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Methods for the detection and characterization of silica colloids by microsecond spICP-MS Manuel D. Montano, Brian J Majestic, Asa Jamting, Paul K. Westerhoff, and James F. Ranville Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b04924 • Publication Date (Web): 08 Apr 2016 Downloaded from http://pubs.acs.org on April 10, 2016

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Analytical Chemistry

Methods for the detection and characterization of silica colloids by microsecond spICP-MS Manuel D. Montaño1,*, Brian J. Majestic2, Åsa K. Jämting3, Paul Westerhoff4, James F. Ranville1 1Colorado School of Mines, Department of Chemistry and Geochemistry, Golden, CO 80401, USA 2University of Denver, Department of Chemistry and Biochemistry, Denver, CO 80208, USA 3National Measurement Institute Australia, Nanometrology Section, West Lindfield, NSW 2070, Australia 4Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, AZ 85287, USA ABSTRACT: The rapid development of nanotechnology has led to concerns over their environmental risk. Current analytical techniques are underdeveloped and lack the sensitivity and specificity to characterize these materials in complex environmental and biological matrices. To this end, single particle ICP-MS (spICP-MS) has been developed in the past decade, with the capability to detect and characterize nanomaterials at environmentally relevant concentrations in complex environmental and biological matrices. However, some nanomaterials are composed of elements inherently difficult to quantify by quadrupole ICP-MS due to abundant molecular interferences, such as dinitrogen ions interfering with the detection of silicon. Three approaches aimed at reducing the contribution of these background molecular interferences in the analysis of 28Si are explored in an attempt to detect and characterize silica colloids. Helium collision cell gases and reactive ammonia gas are investigated for their conventional use in reducing the signal generated from the dinitrogen interference and background silicon ions leaching from glass components of the instrumentation. A new approach brought on by the advent of microsecond dwell times in single particle ICP-MS allows for the detection and characterization of silica colloids without the need for these cell gases; as at shorter dwell times the proportion of signal attributed to a nanoparticle event is greater relative to the constant dinitrogen signal. It is demonstrated that the accurate detection and characterization of these materials will be reliant on achieving a balance between reducing the contribution of the background interference, whilst still registering the maximum amount of signal generated by the particle event.

INTRODUCTION Nanotechnology has been at the center of a technological revolution in the past 20 years.1 New and novel properties afforded by their small size and increased surface area have resulted in development of nano-enabled properties with scientific and economic importance. Commercial nanomaterials have been used in a wide array of consumer products ranging from foods,2 bactericides,3 drug delivery vectors,4 biomarkers,5 and materials for the purpose of ground water remediation.6-8 Many nanomaterials have also found use in industrial processes such as catalysis,9 oil and gas production,10,11 and the fabrication of electronics.12 These numerous applications have led many to expect the global market value for nanotechnology to exceed $2 trillion by the year 2020.13 One example application, with a multi-billion dollar annual market, is the semi-conductor industry which uses nanoparticles (NPs) in liquid suspension.14 Chemicalmechanical polishing (CMP) slurries used in the planarization of electronics incorporate nanoparticles into their formulation because their mechanical properties enable them to act as effective abrasive materials. Silica (SiO2) nanoparticles have become a major component of CMP slurries in addition to ceria (CeO2) and alumina (Al2O3). The goal of planarization is achieved through the removal of defects on the surface by the abrasive action of the slurry. Aside from the semi-conductor industry, many other industries use SiO2 nanoparticles as

polishing agents. As a result of these many application, SiO2 nanoparticles represent the largest market sector of nanomaterials.15 This large market means that the potential for the release of these materials into the environment and the subsequent potential for environmental impact are very high.14,16 To assess the risk of these nanomaterials to the environment, knowledge of their hazard (ecotoxicity) and exposure (environmental concentration) are required. Current research is aimed at determining both the toxic effects of these materials to various organisms, as well as developing new analytical methodologies to detect, characterize, and quantify these materials in complex biological and environmental matrices.17 Size, size distribution, and concentration are among the most important properties to be measured in environmental samples as they provide information about the environmental exposure and expected reactivity of these nanomaterials. There are several techniques that provide information about one or both of these properties to varying degrees of accuracy and precision.18,19 The efficacy of many of these techniques is impeded by interferences from various environmental constituents (c.f. background/naturally occurring NPs) and artifacts brought on by the analyses of these complex matrices.20 To this end, single particle inductively coupled plasma-mass spectrometry (spICP-MS) has been instrumental in the analysis of nanomaterials in biological and environmental

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matrices.21,22 By utilizing short data acquisition times (dwell times), a short-duration pulse of intensity, generated from the atomization and ionization of a nanoparticle, can be detected above the ambient background arising from instrumental noise and dissolved ions in solution.23,24 From this pulse of intensity, particle mass and subsequently a diameter (assuming a spherical geometry, density, and elemental composition) via a calibration curve of dissolved standards, can be derived. Using ICP-MS, an element-specific technique, many of the issues of other ensemble particle analysis techniques can be avoided (e.g. dynamic light scattering (DLS), particle tracking analysis (PTA)), while the specificity and selectivity of single particle techniques (e.g. transmission and scanning electron microscopy (TEM and SEM respectively) are maintained.25 The spICP-MS technique has been used to detect and characterize a wide array of nanomaterials ranging from metallic gold and silver nanoparticles,26-28 zinc and cerium metal oxides,29 and carbonaceous materials such as carbon nanotubes30 in both biological and environmental matrices.31-34 However, not all elements are amenable to detection of small nanoparticle sizes due to the presence of multiple isotopes, interferences, and/or instrument sensitivity.35 Silica colloids are a major component of CMP slurries in addition to ceria and alumina. Recent efforts in this field have been aimed at reducing the size of the polishing agents to nanometer-scale dimensions. Efficient use, recycling and disposal of CMP-containing solutions is facilitated by accurate analytical methods capable of monitoring these materials at low release concentrations. However, the analysis of silica particles by quadrupole spICP-MS is confronted by several challenges. The somewhat low mass resolution (~1 a.m.u.) of the quadrupole, and the prevalence of background silicon and dinitrogen ions possessing the same nominal molecular mass as Si+ (m/z ≈ 28 a.m.u.), present a major interference to the detection of silica nanoparticles. Previously, this hindrance had been overcome for dissolved silicon analysis in conventional solution ICP-MS, by utilizing either a collision gas (helium)36,37 or a reaction gas (ammonia)38 prior to mass selection by the quadrupole. A helium collision cell operates on the principle that the inert helium will more frequently collide with the more voluminous dinitrogen ions compared to silicon ions, thereby reducing their kinetic energy and preventing their passage through a kinetic energy barrier at the end of the collision cell.39 However, incidental collisions with the silicon ions of interest inherently limits the sensitivity of this technique. Ammonia reaction gas is selective toward reacting with dinitrogen, resulting in the conversion of the dinitrogen into a new molecule of a different mass-to-charge ratio (N2H+ = 29 a.m.u.), which is subsequently filtered by the quadrupole.40 The greater selectivity of these reactions results in improved detection limits; however, side reactions with the target analyte ion may limit the sensitivity of this technique. Advances in the electronics and software of ICP-MS have resulted in the analysis of particles at microsecond dwell times; whereas previous spICP-MS analyses were limited to dwell times on the order of milliseconds.41 As a particle event occurs on the order of 500 µs or less,42 the ability to analyze these materials at data acquisitions times of 100 µs or less greatly improves the resolution between particle events, affording a greater dynamic range in particle number concentrations.42 In addition, these short acquisition times

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reduce the proportion of the constant signal generated by dissolved ions and molecular interferences over the acquisition time relative to the signal generated from short nanoparticle events. As a result, greater signal-to-background is achieved between a particle event and dissolved background, allowing for analysis of these materials amidst higher background concentrations of dissolved ions.41 The research presented here demonstrates a comparison of spICP-MS using conventional approaches (collision gas, reaction gas) to remove interferences, to an approach using microsecond dwell time data acquisition with no collision or reaction gas. Except for the short acquisition time approach, the operating conditions used for these methods are meant to be representative of typical ICP-MS analysis. Though the analytical advantages of microsecond dwell times are demonstrated, current detector limitations that hinder that ability to size larger particles as a result of pulse pile-up are also presented. EXPERIMENTAL SECTION Reagents Calibration curves were created by analyzing a set of dissolved gold and silicon standards. Dissolved gold standards were prepared by diluting a 100 mg L-1 stock solution of gold chloride (Spex CertiPrep, Spectroscopy Standard) in 2 % optima hydrochloric acid (Fisher Scientific) to concentrations ranging from 0 to 200 µg L-1 depending on the dwell time employed. Silicon calibration standards were made by dilution of a stock solution of 10 mg L-1 dissolved silicon (High Purity Standards, Spectroscopy Standard) with ultrapure deionized water, 18.2 mΩ cm. Gold nanoparticles used in the determination of the instrument transport efficiency were NIST RM 8013 (nominal 60 nm particles) with a measured diameter of 56 nm as determined by TEM according to the Report of Investigation (NIST). Silica particles of various sizes (100, 200, 300, 500 and 1200 nm) were purchased from Nanocomposix, Inc., and their sizes independently validated through a variety of measurements (SEM (SI figure 1, FEI Nova SEM/FIB), DLS (SI Table 2, Malvern Zetasizer Nano ZS) in conjunction with the manufacturer reported TEM values (JEOL 1010). All dilutions of stock solutions of nanomaterials were made using 18.2 mΩ cm ultrapure deionized water (Barnstead International Nanopure Diamond™). Helium gas employed on the Perkin Elmer NexION 300D was purchased from Airgas, Inc. with research grade purity (99.999% purity). Anhydrous ammonia gas used in this research was purchased from Matheson Tri-gas, Inc. (99.999% purity). Instrumentation All work was performed using a Perkin Elmer, NexION 300D quadrupole ICP-MS with a Type-C Miramist nebulizer and a baffled cyclonic spray chamber. This work utilized different ICP-MS configurations to accomplish reduction in the dinitrogen molecular interference. Experiments using either a collision gas (helium) or reaction gas (ammonia) were performed at 3 ms dwell times. Microsecond dwell time analysis was performed with and without ammonia reaction gas at 25, 50, and 100 µs dwell times using the Perkin Elmer

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Syngistix software package. The Syngistix™ software developed for Perkin Elmer ICP-MS instruments allowed for the implementation of microsecond dwell times through the elimination of the quadrupole settling time for single element readings. Typical instrumental conditions are tabulated in ESI (SI Table 1). Gas flow rate optimization Prior to analysis using helium or ammonia gas, the flow rate for these gases was optimized. These optimizations were performed to ensure maximum sensitivity for the silicon ion signal, while achieving the greatest reduction in the signal from the dinitrogen interference. The ratio of dissolved silicon standards to the count intensity of the blank was compared at different helium flow rates to achieve an optimum value where the ratio was greatest (SI figure 3). A software operated optimization procedure was used to achieve the optimum flow rate for helium and ammonia gas flow rates. Nebulizer transport efficiency When using dissolved standards for mass determination in single particles, an important component of spICP-MS analysis is the determination of the instrument nebulizer transport efficiency.24 When a solution of dissolved ions is aspirated into the spray chamber, only a small percentage of sample reaches the plasma and is analyzed, with the majority of the sample going to waste. However, when a nanoparticle is aspirated and reaches the plasma, the total mass of that nanomaterial is ablated and is analyzed. To account for this discrepancy, the signal intensity of a dissolved calibration curve is compared to the signal generated from a nanoparticle of known diameter, giving a transport efficiency. For this study, the transport efficiency was between 8-10 %. NIST RM8013 particles were utilized as gold nanoparticles of known diameter. Data collection and processing. Recent implementation of microsecond spICP-MS has also required the development of software capable of not only capturing the transient nanoparticle signal, but also the ability to integrate the peak area which is related to the mass equivalent of the nanomaterial. Additionally, the detector dead time between measurements must be sufficiently low (30 ns) in order to fully realize the nanoparticle signal. To this end, Syngistix™ software developed by Perkin Elmer, Inc. allows the operator to choose the dwell time, density, and elemental mass fraction of the material being analyzed. When the nanoparticle sample is analyzed, an iterative algorithm is used to both find and integrate peaks. In contrast to conventional single particle ICP-MS, where peak detection only requires determining a sufficient background cut-off (frequently ̅ + 3σ of the background signal); there are multiple criteria required for peak detection and subsequent integration at microsecond dwell times. The first step requires determining an appropriate threshold by which to identify peaks. An iterative algorithm is used by which the average background signal is added to three times its standard deviation as an initial threshold. Peaks above this threshold are

removed and the process repeated until convergence. The maximum of each peak is taken, and a smoothing algorithm based of the average number of data points that comprise a full nanoparticle peak. This average background intensity is then subtracted and the overall peak integrated to give the peak intensity. These integrated peak intensities are then applied to a mass flux calibration curve generated by a set of dissolved calibration curves and a nebulization efficiency determined by a standard particle (e.g. 60 nm NIST Au NP RM8013). This mass flux curve is used to convert the intensity to mass, and mass subsequently to size.24 All graphs and Gaussian fits were created using OriginPro software student version 9.1. Silica particle density A key consideration in sizing particles by spICP-MS is particle density. As ICP-MS reports mass, knowledge of the density allows for the conversion of mass to diameter assuming a spherical geometry. Silicon dioxide nanoparticles have been shown to have a size-dependent density, and changes to the structure of the particle (e.g. porosity, lattice conformation) can have a significant impact on the density of the particles.43-45 In order to size particles by spICP-MS, the density of the silica particles was determined by comparing results from differential centrifugal sedimentation (DCS) to the TEM based values provided by the manufacturer. In DCS, particles are injected into a rotating disc and separated across a density gradient media (typically sucrose), whereby particles are separated according to their size, shape (in this case spherical), and density relative to the media. Using the TEM based particle diameter, the DCS results were adjusted for particle density to generate a comparable particle diameter. DCS measurements were shown to be reproducible and confirmed the size-dependent density of these silica particles (SI Figure 1). The densities recovered from DCS (Table 1) were used throughout this work in converting the mass of the silica nanoparticles to a spherical diameter. Experimental set-up In these experiments, various sizes of silica particles were analyzed to their size distribution and the efficacy of using different ICP-MS configuration (cell gas, microsecond dwell times) to accurately determine particle size. The particle number concentration for each particle size was chosen to reduce the chance of coincidence. The size distributions were measured in triplicate, with a blank in between samples to ensure no carry over between samples. A new set of calibration standards was run every ten samples to account for any potential instrument drift. RESULTS AND DISCUSSION Millisecond spICP-MS with collision/reaction gases Conventionally, silicon has been measured by ICP-MS using a collision or reaction gas in order to reduce or eliminate the dinitrogen interference that prevents the accurate determination of silicon in a sample. Selecting an appropriate flow rate for the helium gas requires a flow rate high enough to significantly reduce the molecular interference signal

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(background) while preserving the intensity of the silicon signal. To determine this flow rate, a ratio of signal intensities from dissolved signal standards to the signal generated from the method blank was determined, and the flow rate that gave the signal-to-background ratio was determined as the optimal flow rate (SI figure 3). Optimal silicon signal-to-background ratio was achieved on a Perkin Elmer 300D using a 3.0 mL min-1 He gas flow rate. A 3 ms dwell time was chosen to reduce the chance of particle coincidence seen at 10 ms dwell times.41 Figure 1A shows the silicon calibration curve using the helium collision cell, where the size detection limit of this technique is given as the average background

Fig. 2. Measured particle size distributions of the silica particles by sp ICP-MS employing A) ammonia reaction cell and B) helium collision cell. Dashed horizontal lines represent the size detection limits (blue for ammonia, red for helium). The bimodal nature of the 500 nm SiO2 particles was also seen in DCS (Figure S1).

Figure 1. Calibration curves for helium and ammonia reaction cell spICP-MS. A) Calibration curve for helium collision cell (He flow rate = 3.5 ml min-1). Horizontal line indicates size detection limit. B) Calibration curve for ammonia reaction (NH3 flow rate = 0.4 ml min-1) cell. Horizontal line indicates size detection limit

intensity added to three times the standard deviation. This approach yielded an approximately 300 nm size detection limit using these conditions. Using this calibration curve, other silica particles can be sized according to spICP-MS theory.23,24,26,28 Figure 1B shows an improvement upon silica nanoparticle characterization by using a reactive ammonia gas (flow rate of 0.4 ml min-1). The slightly more selective reactive process resulted in a lower size detection limit of 200 nm, an approximate six-fold decrease in the mass of silica particle able to be detected (Figure S4). The size distribution calculated from the analysis of silica nanoparticles (nominal 100, 200, 300, 500, and 1200 nm) at these conditions are shown in figure 2. The intensities of these particles were converted to a mass assuming a silicon mass fraction of 47 % for silica (SiO2) and the densities determined by DCS were used to size the particles according to spICP-MS theory (Table 1). Figure 2A shows the size distributions for the different particles analyzed using helium collision gas. As the size detection limit is set at approximately 300 nm, it can be seen that only the larger sizes, and the upper portion of the 300 nm particle size distribution, are detected. This results in an average diameter that is larger than the expected average diameter. Figure 2B demonstrates that the use of a reactive ammonia gas resulted in the complete capture of the full distribution of the nominal 300 nm SiO2 colloids. However, as before, this method would only capture the larger particles sizes centered around and above the size detection limit of this technique (200 nm for ammonia). Particle sizes measured by spICP-MS are summarized in Table 1, where they are compared to other particle sizing techniques. The average diameter of the size distribution with the corresponding standard deviation of the distribution is reported. If the TEM particle size is assumed to be the ‘true’ particle size, it can be seen that spICP-MS using collision or reaction gases compares well to the TEM values (within one standard deviation). The slightly larger sizes for the 300 nm

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Table 1. Average particle diameter and standard deviation of the particle size distribution (i.e. peak width) as determined by

helium/ammonia reaction cell spICP-MS (Measurements performed in triplicate, **Below the size detection limit by these techniques) Average particle diameter (nm)

Nominal particle size (nm)

DCS density (g cm-3)

TEM*

SEM

DLS

He cell spICP-MS

NH3 cell spICPMS

100

1.96

102 ± 9

92 ± 8

117 ± 1

**

**

200

1.89

203 ± 10

183 ± 14

220 ± 2

**

227 ± 43

300

1.75

305 ± 24

289 ± 24

320 ± 5

346 ± 41

317 ± 60

500

1.76

517 ± 50

532 ± 62

519 ± 7

514 ± 75

528 ± 71

1200

1.70

1173 ± 67

1083 ± 102

986 ± 64

1033 ± 91

996 ± 78

* Manufacturer specifications particle (in helium mode) and the 200 nm particle (using ammonia) can be attributed to the proximity of the size detection limit for the two techniques. One of the primary concerns when analyzing a large particle is ensuring that the particle is completely ionized in the plasma. In order to determine if the particles were fully ablated in the plasma, the signal intensities were plotted against the volume of the particles determined by TEM. If complete ionization occurs, it would result in a linear relationship between counts (ions) and particle volume. The linearity of these plots shown in the supporting information (SI figures 5) demonstrate that complete ionization was achieved requiring no further correction (e.g. ionization efficiency). This complete ablation is further corroborated by previous research which demonstrates that the ablation of SiO2 particles is achieved up to 1 µm.46 Though this technique demonstrates utility in sizing silica particle >200 nm, there is considerable interest in the manufacture of smaller nanomaterials as a result of their increased surface area and reactivity as size decreases. For the purposes of assessing the risk of these materials in the environment, it will be necessary to develop techniques with the requisite sensitivity to detect and characterize these materials. The use of collision and reaction gases will inherently limit some of the detection of the target analyte ions. However, recent advances in the electronics of ICP-MS allows for the detection of 28Si ions without the need for collision or reaction gases through the use of microsecond dwell times. Microsecond spICP-MS A new approach to reducing the background signal from molecular interferences without collision or reaction gasses is possible with the advent of microsecond dwell times. Previous single particle ICP-MS has used millisecond dwell times, which results in single points of intensity above the background signal. By shortening dwell times to microseconds, the particle intensity is divided into different dwells, which when summed together, amount to the total intensity of the particle. By utilizing this method, the background interference signal would similarly be divided and reduced proportionally to the dwell time used. This method

Fig. 3. Raw intensities generated from the analysis of a 300 nm SiO2 particle using three different dwell times. Peak areas are integrated from contiguous intensities above the particle detection threshold (background average + 3σ)

was employed in the analysis of differently sized silica particles (100, 200, 300, and 500 nm SiO2) at dwell times 100 µs and below. In this way, the proportion of signal generated by a particle could be resolved from the background signal generated by the molecular interferences. This is illustrated in figure 3, where the raw intensities generated from the analysis of a 300 nm silica particle are shown. As the dwell time is reduced, the average signal of the background decreases proportionally. Similarly, the number of dwell times the particle intensity is divided into increases with decreasing dwell time. However, when the separate intensities from the particle event are integrated, it results in a similar total intensity regardless of dwell time. A comparison between the dissolved calibration curves was made in figure 4 to ensure that there was no discernable difference in intensity with decreasing dwell time. In order to compare the intensities, the average intensities were normalized to counts-per-second. As figure 4 demonstrates, all intensities are within one standard deviation of another, demonstrating that no signal is lost as the dwell time is decreased.

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Table 2. Average particle diameter and standard deviations (n=3)of the size distribution of silica particle determined by spICP-MS at microsecond dwell times without a cell gas (**Counts exceed pulse threshold and cannot be sized) Nominal size (nm)

Manufactured TEM size (nm)

25µs spICP-MS (nm)

50µs spICP-MS (nm)

100µs spICP-MS (nm)

100

102 ± 9

141 ± 30

131 ± 31

142 ± 37

200

203 ± 10

197 ± 20

179 ± 16

184 ± 36

300

305 ± 24

306 ± 18

272 ± 14

273 ± 35

500

517 ± 50

462 ± 32

**

**

Fige 4. Silicon calibration curves at different dwell times. (Inset: Silicon concentrations 0-20 ppb)

Using this method, silica particles ranging from 100-500 nm were analyzed. At microsecond dwell times data is collected only in pulse counting mode as the time required for the pulseto-analog conversion would result in losses in the transient signal. Subsequently, pulses that register above the pulse threshold that would normally be analyzed in analog mode are instead omitted in this analysis as a result of pulse pileup (an excess of ions reaching the detector at rates that overwhelm the detector response. For the larger particles (500 and 1200 nm SiO2), this resulted in significant losses in total particle intensity, and are thus not presented in these results. Raw data can be found in supporting information (SI Figure 7). Figure 5 shows the distribution of intensity for both the background and three silica particles (100, 200, and 300 nm) at 25, 50, and 100 µs dwell times. As figure 5A demonstrates, the reduction in dwell time results in a significantly reduced background intensity. However, figures 5B-5D show that with decreasing dwell time the integrated particle intensities for the different particles sizes remain the same. The particle intensities at 50 µs appear larger than those at 25 µs and 100 µs, but this is likely due to day-to-day variability in ICP operating conditions, and is accounted for in the dissolved calibration curve obtained that day. This is further corroborated both by the increased signal intensity shown in figure 4 (the dissolved calibration curve), where 50µs dwell times showed an increased average intensity. Gold nanoparticles analyzed for transport efficiency also demonstrated increased counts at 50µs dwell times as shown in SI figure 9. Furthermore, this larger particle intensity runs

counter to the decreasing background intensity shown in figure 5A for 50 µs. Particle intensities were converted to sizes as summarized in Table 2 and shown in figure 6. As before, the accuracy of these sizes was compared to the average and standard deviation of the TEM determined sizes. This method shows an improvement over the previous techniques of using collision or reaction gases at millisecond dwell times, as the user is capable of fully resolving the 200 nm size distribution and is capable of sizing even smaller particles as evidenced in figure 5B. Between replicates there was good agreement between samples as evidenced in SI figure 10. Differences between different dwell times is likely the result of differences in ion transmission in day-to-day operations of the instrument; as well as, variability in the ionization of the silica particles. Previous reports have shown that the method of analyzing silicon in normal mode (without collision gases, monitoring 29 Si) resulted in a size detection limit of approximately 500 nm.35 By employing microsecond dwell times instead, the signal-to-background ratio can be improved to the point where the most abundant isotope of silicon can be monitored without the use of a collision cell ICP-MS. It should be noted that the diameter of the nominal 100 nm particle sizes were larger than the size reported by the manufacturer and established by other sizing techniques. It was determined that the particles captured in figure 5B were either coincidence peaks or particles in the very upper range of the size distribution, resulting in the larger reported size. This is further explained in Table 3 which reports the particle number concentrations with regards to the nominal particle concentration analyzed. The particle number recoveries show a less than 3 % particle number recovery for the 100 nm particle sizes, demonstrating that only the largest sizes were captured. The other particle sizes analyzed demonstrated good recoveries, though the 300 nm SiO2 recoveries were lower than expected. This could be the result of a lower than expected stock concentration or losses during sample preparation. For this sample, there is good agreement in particle number concentration across the different dwell times analyzed. If a reaction cell is available, improvements upon the signalto-background can be made at microsecond dwell times by employing a reactive ammonia gas. As opposed to the inert nature of a helium collision gas, the reactive ammonia gas selectively adds a proton to the interference molecules while minimally reacting with the silicon ion analyte. Figure 7 shows the improvement in the signal-to-background ratio when an ammonia gas is employed (0.2 ml min-1 flow rate) for

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microsecond dwell times. The background signal with and without the reactive gas were 7.6 ± 4.4 counts and 29.3 ± 6.9 Table 3. Particle number recoveries for the analysis of silica nanoparticles Measured particle number conc. (106 particle L-1), % recovery Nominal size (nm)

Nominal Concentration (µg L-1)

Expected particle number concentration (106 particles L-1)

100

1ppb

926.3

16.7 (± 1.7),1.8%

22.7 (± 1.9), 2.5%

20.7 (± 1.9), 2.3%

200

2ppb

243.3

223.0 (± 7.5), 91.5%

258.0 (± 12.3), 106.1%

279.0 (± 59.7), 114.6%

300

5ppb

191.6

110.0 (±5.1), 57.3%

139.0 (±2.0), 72.5%

121.0 (±6.3), 63.2%

500

10ppb

78.3

72.9 (±3.8), 93.0%

95.6 (±13.0), 122.0%

81.4 (±3.7), 103.8%

25µs dwell time

50µs dwell time

100µs dwell time

Fig. 5. Intensity distributions of background and particle intensities at different dwell times. A) Background intensity distributions at different dwell times. B) 100 nm SiO2 integrated particle intensities at different dwell times. C) 200 nm SiO2 integrated particle intensities at different dwell times D) 300 nm SiO2 integrated particle intensities at different dwell times. Integrated particle intensities have been background subtracted concentration analyzed. The particle number recoveries show a less than 3 % particle number recovery for the 100 nm particle sizes, demonstrating that only the largest sizes were captured. The other particle sizes analyzed demonstrated good recoveries, though the 300 nm SiO2 recoveries were lower than expected. This could be the result of a lower than expected stock concentration or losses during sample preparation. For this sample, there is good agreement in particle number concentration across the different dwell times analyzed.

If a reaction cell is available, improvements upon the signalto-background can be made at microsecond dwell times by employing a reactive ammonia gas. As opposed to the inert nature of a helium collision gas, the reactive ammonia gas selectively adds a proton to the interference molecules while minimally reacting with the silicon ion analyte. Figure 7 shows the improvement in the signal-to-background ratio when an ammonia gas is employed (0.2 ml min-1 flow rate) for microsecond dwell times. The background signal with and without the reactive gas were 7.6 ± 4.4 counts and 29.3 ± 6.9

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Fig. 6. Diameter of silica nanoparticles at microsecond dwell times with collision or reaction gas. Sizes of TEM measurements (n=100) are shown for comparison. A) 100 nm SiO2 nanoparticle sizing by spICP-MS. B) 200 nm SiO2 nanoparticle sizing by spICP-MS. C) 300 nm SiO2 nanoparticle sizing by spICP-MS.

Fig. 7. Improvement in the signal-to-noise ratio using a reactive ammonia gas at 100 µs dwell times counts respectively (an approximate 76.7 % decrease in intensity. Though the background decreases significantly more than the particle intensities, the ammonia still reacts with silicon, resulting in a decrease of the particle signal ranging from 50-70 %, limiting the improvement in the size detection limit. CONCLUSION The frequent use of these materials, and the increasing development of nanotechnology as a whole will inevitably result in the release of nanomaterials into the environment. As such it will be important to determine not only the concentration of the particle in the environment, but their size distribution, as many nanomaterial properties are sizedependent. The challenge of characterizing silica nanomaterials using spICP-MS requires new methods to reduce or eliminate interfering signal from molecular ions. Conventional approaches using a helium collision gas or a reactive ammonia gas are sufficient for larger silica colloids, but are limited by either inherent indiscriminate collisions or side reactions that reduce silicon ion sensitivity. By utilizing microsecond dwell times, the proportion of signal generated from a silica particle event outweighs the constant signal generated from the dinitrogen interference. The separation between particle and

background signal increases with decreasing dwell time, but if the dwell time is too low, the edges of the particle peak are lost to the background resulting in only partial capture of the signal intensity. As such, the accurate characterization of silica particles will require a balance between reducing the dinitrogen signal while preserving the particle peak integrity; a feat that may be achieved by improving the ion transport efficiency during analysis. The entry of silica nanoparticles into the environment is inevitable. By using spICP-MS, the exposure concentration and physicochemical state of these nanomaterials can be determined, however more sophisticated analytical techniques will be required to differentiate between naturally occurring and engineered silica colloids. Future work in utilizing microsecond acquisition times with short dwell time to allow dual element detection may in part assist in this effort.25 This information will be crucial in assessing the environmental risk of these nanomaterials, and promote the responsible development of these materials as nanotechnology continues to expand. ACKNOWLEDGEMENTS This body of work was supported by the Semiconductor Research Corporation (CSM Task Order 425.040) The authors would like to thank Yu Yang and Xianyu Bi (Arizona State University) for their assistance in acquiring SEM images of the silica particles.

ASSOCIATED CONTENT Supporting Information Supporting information contains additional characterization information. Scanning electron microscopy images of each nanoparticle are shown. Data from differential centrifugation sedimentation (DCS) is presented. Typical instrument parameters as well as optimization for the collision and reaction gases are shown. Additional microsecond data showing large particle analysis at microseconds, silica particle size distributions, and the effect of dwell time on particle detection is provided. The Supporting Information is available free of charge on the ACS Publications website.

AUTHOR INFORMATION

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Corresponding Author * Manuel David Montaño ([email protected]).

Present Addresses †Duke University, Department of Civil and Environmental Engineering, Durham, NC, 27708

Author Contributions The manuscript was written through contributions of all authors. / All authors have given approval to the final version of the manuscript

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Figure 6. Diameter of silica nanoparticles at microsecond dwell times with collision or reaction gas. Sizes of TEM measurements (n=100) are shown for comparison. A) 100nm SiO2 nanoparticle sizing by spICP-MS. B) 200nm SiO2 nanoparticle sizing by spICP-MS. C) 300nm SiO2 nanoparticle sizing by spICP-MS 167x42mm (150 x 150 DPI)


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254x190mm (150 x 150 DPI)


Methods for the Detection and Characterization of ... - ACS Publications

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