the fate and the behavior of nanomaterials in the environment. TrAC Trends Anal. Chem. 30, 517–527.

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