Characterizing Concentrations 

and Size Distributions of 

Metal-Containing Nanoparticles 

in Waste Water 

APM 272 

R E S E A R C H A N D D E V E L O P M E N T 

EPA/600/R-10/117 

October 2010 

www.epa.gov 

Characterizing Concentrations 

and Size Distributions of 

Metal-Containing Nanoparticles 

in Waste Water 

APM 272 

Edward M. Heithmara
 

Spiros A. Pergantisb
 

a

U.S. Environmental Protection Agency 

National Exposure Research Laboratory 

Environmental Sciences Division 

944 E. Harmon Ave. 

Las Vegas, NV 89119 

b

NRC Senior Research Fellow 

U.S. Environmental Protection Agency 

National Exposure Research Laboratory 

Environmental Sciences Division 

944 E. Harmon Ave. 

Las Vegas, NV 89119 

Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official 

Agency policy. Mention of trade names and commercial products does not constitute endorsement or 

recommendation for use. 

U.S. Environmental Protection Agency 

Office of Research and Development 

Washington, DC 20460
 

Abstract 

 Nanomaterials containing metals are finding increasing use in consumer, industrial, and 

medical products, and they are subsequently being released into the environment. Methods for 

detecting, quantifying, and characterizing these materials in complex matrices are critical for the 

eventual understanding of their implications to environmental quality and human health. This 

report describes recent progress in the development of new metrology tools. Single particle – 

inductively coupled plasma mass spectrometry (SP-ICPMS) is used to analyze complex aqueous 

samples. SP-ICPMS simultaneously quantifies the concentration of nanoparticles containing an 

analyte metal and measures the metal mass in individual nanoparticles. The accuracy of SPICPMS is assessed over a range of nanoparticle sizes. The utility of the technique in a number of 

applications is examined. Nanoparticles containing the analyte element can be measured 

accurately and precisely in the presence of a 10,000-fold greater concentration of other 

nanoparticles. SP-ICPMS is used to assess transformation of nanoparticles induced by changes 

in ionic strength. A screening-level assessment of metal-containing nanoparticles in urban runoff using SP-ICPMS is demonstrated, and preliminary studies of coupling SP-ICPMS with online nanoparticle size separation methods are presented. 

ii 
Notice 

The United States Environmental Protection Agency’s (EPA) Office of Research and 

Development performed and funded the research described here. Mention of trade names or 

commercial products does not constitute endorsement or recommendation for use. 

iii 
Table of Contents 

Abstract........................................................................................................................................... ii 

Notice............................................................................................................................................. iii 

Table of Contents........................................................................................................................... iv 

Abbreviations and symbols ............................................................................................................. v 

Acknowledgments.......................................................................................................................... vi 

Introduction..................................................................................................................................... 1 

Uses of engineered nanomaterials. ............................................................................................. 1 

Environmental implications of increased use of engineered nanomaterials ............................... 2 

Rationale for the development of metrology tools for metal-containing ENMs ........................ 3 

Methods for characterizing engineered nanomaterials ............................................................... 4 

Theory of SP-ICPMS.................................................................................................................. 7 

Experimental ................................................................................................................................... 9 

Materials ..................................................................................................................................... 9 

Instrumentation ........................................................................................................................... 9 

Methods....................................................................................................................................... 9 

Results and Discussion ................................................................................................................. 10 

Assessment of SP-ICPMS accuracy ......................................................................................... 10 

Precision and selectivity of SP-ICPMS .................................................................................... 11 

Nanoparticle transformation study ............................................................................................ 11 

Screening urban runoff using SP-ICPMS................................................................................. 12 

Coupling SP-ICPMS with separation methods......................................................................... 13 

Conclusions and Future Work ...................................................................................................... 14 

References..................................................................................................................................... 16 

Tables............................................................................................................................................ 22 

Figure Captions............................................................................................................................. 26 

 

iv 
Abbreviations and symbols 

AFM Atomic force microscopy 

ca Analyte concentration in aqueous sample (g mL-1) 

CAPS 3-(cyclohexylamino)propanesulfonic acid 

CDF Cumulative distribution function 

CE Capillary electrophoresis 

cp Particle concentration in aqueous sample (mL-1) 

DLS Dynamic light scattering 

EDS Energy dispersive X-ray spectrometry 

EHS Environmental health and safety 

εn Nebulization transport efficiency (dimensionless) 

ENM Engineered nanomaterials 

EPA Environmental Protection Agency 

ESD Environmental Sciences Division 

FFF Field flow fractionation 

Flow-FFF Flow field flow fractionation 

HDC Hydrodynamic chromatography 

ICPMS Inductively coupled plasma mass spectrometry 

IRZ Initial radiation zone 

ma,p Analyte element mass in the particle (g) 

NAZ Normal analytical zone 

n Number of analyte ions detected (unitless number) i,p 

NOM Natural organic matter 

NTA Nanoparticle tracking analysis 

qi,a Ionized analyte flux (s-1) 

qp Particle flux (s-1) 

qs Sample uptake rate (mL s-1) 

rg Radius of gyration 

rh Hydrodynamic radius 

SDS Sodium dodecyl sulfate 

Sed-FFF Sedimentation field flow fractionation 

SEM Scanning electron microscopy 

SLS Static light scattering 

SP-ICPMS Single particle - inductively coupled plasma mass spectrometry 

td Dwell time (s) 

TEM Transmission electron microscopy 

τp Plume pulse transit time (s) 

v 
Acknowledgments 

I am grateful to PerkinElmer for providing the DRCe inductively coupled plasma mass 

spectrometer used for some of this research. Charlita Rosal of EPA developed and performed 

the capillary electrophoresis separation shown in this report. 

vi 
Introduction 

The National Nanotechnology Initiative defines nanotechnology as “the understanding and 

control of matter at dimensions between approximately 1 and 100 nanometers”(Council 2010), 

and nanomaterials are defined as materials with at least one characteristic dimension in this 

range. Nanomaterials can be natural (e.g., humic and fulvic acids) (Thurman and Malcolm 

1981), incidental to human activity (e.g., diesel emissions, welding fumes) (Peters, Elzey et al. 

2009), or engineered. Engineered nanomaterials (ENMs) exhibit optical, electrical, and chemical 

characteristics different from either their bulk or dissolved forms. Because of this, they are the 

source of the so called nanotechnology revolution. Most ENMs can be divided into two general 

classes, depending on whether they are carbon-based (e.g., carbon nanotubes and fullerenes) or 

metal-containing (e.g., Ag, TiO2, CeO2, Fe). 

Uses of engineered nanomaterials. 

The use of ENMs in consumer, industrial, and agricultural products, as well as in 

environmental technology is rapidly increasing (Ponder, Darab et al. 2001; Chaudhry, Scotter et 

al. 2008; Nanotechnologies 2010). This is largely due to the unique optical, electrical, and 

chemical properties of nano-scale particles. Often, the benefit of using nanomaterials stems from 

the increased surface area per unit mass of material, which increases with the inverse of the 

diameter in the case of spherical particles. This results in faster rates of chemical reactions such 

as oxidative catalysis that occur at surfaces. Sometimes the benefit of nanomaterials arises from 

the quantum nature of energy states at the nanometer scale, as in the wavelength tuning of the 

fluorescence of quantum dots (Michalet, Pinaud et al. 2005). In the health sciences, the ability of 

ENMs to bind to cell walls can be utilized for drug delivery (Lai, Trewyn et al. 2003; Zhang, 

Pornpattananangkul et al. 2010). 

The Project on Emerging Nanotechnologies maintains a database of consumer products that 

manufacturers claim contain ENMs (Nanotechnologies 2010). Currently the database contains 

over 1000 products. Consumer products containing nano-scale silver dominate current ENM 

usage (Table 1). Note that of the carbon-based ENMs, nearly 3⁄4 of the products are durable 

goods that incorporate carbon nanotubes in the construction material. The nanotubes are bound 

in this matrix for the lifetime of the product. Conversely, the metal-containing ENMs are most 

often used in dispersive applications, where they are intentionally released from the product or 

where incidental release is substantial. For example, fabrics containing nano-scale silver release 

silver during washing at varying rates, depending on the type of fabric and the washing 

conditions (Benn and Westerhoff 2008; Geranio, Heuberger et al. 2009). Of the products using 

carbon-based ENMs only 7 very low-volume uses of fullerenes are documented. Although there 

is only one product listed using cerium oxide, it is as a diesel fuel additive that potentially could 

entail high-volume dispersive use. 

1 
Environmental implications of increased use of engineered 

nanomaterials 

The unusual properties that result in unique benefits from the use of ENMs have also elicited 

intense interest in the environmental behavior of these materials. The environmental transport, 

transformation, fate, exposure potential, and effects of ENMs cannot be predicted by the 

behavior of either the corresponding dissolved or neat chemicals. In addition, the metrics that 

affect all these environmental properties are different and more complex than those of 

conventional pollutants. The number of funded research projects on the environmental health 

and safety (EHS) implications of ENMs (Figure 1) increased dramatically from 1998 to 2007 

(Nanotechnologies 2010). There has been some decline in newly initiated projects in the past 

two years (although data for 2010 are incomplete); nonetheless, research into EHS of ENMs 

remains intense. The level of research activity is roughly evenly apportioned to carbon-based 

and metal containing ENMs. For the former, there are currently about 54 investigations of 

nanotubes and 29 of fullerenes. Of the metal-containing ENMs, most investigations involve 

titanium dioxide and iron, followed by quantum dots, silver, and then the remaining metal 

oxides. The interest in carbon nanotubes stems from the known toxicity of these materials that is 

mediated by the particle aspect ratio. Research focus on the metal-containing ENMs is prompted 

by their high likelihood to be released from products. This leads to a greater potential loading of 

the metal-containing ENMs in environmental compartments. 

An essential capability for any successful research into the environmental behavior of ENMs 

is a set of metrology tools for the several metrics of the materials that control their release 

transport, transformation, fate, and effects. These tools would also be required for determining 

the occurrence and distribution of ENMs in the environment, and they would permit the temporal 

trends in these to be assessed. A previous report by the Environmental Sciences Division (ESD) 

described the importance of characterization of metal-containing ENMs in the exposure research 

of these potential environmental stressors (Heithmar 2009). It discussed the several methods 

currently available for the characterization of various exposure metrics and why the current 

methods are inadequate. The report introduced single particle – inductively coupled plasma 

mass spectrometry (SP-ICPMS) as a method for both screening-level and selective determination 

of metal-containing ENM dispersions. The potential of SP-ICPMS to become the first practical 

analytical method for characterization of metal-containing ENMs in environmental matrices was 

demonstrated. 

This report describes recent progress in the development of SP-ICPMS, which can 

simultaneously quantify the concentration of nanoparticles containing an analyte metal and 

measure the metal mass in individual nanoparticles. The report briefly reviews the current 

metrology methods discussed in the previous report and introduces two not covered there. The 

introduction concludes with a conceptual description of SP-ICPMS, with a real analytical 

example, and the equations and associated assumptions to calibrate the method. The 

experiments discussed in this report first assess the accuracy of SP-ICPMS over a range of 

nanoparticle sizes. The utility of the technique in a number of applications is then examined. 

2 
Nanoparticles containing the analyte element can be measured accurately and precisely in the 

presence of a 10,000-fold greater concentration of other nanoparticles. SP-ICPMS is used to 

assess transformation of nanoparticles induced by changes in ionic strength. Limitations in 

sampling and sample handling of real environmental water samples for nanoparticle 

measurements are demonstrated. A screening-level assessment of metal-containing 

nanoparticles in urban run-off using SP-ICPMS is demonstrated. Finally, preliminary studies of 

coupling SP-ICPMS with on-line nanoparticle size separation methods are presented. 

Rationale for the development of metrology tools for metal-containing 

ENMs 

A 2009 workshop of approximately 50 scientists involved in EHS research of ENMs was 

sponsored by the International Council on Nanotechnology (ICON). The charge of the workshop 

was to identify and rank the key research priorities for informed decisions on developing ENMs 

with minimal potential adverse environmental impacts. The workshop report (Alvarez, Colvin et 

al. 2009) identified fourteen research priorities, which were ranked according to two criteria: (1) 

the importance of each issue in advancing our ability to design ENMs responsibly, and (2) the 

current development of scientific understanding of the issue. Tools to detect, measure and 

characterize ENMs ranked highest in importance. This area was also determined to be the most 

poorly developed. Consequently, development of metrology tools, especially for real 

environmental matrices, was the highest ranked research priority. 

The first reason for the critical need for research to develop metrology tools is their 

central role in every area of EHS research. These areas include (1) measuring the environmental 

occurrence and distribution of ENMs, (2) determining temporal trends in environmental loads, 

(3) laboratory studies of transport, transformation, and fate, and (3) toxicity studies. Ultimately, 

exposure models must be developed for ENMs (Mueller and Nowack 2008), and these will 

require measuring releases for source terms, as well as measuring ENM distributions in the 

environment to verify the models. 

Another reason for developing ENM metrology tools is the complex set of metrics. For 

conventional pollutants, mass concentration is often the only relevant metric for determining 

exposure. In the case of ENMs, exposure potential is affected by particle size. Depending on the 

exposure model, measurement of size distributions in terms of particle mass, volume, or 

equivalent hydrodynamic radius may be most relevant (Hassellöv, Readman et al. 2008). 

Specific surface area (area/mass ratio) is important to the catalytic activity of ENMs such as 

TiO2, and it can influence toxicity (Schulte, Geraci et al. 2008). Surface charge and the related 

property of ζ potential can determine the degree ENMs tend to aggregate (Kim, Lee et al. 2008). 

As previously discussed, particle shape, characterized by aspect ratio, can dramatically affect 

toxicity of carbon nanotubes (Magrez, Kasas et al. 2006; Takagi, Hirose et al. 2008). 

A third factor fueling the high interest in developing ENM metrology tools is the current 

lack of many practical methods. There are a number of techniques for measuring concentrations 

and size distributions of ENMs in pure suspensions used as starting materials in laboratory 

3 
studies. However, very few methods exist for detection, quantification, and characterization of 

suspensions after they are introduced into test systems. Such techniques are critical, because the 

ENM can transform in the test system (Alvarez, Colvin et al. 2009). Going one step further in 

complexity, virtually no practical methods have been published for measuring concentration and 

size distributions of ENMs in real environmental samples (Handy, von der Kammer et al. 2008). 

There are two reasons for this situation. First, concentrations of ENMs in the real environment 

are very low (Benn and Westerhoff 2008). Compounding this low concentration is the presence 

of natural colloids that interfere (Klaine, Alvarez et al. 2008), especially in the case of metalcontaining ENMs. Detection and quantification of metals usually involves measuring total 

element concentration, and natural colloids often include minerals that contain the same element 

as the ENM of interest. In addition, analyte elements can also adsorb on natural organic matter 

(NOM) in colloids. 

Advances in understanding the environmental behavior of metal-containing ENMs can 

only be made if detection, quantification, and characterization methods are developed, especially 

for the concentration and size distribution metrics of the nanomaterials. In the short term, these 

methods must be effective at least in laboratory test systems, and at least screening-level 

methods must be available to detect the probable presence of metal-containing ENMs in real 

environmental samples. Ultimately, selective determinative methods that greatly minimize false 

positives in real-world samples must be developed. The development of characterization 

methods for metrics other than nanoparticle concentration and size distribution may never be 

attainable for these systems. 

Methods for characterizing engineered nanomaterials 

Methods that are currently available to measure several common ENM characterization 

metrics (Table 2, adapted from Heithmar 2009a) generally work well in simple matrices. One 

general class of characterization techniques is single particle imaging. The most common of 

these methods are scanning electron microscopy (SEM) or transmission electron microscopy 

(TEM) (Lin and Yang 2005; Pyrz and Buttrey 2008). In well controlled systems, atomic force 

microscopy (AFM) can be advantageous, because interaction forces between ENMs and 

substrates can be studied (Ebenstein, Nahum et al. 2002). When combined with X-ray emission 

spectroscopy, usually in the energy dispersive mode (EDS), SEM or TEM can definitively 

identify ENMs in a simple sample matrix. However, particle concentration must be generally at 

least 109

 mL-1 with current sampling methods, making the use of this method for 

environmentally relevant concentrations of ENMs infeasible. Representative sampling of the test 

material is generally not attainable, so quantification is impossible. In environmental samples, 

distinguishing metal-containing ENMs from colloids containing the same metal is difficult 

(Tiede, Hassellöv et al. 2009). Finally, sampling methods can introduce artifacts, especially in 

the size distribution of ENMs (Tang, Wu et al. 2009). 

An alternative to single-particle imaging for characterizing nanoparticle concentration 

and size distribution is the general class of ensemble measurement techniques, in which a signal 

produced by a sample population of particles is measured. Several light scattering methods are 

4 
among the ensemble approaches, and they can provide representative size distribution 

characterization at relatively low concentrations (ca. 107

 -108 mL-1). Dynamic light scattering 

(DLS), also called photon correlation spectroscopy, measures the decay of the autocorrelation of 

laser light scattering to calculate the hydrodynamic radius (rh) distribution (Filella, Zhang et al. 

1997). DLS combined with applying an oscillating electric field also can measure ζ potential , 

which is related to the surface charge of an ENM. Static light scattering (SLS), which measures 

the continuous scatter signal at various scattering angles, provides the complementary size 

characteristic of radius of gyration (rg) (Kammer, Baborowski et al. 2005). The ratio rg/rh can 

provide an estimation of ENM shape (Schurtenberger, Newmen et al. 1993). Unfortunately, both 

these ensemble laser light scattering methods are prone to errors in widely polydisperse 

suspensions, because scatter signal is exponentially related to size. Nanoparticle tracking 

analysis (NTA) is a single particle light scattering technique that does not suffer this limitation. 

None of the light scattering techniques are effective in complex media with multiple particle 

types, because they do not measure particle elemental composition. 

Field flow fractionation (FFF) has become popular for physical separation of 

nanoparticles of different sizes. It can be used with an ensemble detector, such as inductively 

coupled plasma mass spectrometry (ICPMS), that can provide elemental concentration for each 

size fraction. Therefore, the FFF-ICPMS combination is applicable in complex systems. FFF is 

actually a general technique with several implementations. All the FFF methods use the 

application of a laminar flow of eluent through a narrow channel combined with an orthogonal 

force field (hence the term field flow). In most implementations of FFF, the orthogonal force 

field results in bigger particles having an average position closer to the channel wall than smaller 

particles, resulting in a lower average velocity for the bigger particles, and a resulting size 

separation at the channel outlet. The two commonly used FFF techniques for ENM 

characterization are flow field (flow-FFF) (Stolpe, Hassellöv et al. 2005; Lesher, Ranville et al. 

2009) or, to a lesser extent, a sedimentation (gravity) field (sed-FFF) (Taylor, Garbarino et al. 

1992; Hassellöv, Lyvén et al. 1999). Flow-FFF separates nanoparticle sizes by hydrodynamic 

radius, while sed-FFF separates by buoyant mass. Flow-FFF combined with ICPMS detection 

has recently been used to study the adsorption of uranium ions on iron oxide nanoparticles 

(Lesher, Ranville et al. 2009). 

Another technique for physically separating nanoparticle size fractions that has recently 

been applied to ENMs is hydrodynamic chromatography (HDC) (Tiede, Hassellöv et al. 2009). 

HDC can be coupled on-line with ICPMS to provide an ensemble characterization method 

(Tiede, Boxall et al. 2010). In HDC, the sample is passed through a column of inert material 

such as silica with a mobile phase. The inter-particle channels in the stationary phase approach 

the size of the nanoparticles, so that nanoparticles with larger hydrodynamic radius experience a 

higher average velocity of the laminar flow of mobile phase than smaller particles. Thus, larger 

particles elute before smaller ones. HDC potentially has less likelihood for artifacts due to 

interactions with the stationary phase than FFF methods, which use semi-permeable polymer 

membranes. On the other hand, FFF provides separations of higher size resolution. 

Capillary electrophoresis (CE) has recently been applied to separation of engineered 

nanomaterials (Liu and Wei 2004; Liu, Lin et al. 2005). CE has the advantage of relatively fast 

5 
analysis compare with FFF and HDC, and very good theoretical resolving power. It separates 

nanoparticles based on electrophoretic mobility (Petersen and Ballou 1992; Huff and McIntire 

1994; Quang, Petersen et al. 1996). Therefore, nanoparticle charge and size both affect retention 

time. This can lead to complications in data interpretation. 

Ensemble methods based on size separation coupled on-line with elemental detection 

(i.e., hyphenated methods) provide elemental information in addition to size distribution. This 

level of specificity makes them effective tools in moderately complex systems like laboratory 

test media. However, they can only give screening-level characterization of real environmental 

samples. They provide the information on total analyte element content associated with different 

size particles. Therefore, they cannot distinguish a large concentration of nanoparticles, each 

containing a small fraction of analyte element, from a lower concentration of the same size 

particles, each with a high analyte element fraction. For example, a large background of natural 

organic matter of 20 nm hydrodynamic radius with adsorbed silver ions cannot be distinguished 

from a low concentration of 20 nm silver nanoparticles. 

The lack of any practical methods for detecting, quantifying, and characterizing metalcontaining ENMs in environmental media has resulted in the adaptation of a single particle 

technique from aerosol analysis and colloid chemistry. This method, as modified in our 

laboratory for aqueous suspensions of metal-containing ENMs, is called single particleinductively coupled plasma mass spectrometry. A related technique was originally developed to 

characterize airborne particulates (Nomizu, Nakashima et al. 1992; Nomizu, Hayashi et al. 

2002). That technique was later adapted for characterizing zirconia colloids (Degueldre, 

Favarger et al. 2004), thorium oxide (Degueldre and Favarger 2004), and gold particles 

(Degueldre, Favarger et al. 2006). Recently, the technique was applied to characterize silver 

nanoparticles in municipal waste water (Monserud, Lesher et al. 2009), and preliminary results 

of the technique coupled with flow-FFF have been presented (Hassellöv 2009). ESD presented a 

study of the performance of SP-ICPMS and how ICPMS acquisition and plasma parameters 

control performance (Heithmar 2009). 

SP-ICPMS relies on the fact that metal-containing nanoparticles entering an ICPMS 

plasma produce intense ion plumes of the metal isotopes in very short time periods (< 1 ms). If 

the ICPMS signal is monitored very fast (≤10 ms data points), any moderate background from 

dissolved analyte metal, or plasma matrix ions (Lam and Horlick 1990), diminishes to an average 

count of less than 1 ion detected per data point. The ion plume pulses from the nanoparticles are 

easily distinguished. The frequency of the pulses is proportional to the nanoparticle 

concentration in the sample, and the number of ions detected in each pulse is proportional to the 

analyte mass in the nanoparticle creating the pulse. 

SP-ICPMS is a single particle rather than an ensemble method, so it analyzes individual 

nanoparticles (Table 3). SP-ICPMS does not measure particle size, but rather analyte mass for 

each particle and the nanoparticle concentration. The hyphenated methods do not give 

information on individual nanoparticles, but rather on each size fraction of nanoparticles as a 

group. One other difference between SP-ICPMS and the hyphenated methods is that the former 

measurement is made on a time scale of several seconds to a minute depending on the 

6 
concentration, while the latter typically require 10 – 30 minutes. It can be seen that SP-ICPMS 

and the hyphenated approaches are complementary, and preliminary results of coupling the two 

will be discussed in this report. 

Theory of SP-ICPMS 

A detailed description of ICPMS processes in the conventional mode as well as in the SP 

mode was given in our previous report (Heithmar 2009). This report gives a conceptual 

description of the techniques with real analytical results to demonstrate the concepts. 

In conventional ICPMS, an aerosol of droplets is produced from the sample usually using some 

form of pneumatic nebulizer (Taylor 2001). The larger aerosol droplets are removed by 

collisions within a spray chamber before a tertiary aerosol of droplets enters the plasma. The 

flux of droplets is typically greater than 106

 s

-1. Each droplet contains a few hundred to a few 

thousand analyte atoms. The efficiency of the sampling process from aqueous sample to aerosol 

flux in the plasma is quantified by the nebulization transport efficiency, εn, which is generally 

between 2% and 30%, depending on the nebulizer and spray chamber used. The aerosol is 

evaporated in the preheating zone of the plasma, within the load coil region. The salt residues 

containing the analye begin to vaporize in this region. The salts are atomized in the initial 

radiation zone (IRZ), which extends from the load coil several mm (Koirtyohann, Jones et al. 

1980). Ionization occurs in the normal analytical zone (NAZ) (Thomas 2004). The 

vaporization, atomization, and ionization processes are usually >80% efficient for most elements 

(O'Connor and Evans 1999). 

In conventional ICPMS, the above processes result in a fairly constant flux of analyte ions 

reaching the detector, due to the large number of droplets and the small number of analyte atoms 

dissolved in each droplet. The resulting ICPMS signal for 30 pg/mL dissolved gold with a short 

measurement window (known as the dwell time, td) of 10 ms is very low and somewhat noisy 

(Figure 2a). Therefore, a longer measurement window is usually used in conventional ICPMS 

(Figure 2b). The signal for the 30 pg/mL dissolved gold is roughly constant at about 80 s-1. 

When the same 30 ng of gold in 50 nm nanoparticles are suspended in each mL of sample, 

very few aerosol droplets contain any gold, and a few contain ca. 4 x 106

 gold atoms (one 

nanoparticle). The resulting signal (Figure 3a) for the conventional long td (2 s in this example) 

is somewhat noisier than for dissolved gold, which is expected by the episodic nature of the gold 

flux. The average signal intensity is the same as for the same concentration of dissolved gold 

(Figure 2b). Each gold nanoparticle produces a plume of millions of ions that enter the mass 

spectrometer interface over a period of less than 500 μs (Gray, Olesik et al. 2009; Heithmar 

2009). In SP-ICPMS, this discrete nature of the ion signal is exploited by using very short dwell 

times. With td = 10 ms, each gold nanoparticle ion plume reaching the detector contains about 

30 ions (the rest of the ions entering the interface are lost in the spectrometer), producing a 

corresponding pulse (Figure 3b). The number of ions detected in each pulse is directly 

proportional to the gold mass in the corresponding single nanoparticle. The frequency of pulses 

is proportional to the concentration of nanoparticles in the sample (in this case, ca. 2 x 104

 mL-1). 

7 
This theory of SP-ICPMS that results in an easily calibrated signal relies on two 

assumptions. First, every nanoparticle that reaches the plasma is detected. This also depends on 

a sufficiently long residence time, so the ion plume expands enough to substantially fill the cross 

section of the central channel of the plasma. If so, equation 1 is valid. 

(1) qp / cp = qs εn, 

where qp = flux of particles detected in plasma (s-1), cp = concentration of nanoparticles 

containing the detected metal in the sample (mL-1), qs = sample uptake rate (mL s-1), and εn = 

nebulization efficiency (dimensionless). Note that qs and εn are properties of the ICPMS 

instrument conditions, and independent of the element. Therefore, equation 1 can be used to 

calculate their product using any type of nanoparticle suspension of known cp. 

The second assumption of the SP-ICPMS theory is that ICPMS sensitivity is constant for 

an analyte, irrespective of whether it is dissolved or contained in nanoparticles. Again, this 

requires that the residence time in the plasma to be sufficiently long. If so, equation 2 is valid. 

(2) ma,p = [qs εn ca / qi,a] ni,p = k ni,p, 

where ma,p = mass of analyte element in a single nanoparticle (g), ni,p = number of ions of analyte 

element detected in the corresponding plume (number of ions detected in a single SP-ICPMS 

pulse – see Figure 3 b), ca = the analyte concentration in a dissolved standard of the analyte (g 

mL-1), qi,a = ion flux measured for the dissolved standard (s-1). For each analyte element, 

calibration of the element mass in individual particles (calculation of the response factor k) 

requires only the qs εn product from Equation 1 and analysis of a known concentration of 

dissolved analyte element using a conventional ICPMS standard). 

Equation 2 provides calibration of nanoparticle element mass. The calibration of 

nanoparticle concentration is provided by rearrangement of equation 1 for any unknown 

nanoparticle suspension, once qs εn has been determined: 

(3) cp = qp / qs εn. 

8 
Experimental 

Most of the materials, instrumentation and methods used in the present study are found in 

our previous report (Heithmar 2009). Only those added or modified are described here. 

Materials 

Sodium dodecyl sulfate (SDS), Triton X-100, and 3-(cyclohexylamino)propanesulfonic 

acid (CAPS) were obtained from Sigma-Aldrich, St. Louis, MO. Calcium chloride was obtained 

from EMD Chemicals, Darmstadt, Germany. Syringe filters (0.45 μm and 5.0 μm nylon) were 

obtained from Sterlitech, Kent, WA. Urban runoff water for SP-ICPMS experiments were 

obtained from Las Vegas Wash, Las Vegas, NV (36° 6.826’ N, 115° 8.741’ W, ca. 2 km east of 

Las Vegas Blvd.). 

Instrumentation 

ICPMS analyses were performed on both the DRCe (PerkinElmer, Waltham, MA) 

described previously and on a 7500ce (Agilent, Santa Clara, CA), with an upgraded 7500cx lens 

system. HDC-SP-ICPMS experiments were conducted with a hydrodynamic chromatography 

column (5-300 nm size range, Polymer Laboratories, Shropshire, UK) with either an Agilent 

1200 HPLC pump or an 100DM syringe pump (ISCO, Lincoln, NE). CE-ICPMS experiments 

were conducted with a MDQ capillary electrophoresis instrument (Beckman-Coulter, Brea, CA). 

Methods 

Because of limitations in software, SP-ICPMS measurements on the 7500ce with dwell 

times less than 10 ms had to be acquired by entering multiple elements, so the total integration 

time was at least 10 ms. 

Silver nanoparticle transformation studies were conducted on either 500 ng/mL (4.5 x 108 

mL-1 particle concentration) or 0.25 ng/mL (2.2 x 105

 mL-1) silver nanoparticle suspensions in 

water. The samples were spiked to final concentration of 200 mM CaCl2. Subsamples were 

taken at designated times after spiking and diluted to 1 x 104

 mL-1 for SP-ICPMS analysis. 

Urban runoff water (10 mL) was collected in polypropylene syringes and filtered on-site 

with 5.0 μm syringe filters. One sample was spiked with gold and silver nanoparticle 

suspensions prior to filtering. Samples were analyzed by SP-ICPMS within 36 hours, usually 

within three hours. 

HDC separations were conducted using 10 mM SDS buffer optimized for the 

nanomaterial. Triton X-100 (0.1%) was added in some experiments. Eluent flow was 1.0 

9 
mL/min. The column eluent was split 60/40 with a tee and a PEEK capillary tube to waste in 

order to match the optimum uptake rate of the nebulizer (400 μL/min). 

The 30 and 50 nm gold nanoparticle CE separation was conducted in 75 μm ID x 80 cm 

fused capillary with 40 mM SDS and 10 mM CAPS, pH 7.3. The SP-ICPMS dwell time was 10 

ms. 

Results and Discussion 

Assessment of SP-ICPMS accuracy 

The validity of the SP-ICPMS theory as described in this report was evaluated by 

applying it to a series of gold nanoparticles (30, 50, 80, and 200 nm nominal diameter). A 

suspension of 30 nm gold at 2 x 104

 mL-1 was analyzed by SP-ICPMS with a 10 ms dwell time to 

calculate qs εn from Equation 1, and a 1.0 ng/mL dissolved gold ICPMS standard was analyzed to 

calculate k in Equation 2. This was then applied to the SP-ICPMS responses (mean ni,p of 

suspensions of the four nanoparticle standards). The resulting ma,p values were used to calculate 

the theoretical diameters of corresponding spherical gold nanoparticles. The linear least square 

fit of the calculated diameters from this procedure against the nominal values (Figure 4) is: 

dcalculated = 1.00 dnominal + 0.73; (r2 = 0.9989). 

The agreement between the SP-ICPMS calculated diameters and the nominal values 

demonstrates that the SP-ICPMS theory is valid for gold nanoparticles up to at least 200 nm. At 

some point, larger particles will not vaporize, atomize and ionize with the same efficiency as 

dissolved analyte, or their ion plume will not be collected with the same efficiency. At that 

point, calculated ma,p values will be negatively biased, so quantitative analysis by SP-ICPMS 

should be evaluated with standards of the largest nanoparticles expected. When nanoparticle 

standards of well characterized size distribution are not available for a given material, SP-ICPMS 

should be considered a semi-quantitative technique for measuring particle elemental mass. 

There are published studies of the behavior of ion populations in the interface region of 

the ICPMS mass spectrometer (Macedone, Gammon et al. 2001; Mills, Macedone et al. 2006; 

Farnsworth, Spencer et al. 2009) and of ion clouds in the plasma produced by single particles 

(Hobbs and Olesik 1993; Hobbs and Olesik 1997; Gray, Olesik et al. 2009). These studies 

present the vaporization and ionization, as well as the transmission of ions through the interface, 

as complex processes influence by many factors. They might be used to hypothesize that the 

assumptions made by the SP-ICPMS theory are suspect. However, the results of our 

investigation demonstrate that the assumptions are valid, at least for gold nanoparticles up to 200 

nm diameter. 

10 
Precision and selectivity of SP-ICPMS 

The precision of SP-ICPMS was assessed by replicate analyses of 50 nm gold 

nanoparticle suspensions (4 x 103

 mL-1; 5.0 pg/mL). No significant differences in the slopes or 

positions of the cumulative distribution functions (CDFs) of the detected ion pulses (Fgure 5) are 

detectable. The relative standard deviation of 2.4% for the mean ion counts reflects good 

precision of the calculated mean particle element mass (Table 4). The relative standard deviation 

of the pulse count (11%) is consistent with Poisson statistics for a mean of 101, the best precision 

attainable for the nanoparticle concentration metric. 

The selectivity of SP-ICPMS in complex matrices was evaluated by spiking the 

suspension analyzed in the last two replicates with 60 nm silver nanoparticles at a concentration 

of 5 x 107

 mL-1. Neither the mean particle gold mass nor the gold nanoparticle concentration 

metric were affected by a greater than 104

 ratio of silver to gold nanoparticles (Table 4). 

Nanoparticle transformation study 

There have been several studies of changing size distributions of metal-containing 

nanoparticles as produced by changes in matrix (Kapoor, Lawless et al. 1994; Peukert, 

Schwarzer et al. 2005; Shim and Gupta 2007; Baalousha 2009). Increased ionic strength is one 

factor that often induces greater degree of aggregation, usually attributed to a collapse of the 

double layer and a decrease of the associated electrostatic repulsion (Domingos, Tufenkji et al. 

2009; Gilbert, Ono et al. 2009). SP-ICPMS is a potentially powerful tool for studying 

nanoparticle transformation processes. Because of the speed of SP-ICPMS analyses, fast 

transformation processes can be followed. The sensitivity of SP-ICPMS could allow the first 

studies of transformations at environmentally relevant concentrations. 

SP-ICPMS was used to analyze suspension of 60 nm silver over several minutes after the 

ionic strength was increased by addition of CaCl2 to a final concentration of 200 mM. The 

original nanoparticle concentration was similar to those used in most transformation studies to 

date (4.5 x 108

 mL-1 or 500 ng/mL). The resulting CDFs at 0 (unspiked), 8, and 14 minutes after 

CaCl2 addition shift progressively to the right (higher ion counts per pulse) with time (Figure 6). 

This reflects the expected aggregation to larger particle clusters. 

A similar experiment was conducted at a concentration of silver nanoparticles that might 

be found in a wastewater effluent (2.2 x 105

 mL-1 or 0.25 ng/mL) (Benn and Westerhoff 2008). 

The transformation was followed over two days, reflecting the slow transformation kinetics 

expected at low concentration. The CDFs at 0, 1, and 49 hours indicate that at least 85% of the 

dwell times containing any ion signal produced only 1 or 2 ion counts (Figure 7). This result 

indicates either dissolved silver or silver nanoparticles of less than about 30 nm diameter. Silver 

nanoparticles in this experiment appear to dissolve at environmentally relevant concentrations. 

11 
The portion of the CDFs with particle mass rank >0.85 shift progressively to the left over time, 

indicating that the dominant process is a continued dissolution, rather than aggregation. The 

small number of large nanoparticles (ion counts >200 per particle) does increase over time, 

indicating slight competing aggregation. This very preliminary study is the first of silver 

nanoparticle size transformations at environmentally relevant concentrations. It is made possible 

by the high particle concentration sensitivity of SP-ICPMS. 

Screening urban runoff using SP-ICPMS 

SP-ICPMS was applied to a small screening-level assessment of metal-containing 

nanoparticles in a surface water consisting of urban runoff (Las Vegas Wash). Water samples 

were to be filtered for two reasons. First, it was hoped that a size cutoff of about 0.45 μm could 

be achieved that would produce data that reflected true nanoparticle content. Second, unfiltered 

water could contain larger particles (>5 μm) that could potentially clog the nebulizer. The effect 

of filtration on nanoparticle recovery was studied. 

A reagent water suspension of 50 nm diameter gold nanoparticles (1 x 104 mL-1) before 

and after filtration through a 0.45 μm nylon filter (Figure 8a and 8b, respectively) was analyzed 

by SP-ICPMS. Although the nominal cut-off of the filter is 10 times the diameter of the 

nanoparticles, the filter retains virtually all the nanoparticles. The CDFs of the unfiltered (Figure 

8c) and filtered (Figure 8d) suspensions show that only a few smaller nanoparticles (< 30% of 

the original mean particle mass) were recovered in the filtrate. The poor filtrate recovery may 

indicate adsorption of the nanoparticles on the membrane material. 

Recovery studies with reagent water suspensions of 50 nm gold and 60 nm silver showed 

that nearly 100% recovery was obtained in the filtrate using 5.0 μm cut-off nylon filters, so Las 

Vegas Wash water samples were filtered in the field with these filters. One sample was spiked 

before filtration with 50 nm gold and 60 nm silver nanoparticle suspensions. Even accounting 

for a 10:1 dilution of silver nanoparticles in the spiked sample, the silver SP-ICPMS analysis of 

the spiking suspension (5 x 104

 mL-1 – Figure 9a) and the 5.0-μm filtered spiked Las Vegas 

Wash water sample (5 x 103

 mL-1 – Figure 9b) demonstrate poor recovery. In addition, the 

CDFs of the spiking suspension (Figure 9c) and the 5.0-μm filtered spiked sample (Figure 9d) 

show a large bias toward small nanoparticles in the filtered sample. The poor recovery in the 

real water samples could be due to either adsorption of nanoparticles on larger suspended solids 

in the spiked sample or a layer of solids on the filter membrane blocking the passage of 

nanoparticles. An investigation of the mechanism of the poor nanoparticle recovery and of 

possible approaches to ameliorate the problem is needed. For the preliminary demonstration 

study reported here, 5.0-μm filtered samples were analyzed to protect the nebulizer, despite the 

poor recovery. No attempt at correcting results for recovery was made. 

Silver–bearing particles are present in the Las Vegas Wash water (Figure 10). The pulse 

heights indicate a Ag mass equivalent to about 50-nm diameter metallic Ag particles. The 

particle concentration is approximately 200 mL-1, not correcting for the low recovery. By 

12 
contrast, the recovered gold-bearing particle concentration may be on the order of 10 mL-1 

(Figure 10). 

Approximately 400 particles per mL of titanium-bearing particles above an equivalent 

TiO2 diameter of about 60 nm are present in Las Vegas Wash water samples (Figure 11). The 

largest particle is equivalent to about 400 nm TiO2. 

The SP-ICPMS analysis of Las Vegas Wash water for the lanthanoid elements and 

yttrium (Figure 12) show the presence of cerium-bearing particles. It is likely that the source is 

natural, given the presence of the other lanthanoids and yttrium which tend to occur with cerium 

in minerals. 

SP-ICPMS analysis of Las Vegas Wash water at m/z 57, which is the isotope commonly 

used for iron determination, was conducted at a 10 ms dwell time (Figure 13, upper trace). The 

continuous signal is largely due to ArOH+

 plasma background. Significant pulses above the 

background are hard to distinguish. SP-ICPMS analysis with a 3 ms dwell time demonstrates the 

improved signal-to-noise in SP-ICPMS as dwell time decreases (Figure 13, lower trace). In SPICPMS, background is proportional to dwell time, while signal is independent of dwell time, as 

long as the latter is much longer than the plume pulse transit time, τp (Heithmar 2009a). The 

background signal in Figure 13 is decreased proportionally to the 3/10 dwell time ratio. The 

background is apparently dominated by flicker noise rather than shot noise, because the 

background fluctuation appears to decrease nearly as much as the mean background, rather than 

the square-root relationship produced by shot noise. Significant pulses above the 3 ms 

background are obvious, indicating iron-bearing particles. 

 

Coupling SP-ICPMS with separation methods 

A preliminary investigation of the coupling of SP-ICPMS with HDC separations was 

conducted. Initial results indicated significant background interference in the SP-ICPMS of 

gold. SP-ICPMS analysis of SDS eluent from the Agilent reciprocating piston pump (no HDC 

column) detected numerous background pulses that precluded analysis of HDC chromatograms 

(Figure 14). It was considered unlikely that the background pulses were due to gold. Tantalum, 

with a single isotope at m/z 181, is known to form oxide polyatomic ions (m/z 197). The 

presence of tantalum-containing particles in the SDS eluent was confirmed by the SP-ICPMS 

analysis at m/z 181 (Figure 14 b). The intensities of the m/z 197 background peaks to the m/z 

181 peaks indicate a roughly 1-2% TaO+

 formation relative to the Ta+. This is consistent with 

expected values. Because the tantalum appeared to come from the HPLC pump, the 

reciprocating piston pump was replaced with a syringe pump. It was hoped that the much slower 

moving parts of the latter would produce fewer tantalum-bearing particles. The resulting m/z 

197 and m/z 181 SP-ICPMS backgrounds revealed only two background peaks greater than 5 

ions appear at m/z 197 over a 12 minute period (Figure 15). This level of background is 

acceptable. Interestingly, the two peaks observed are likely gold, since none of the peaks at m/z 

181 are large enough to have produced a pulse of these magnitudes, assuming 2% TiO+

/Ti+. 

13 
 

 

A suspension of 60 nm gold nanoparticles (50 pg/mL – 2.3 x 104

 mL-1) was analyzed by 

HDC coupled to the ICPMS. The m/z 197 chromatogram with 1 s integration time typical for 

conventional hyphenated analysis includes a poorly defined peak around 760 s, indicating the 

elution of 60 nm gold (Figure 16a). The small peak at about 830 s is near the elution volume of 

dissolved gold. The chromatogram of an identical sample at the SP-ICPMS dwell time of 10 ms 

(Figure 16b) exhibited a group of individual pulses with a mean ion count of about 40-50 at 760 

s, consistent with a SP-ICPMS particle gold mass equivalent to 60 nm gold. Thus, the retention 

time of the HDC peak provides information that the particle diameter is about 60 nm, while the 

SP-ICPMS signal confirms that the individual particles contain a high fraction of gold. This 

complementary information would eliminate the possibility in a real analysis that the signal at a 

60 nm retention is due to nanoparticles with adsorbed gold or minerals with low gold content. 

The HDC-ICPMS chromatogram (conventional 1 s integration windows) of a mixture of 

60 nm gold nanoparticles (50 pg/mL – 2.3 x 104 mL-1) and 30 nm gold nanoparticles (25 pg/mL 

– 9.2 x 104

 mL-1) contains peaks around 770 s (50 nm gold) and 785 s (30 nm gold) (Figure 17a). 

There is no complementary information in the chromatogram to confirm that composition. The 

HDC-SP-ICPMS chromatogram (10 ms dwell time) includes individual pulses around 770 s 

producing about 40-50 ions each, while those around 785 s produce about 6-7 ions (Figure 17b). 

This is in excellent agreement with the ratio predicted by their nominal diameters of about 8:1. 

One issue with HDC-ICPMS in either the conventional mode or the SP mode is the presence of 

occasional spurious single particles (see for example the peaks at about 820, 840, and 850 

seconds). The cause of these peaks is currently unclear. However, we suspect that they result 

from contamination from earlier chromatographic runs using larger Au nanoparticles at much 

higher concentrations (μg/mL). 

A preliminary experiment coupling CE with SP-ICPMS was conducted with a suspension 

of 30 nm gold (4 x 108 mL-1; retention time ca. 13 minutes) and 50 nm gold (9 x 107 mL-1 ; 

retention time ca. 15 minutes). The 30 nm particle concentration in this experiment resulted in a 

substantial fraction of dwell times with multiple nanoparticles, so the pulse ion-count ratio was 

less than the expected 5:1 (Figure 18). Nevertheless, the 30 nm and 50 nm particles appear to be 

nearly completely resolved. 

Conclusions and Future Work 

The experiments described in this report indicate the promise of SP-ICPMS as a viable 

metrology tool for metal-containing ENMs. The theory of the technique is valid for gold 

nanoparticles up to at least 200 nm diameter (83 femtogram mass). Particle element mass and 

particle concentration measurements are precise and unaffected by large excesses of non-analyte 

particles. 

SP-ICPMS allows the study of nanoparticle size transformation at an environmentally 

relevant concentration. The particle concentration sensitivity of other methods have previously 

limited transformation studies to concentrations several orders of magnitude higher. The results 

presented in this report indicate that, while aggregation is significant at high nanoparticle 

14 
concentration (2.2 x 105

 mL-1), dissolution is the dominant transformation process at a more 

environmentally relevant concentration (4.5 x 108 mL-1). 

SP-ICPMS is a convenient stand-alone method for screening urban runoff water for 

particles containing substantial mass of a selected analyte element. The method does not 

respond to high concentrations of particles with low element mass per particle, which complicate 

analyses by hyphenated methods. 

Sample handling artifacts, such as low recoveries of filtered samples, must be addressed 

to allow reliable screening-level assessments of water containing suspended solids. Alternative 

methods for removing large particles will be investigated. These methods include filtering with 

large-area metal sieves and centrifugation. 

The coupling of separation methods such as HDC or CE with SP-ICPMS provides more 

selective analysis than either the separation technique with conventional ICPMS or SP-ICPMS 

alone. Additional coupled techniques will be investigated. Coupling flow-FFF with SP-ICPMS 

is particularly promising, given the high resolution of that method compared to HDC. Other 

emerging techniques such as microfluidic oscillating electric field-FFF may be investigated in 

collaboration with others. 

The main limitation of the SP-ICPMS is its limited nanoparticle concentration dynamic 

range using commercial ICPMS instruments. This limitation may be largely overcome using a 

commercial instrument introduced this year that greatly reduces the dead time between 

individual dwell times, allowing shorter dwell times that increase the upper limit of the dynamic 

range. Implementation of SP-ICPMS on that instrument is planned soon. 

15 
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21 
Tables 

Table 1 Engineered nanomaterials uses in consumer products. 

Nanomaterial Number of products Common uses (with selected examples) 

Silver 121 Nutritional supplements; personal care 

products (tooth brushes, tooth paste); 

biocide coating on membranes (gloves, 

masks, condoms); cothing (socks, 

underwear, baby apparel); toys and 

pacifiers; dispersed biocide (washing 

machines) 

Titanium dioxide 32 Resin coatings; personal care products 

(skin cream, hair dryers); nutritional 

supplements; catalysts (self-cleaning 

coatings) 

Zinc oxide 30 Sun block 

Carbon nanotubes 19 Material construction (sporting goods, 

automobile and aircraft parts) 

Gold 15 Nutritional supplements, personal care 

products (skin cream); catalysts. 

Fullerenes 7 Cosmetics 

Cerium oxide 1 Fuel additive 

22 
Table 2 Common ENM characterization metrics and selected characterization methods. 

Characterization metric Measurement methods 

Size distribution - diameter SEM, TEM, AFM, DLS, SLS, NTA, flowFFF, sed-FFF, CE, HDC 

Size distribution - mass Sed-FFF 

Specific surface area adsorption isotherm 

Surface charge ζ potential by DLS 

Shape SEM, TEM, AFM, SLS with DLS or FFF 

23 
Table 3 Comparison of SP-ICPMS with hyphenated ensemble methods. 

SP-ICPMS Hyphenated ensemble methods 

Measures elemental mass of nanoparticles, not 

nanoparticle size distribution. 

Separate nanoparticles into size fractions 

diameter, and determine total metal 

concentration associated with each particle size 

fraction. 

Provides concentration of metal-based 

nanoparticles, and mass of metal in each 

particle. 

Cannot differentiate large concentration of 

particles containing little analyte from low 

concentration with high analyte content. 

Therefore, no information is provided on 

number or characteristics of metal-based 

particles. 

Fast analysis (e.g., ≤ 60 seconds) Lengthy analysis (e.g., 20 – 30 minutes) 

24 
 

Table 4. Statistics of replicate SP-ICPMS analyses of 50 nm gold nanoparticles 

(4 x 103

 mL-1). 

Replicate Mean number of ions per particle Number of particles 

1 25.6 94 

2 25 105 

3 26.4 87 

41 26.3 101 

51 25.9 115 

Grand mean 25.8 101 

% RSD 2.22 11 

1 Replicates 4 and 5 were analyzed in presence of 60 nm silver particles at 5 x 107

 mL-1. 2

 Corresponds to < 0.8% RSD in mean particle diameter. 

25 
Figure Captions 

Figure 1. Number of environmental health and safety (EHS) research projects initiated since 

1998. 

Figure 2. Time resolved ICPMS signal for dissolved gold (30 pg/mL) at (a) 10 ms dwell time 

and (b) 2 s dwell time. 

Figure 3. Time resolved ICPMS signal for 50 nm gold nanoparticle suspension (30 pg/mL) at 

(a) 2 s dwell time and (b) 10 ms dwell time. 

Figure 4. Correlation of calculated gold nanoparticle diameter from the particle elemental mass 

determined by SP-ICPMS vs. the nominal diameter reported by the manufacturer. 

Figure 5. CDF of five replicate SP-ICPMS analyses of 50 nm gold nanoparticle suspensions at 4 

x 103

 mL-1. Replicates 4 and 5 also contained 60 nm silver nanoparticles at 5 x 103 mL-1. 

Figure 6. Change in CDF of 60 nm silver nanoparticles (500 ng/mL) over time after spiking with 

200 mM CaCl2. 

Figure 7. Change in CDF of 60 nm silver nanoparticles (0.25 ng/mL) over time after spiking 

with 200 mM CaCl2. 

Figure 8. SP-ICPMS of 50 nm gold nanoparticles (1.0 x 104

 mL-1) - (a) time-resolved signal of 

unfiltered suspension, (b) signal of filtrate suspension - 0.45 μm Nylon filter, (c) CDF of 

unfiltered suspension, (b) CDF of filtrate suspension - 0.45 μm Nylon filter. 

Figure 9. SP-ICPMS of 60 nm silver ENMs - (a) time-resolved signal of 5x104 mL-1 spiking 

solution, (b) signal of Las Vegas Wash water spiked to 5x103

 mL-1 and then passed through 5.0 

μm filter, (c) CDF of 5x104

 mL-1 spiking solution, (d) CDF of spiked and filtered Las Vegas 

Wash sample. 

Figure 10. Time resolved SP-ICPMS signal of silver and gold in 5.0-μm filtered Las Vegas 

Wash water. 

Figure 11. Time resolved SP-ICPMS signal of titanium in 5.0-μm filtered Las Vegas Wash 

water. 

Figure 12. Time resolved SP-ICPMS signal of lanthanoid elements and yttrium in 5.0-μm 

filtered Las Vegas Wash water. 

Figure 13. Time resolved SP-ICPMS signal of m/z 57 in 5.0-μm filtered Las Vegas Wash water 

at two dwell times. 

26 
Figure 14. Time resolved SP-ICPMS signal of eluent from liquid chromatograph reciprocating 

pump – (a) m/z 197 and (b) m/z 181. 

Figure 15. Time resolved SP-ICPMS signal of eluent from piston pump – (a) m/z 197 and (b) 

m/z 181. 

Figure 16. HDC-ICPMS chromatograms of 60 nm gold nanoparticles (50 pg/mL) – (a) 1 s dwell 

time and (b) 10 ms dwell time. 

Figure 17. HDC-ICPMS chromatograms of 60 nm gold nanoparticles (50 pg/mL) and 30 nm 

gold nanoparticles (25 pg/mL) mixture – (a) 1 s dwell time and (b) 10 ms dwell time. 

Figure 18. CE-SP-ICPMS electropherogram of 30 nm and 50 nm gold nanoparticles. 

27 
r Number of p ojects 

600 500 400 300 200 100 

0 

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 

Year initiated 
b 

ICPMS (dissolved) ICPMS (dissolved) 2 s dwell time 

time (s) time (s) 

Ions detected 

140120100

80604020

0

0 10 20 30 40 50 60 70

a ICPMS (dissolved) 10 ms dwell time 

Ions detected 

3530252015

10

50

0 5 10 15 20 25 30

time (s) 

0 5 10 15 20 25 30
45

b 40

35 ICPMS (nanoparticles) ICPMS (nanoparticles) 

10 ms dwell time 

Ions detected 

353025201510

50

0 5 10 15 20 25

time (s) time (s) 

30 

250 

a ICPMS (nanoparticles) 2002 s dwell time

0 10 20 30 40 50 60 70

Ions detected 150 100

50

0 

0 10 20 30 40 50 60 70

time (s) 
Diameter from elemental mass (SP-ICPMS; nm) 

250 

200 

150 100 

50 

0 

30.4 

48.6 

84 

20

0 50 100 150 

Nominal diameter (nm) 
particle mass ran k 

1.2 

1 

0.8 0.6 0.4 0.2 

0 

0 10 20 30 40 50 60 70 80 

ions detected ions detected 

Rep. 1 Rep. 2 Rep. 3 Rep. 4 Rep. 5 
0 min. 8 min. 14 minutes 

0.9 

1 

0.5 0.6 0.7 0.8 

mass rank 

01 0.2 0.3 0.4 

particle 

0 

0.1 

0 100 200 300 400 500 600 

ions detected 
Particle mass rank 

1 

0.8 0.6 0.4 0.2 

0 

0 100 200 300 400 500 600 

Ions detected 

0 hr 1 hr 49 hr 
50 

60 70 

ions detected/dwell ions detected/dwell 

(c)

90 (a) 1 0.9 80

0 8. 

403020

particle mass rank particle mass rank 0.7 0.6 0.5 0.4 0.3 0.2 10 0.1

0 0 

0 5 10 15 20 25 30 35 40 45 50 0 1 0 2 0 3 0 4 0 5 0 

ions detected time (s) (d)

90 (b) 80

1

0.9 

7060504030

0 8. 0.7 0.6 0.5 0.4 0.3 

20 0.2 

0.1 

0 

10

0 

0 1 0 2 0 3 0 4 0 5 0 6 

0 5 10 15 20 25 30 35 40 45 50 ions detected 

time (s) 
40 

60 

200 

(a) 1 (c) 

180 0.9 160 0.8 

e0.7 

ions detected/dwell ions detected/dw ll 140 120 100 140 120 100 

particle mass rank particle mass ran

k 

0.6 0.5 0.4 0.3 0.2 

80 60 40 

20 0.1 

0 0 

0 50 100 150 200 250 300 0 50 100 1

time (s) ions detected 

(d) 

(b) 1200 0.9 

180 0.8 160 

0.7 0.6 0.5 0 4. 

0.3 

80 

0.2 0.1 20 

0 

0 50 100 150 200 250 300 0 50 100 1

0 

time (s) time (s) ions detected ions detected

e 
ions detected/dwell 

Ag107 Au197 

90 80 

70 60 50 40 30 20 10 

0 

0 50 100 150 200 250 3

time (s) 
Ions de

tected per dwell 

1600 1400 1200 

1000 

800 600 400 200 

0 

0 50 100 150 200 250 300 350 

time (s) 

400 
Ions detected per dwell 

350 300 250 200 150 100 

50 

0 

0 50 100 

Ce140 

150 

time (s) 

La139 Nd142 

200 

Y89 

250 300 
6000 

4000 5000 

ll 

2000 3000 

ons detected/dwe 

0 

1000 

io 

0 100 200 300 400 

ti ( )mes 

10 ms dwell time 3 ms dwell time 
a 120 

m/z 197 100 

80 

counts 60 

40 20 

0 

0 100 200 300 400 500 600 700 800 

time (s) 

b 25000 m/z 181 20000 

counts 15000 10000 

5000 

0 

0 100 200 300 400 

time (s) 

500 600 700 800 
a 120 

m/z 197 100

80604020

0

0 100 200 300 400 500 600 700 800

time (s)

120 b 

m/z 181 100

80604020

0 

0 100 200 300 400 500 600 700 800

time (s) 

counts counts 
a 400 

350 

1 s 300 

750 770 790 810 830 850 870 890 

time (s) 

counts 250 200 

150 

100 

50 

0 

time (s) 

b 100 

90 

10 ms 80 

70 

counts 60 50 40 

30 20 10 

0 

750 770 790 810 830 850 870 890 

time (s) 
a 1400 

1s 1200 

1000 

counts 800 600 

400 200 

0 

600 650 700 750 800 850 

time (s) 

200 b 180 

10 ms 160 

140 

counts 120 100 

80 60 40 20 

0 

600 650 700 750 800 850 

time (s) 
30 & 50 nm Au @ 10-ms integration time 

140 

60 80 

100 120 

undance 

0 

20 40 

60

abu 

0 5 10 15 20 25 

minutes 

 

 

 

 

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