Improvement of the Limits of Detection for P, S, and Ca Nanoparticle Size in the Absence of Dissolved Analyte Using a Mixed-Gas Plasma in Single-Particle Inductively Coupled Plasma–Mass Spectrometry

Addition of nitrogen (N₂) to the plasma (outer) flow increases the power density of the plasma, which reduces matrix effects and may aid in the atomization and ionization of nanoparticles (NPs). For the first time, this work reports the limits of detection of NP diameter (LoD_size) for 37 elements in the absence of dissolved analyte using single particle inductively coupled plasma–mass spectrometry (spICP–MS) with argon (Ar) plasma as well as Ar-N₂ and Ar-N₂-H₂ mixed-gas plasmas. For each plasma, the selected operating conditions provided a compromise in sensitivity for multielemental analysis. Despite this compromise, the Ar plasma provides significantly lower LoD_size than previously reported using a different instrument brand. The Ar plasma also provides significantly lower LoD_size than the mixed-gas plasmas, except for ³³S, ³¹P, and ⁴⁴Ca, where oxygen scavenging by N₂ within the Ar-N₂ plasma significantly improved LoD_size. With the Ar-N₂-H₂ plasma, an accompanying loss of sensitivity largely negated the LoD_size improvement from oxygen scavenging. Further improvement of LoD_size for S, P, and Ca can be expected from optimizing the Ar-N₂ mixed-gas plasma for the individual determination of each element.

Engineered nanomaterials have diverse uses in textiles, medicine, fuel production, and storage (1). The potential applications of nanoparticles (NPs) depend heavily on properties such as size, shape, and elemental composition; their preparation is thus aimed at minimizing the variability in these characteristics (2). The release of these materials into the environment has raised concerns about their potential toxicity (3,4). Attempts at characterization and risk assessment of nanomaterials has brought to the forefront a need for reliable quantitative methods for their characterization, especially in complex environmental matrices (3). The extremely low concentration of NPs in realistic environmental conditions, effects of complex environmental matrices on sensitivity, and interfering species in the environment samples make in situ measurement difficult (5).

Single particle inductively coupled plasma–mass spectrometry (spICP-MS) is a useful analytical tool for the characterization of NPs in aqueous suspension, due to its extremely high sensitivity and isotopic specificity (6). These features enable spICP-MS to surmount the challenges of in situ NP characterization (namely, low analyte concentration in complex matrices), which preclude other techniques, such as transmission electron microscopy, nanoparticle tracking analysis, and X-ray diffraction (7). At a sufficiently low NP concentration, each droplet of the polydisperse sample aerosol resulting from nebulization will contain one NP at the most, which can be individually atomized and ionized in the plasma. If measurements are made sufficiently frequently so as to observe single droplets in isolation, the resulting ion flux at the detector is proportional to the mass of each single NP. Sample dilution, dwell time, and other experimental parameters must be optimized to ensure these conditions are met. Assuming a spherical shape and density equal to that of the bulk material allows for the conversion of NP mass to diameter (8–12). Assuming no dissolved analyte in solution with the NP suspension, the limit of detection (LoD) for mass flux can be converted to the LoD for NP mass (LoD_mass) or diameter (LoD_size) (13).

So far, an Ar plasma has been used for all spICP-MS work. Yet, the addition of N₂ to the Ar plasma gas flow creates a more robust ionization source, which suppresses or enhances analyte sensitivity compared to that with an Ar plasma, depending on the analyte (14). The concurrent use of hydrogen (H₂) sheath gas may slightly increase sensitivity versus that with an Ar plasma (14), which would be advantageous for spICP-MS. Hence, for the first time, this study aims to determine LoD_size and LoD_mass in the absence of dissolved analyte for NPs of each of 37 elements by spICP-MS using Ar-N₂ and Ar-N₂-H₂ mixed-gas plasmas, and compare them to those obtained with a conventional Ar plasma. Results from this study provide an updated data set for NP LoD_size in the absence of dissolved analyte using a more sensitive spICP-MS instrument with Ar and mixed-gas plasmas, and a comparison to the data set published previously by Lee and associates (13).

Experimental

Chemicals

Five aqueous standard solutions containing 37 elements (Ag, Al, Co, Cr, Cu, Eu, Fe, In, Ni, Sr, and Zn; Mo, P, S, Se, Ti, and Zr; As, Be, Bi, Ca, Cd, Ce, Ga, Ge, Li, Mg, Pb, Sb, Sc, Th, Tl, V, and Y; Pd and Pt; and Au) were prepared from 1000−10000 mg/L monoelemental standard solutions (SCP Science) in doubly deionized water (DDW; 18 MΩ cm resistivity, Arium Pro UV|DI water purification system, Sartorius Stedim Biotech). The elemental combinations were selected to reduce spectroscopic and isobaric interferences. A suspension of monodisperse 49.9 ± 2.2 nm PEG carboxyl-coated Au NPs in 2 mM citrate (4.2 x 10¹⁰ particles/mL; 99.99% Au purity; nanoComposix) was used to determine transport efficiency and for method optimization. A suspension of monodisperse 45 ± 5 nm Pt NPs in 2 mM citrate (4.8 x 10¹⁰ particles/mL; 99.99% Pt purity; nanoComposix) was used for method optimization.

Working stocks of the multielemental aqueous standard solutions at a concentration of 1 mg/L were diluted to 0.5, 1, 3, 5, and 10 µg/L in DDW in polypropylene vials on the day of analysis. The Au NPs and Pt NPs stock solutions were diluted serially in DDW to 10500 NPs/mL to be used in the determination of transport efficiency and for method optimization. The Au NPs and Pt NPs working solutions were prepared in DDW in polypropylene vials on the day of analysis.

Instrumentation

A Varian 820MS (Varian Inc., now produced by Analytik Jena AG) quadrupole-based ICP-MS instrument was used for this research, including Ni sampler and skimmer cones (0.9 and 0.4 mm orifices, respectively). The collision reaction interface was not employed. Aqueous sample introduction was achieved using a T2100 nebulizer (Burgener Research, Inc.) fitted to a Scott double-pass spray chamber (SCP Science) maintained at 0 °C via a computer-controlled Peltier cooling system. Ultrahigh purity N₂ (99.999%) was introduced to the plasma gas flow through an additional gas inlet. Ultrahigh purity H₂ (99.999%) was introduced into the central gas channel through the sheathing device located between the spray chamber and torch injector.

Data acquisition was carried out in steady-state analysis mode with 10 replicates of 25 scans each. Torch alignment was optimized daily for maximum sensitivity and minimum oxide and doubly-charged ion levels using a tuning solution containing 5 µg/L Ba, Be, Ce, Co, In, P, Mg, and Th in 1% v/v HNO₃ (SCP Science).

The parameters for the Ar and mixed-gas plasmas were optimized for maximum sensitivity and robustness with 10500 particles/mL Au NPs and Pt NPs solutions. Robustness was quantified as the relative NPs concentration and accuracy in observed NP diameter measured in NP solutions with and without the addition of 0.1 M Na. Operating parameters are summarized in Table I. For each element, 1−4 isotopes were monitored.

Measurement of Sample Uptake Rate and Transport Efficiency

The change in mass of a vial containing DDW following 1 min of continuous DDW aspiration, measured in triplicate on the day of analysis, yielded the sample uptake rate. Transport efficiency was determined as the ratio of observed Au NPs concentration (expressed as number of NPs/mL) to the expected concentration over a 1-min time-resolved analysis period. Transport efficiency was corrected for a 27-ms settling time (varying over the course of data acquisition) to obtain the corrected transport efficiency, η_n´, using:

             [1]

where η_n´ is the uncorrected transport efficiency, t_dt is the dwell time (5 ms), t_at is the total acquisition time, and N_dp is the number of data points during the analysis time (15). Transport efficiency measurements were taken in triplicate with three Au NPs working solutions. The observed number concentration was calculated as the number of Au NPs distinct from the background (based on three times the standard deviation of the background) divided by the volume of sample analyzed.

Data Processing

For each monitored isotope, the signal intensity was plotted against elemental mass flux for each standard solution to construct a calibration curve. The LoD obtained using this aqueous standard calibration curve corresponds to the smallest NP mass detectable with spICP-MS (LoD_mass), in the absence of the element in solution (7–13). With the assumption that the NP is perfectly spherical and its density is the same as the bulk elemental density, a LoD for NP diameter (LoD_size) is calculated as:

       [2]

where σ_Bgd is the standard deviation of the signal from the DDW blank solution, m_cal is the slope of the aqueous standard calibration curve, ρ is the bulk elemental density, and f_M is the elemental mass fraction in the NP (in this work, monoelemental NPs were assumed, so f_M = 1).

Results and Discussion

Comparison of Ar and Mixed-Gas Plasmas

Figure 1 shows the LoD_size for spICP-MS with Ar and mixed-gas plasmas. Overall, the Ar plasma provides significantly better LoD_size than both the Ar-N₂ and Ar-N₂-H₂ plasmas (p < 0.001 by paired Student’s t test for each). However, the LoD_size with the Ar-N₂ plasma is lower than with the Ar-N₂-H₂ plasma (p < 0.001 by paired Student’s t test).

Figure 1: Calculated LoDsize in the absence of dissolved analyte for NPs of each of various elements by spICP-MS with Ar and mixed-gas Ar-N2 and Ar-N2-H2 plasmas, compared with those determined by Lee et al. (13).

The largest difference in LoD_size between the Ar plasma and the mixed-gas plasmas occurs for heavier metallic elements such as Pt and Pd. For example, when monitoring ¹⁹⁵Pt⁺, LoD_size increases from 5.6 nm with Ar to 20 nm with Ar-N₂ and 21 nm with Ar-N₂-H₂. With ¹⁰⁶Pd⁺, LoD_size increases from 7.7 nm with Ar to 28 nm with Ar-N₂ and 86 nm with Ar-N₂-H₂.

In fact, LoD_size is smaller with an Ar plasma than with the Ar-N₂ plasma for 63 of 67 isotopes and the Ar-N₂-H₂ plasma for 64 of 67 isotopes. However, for ^54,56Fe, ³¹P, ³³S and ⁴⁴Ca, LoD_size is better with the mixed-gas Ar-N₂ plasma (Table II), due to a reduction in background signal and noise (Table III), as well as in interfering oxide formation. Previous studies have shown that the addition of N₂ to the Ar plasma flow reduces the formation of analyte oxide and interfering oxide species, such as ArO⁺ and ArOH⁺ (16). Decomposition of oxides may be promoted in the higher-temperature mixed-gas plasma (17). The added N₂ can also scavenge oxygen from the plasma to form nitrogen oxide species such as NO⁺. Unfortunately, the concomitant loss of sensitivity that resulted with the Ar-N₂-H₂ plasma largely negated this beneficial effect. Thus, the remainder of this discussion will focus on the Ar-N₂ plasma.

No LoD_size could be calculated for ⁵⁶Fe⁺ in an Ar plasma because the ⁴⁰Ar¹⁶O⁺ background signal systematically caused detector saturation, including with the blank. Because of the oxygen-scavenging effect of N₂ in the mixed-gas plasmas, little ⁴⁰Ar¹⁶O⁺ was detected, leading to an improved LoD_size. Nonetheless, the lowest LoD_size for Fe was obtained with ⁵⁷Fe⁺ in an Ar plasma.

With the Ar-N₂ plasma, the background intensity on ³¹P⁺ (from ¹³C¹⁸O⁺ and NO⁺), ³³S⁺ (from O₂⁺), and ⁴⁴Ca⁺ (from ¹²C¹⁶O₂⁺ as a result of dissolved CO₂, as well as contamination of Ar and entrained air into the plasma) decreased by over an order of magnitude while sensitivity remained similar to that in an Ar plasma (Table III), resulting in improved LoD_size for P, S and Ca. The oxygen-scavenging effect of N₂ also reduced the formation of sulfur oxide, phosphorus oxide, and calcium oxide species, thereby increasing the pool of detectable ³¹P⁺, ³³S⁺ and ⁴⁴Ca⁺ . Because of the important ¹⁶O₂⁺ interference on ³²S⁺, the latter was not monitored. Given that these improvements in LoD_size with the Ar-N₂ plasma were achieved under compromise operating conditions for multielemental analysis, even lower LoD_size could be reached if operating conditions were optimized for each element. Indeed, depending on the element, better LoD_size was observed under different mixed-gas plasma conditions. For example, the LoD_size for ¹⁹⁵Pt⁺ using an Ar-N₂-H₂ mixed-gas plasma with 10 mL/min H₂ (instead of 20 mL/min in Table I) was 11 nm (instead of 21 nm under the conditions for multielemental analysis).
 

Comparison of LoD_size Using Ar ICP with Literature

Figure 1 also includes the LoD_size for the isotopes reported by Lee and associates (13), which are significantly higher than those obtained in this work with the Ar plasma (p = 0.028 by paired Student’s t test). In fact, lower LoD_size was obtained for 29 of the 33 isotopes in common with Lee and associates. The greatest improvements in LoD_size were observed for metallic elements, with little improvement or an increase in LoD_size for metalloids and nonmetals.

LoD_size was only degraded for four isotopes compared to that calculated by Lee and associates (Table IV). In the case of ⁶⁴Zn⁺ and ⁶⁶Zn⁺, the background signal was on the order of 105 counts/s whereas most monitored isotopes had blank signals one to four orders of magnitude lower. This issue was resolved when monitoring ⁶⁸Zn⁺, whose lesser background signal resulted in LoDsize of 48 nm. Both ²⁷Al⁺ and ²⁴Mg⁺ had considerable background noise, with signal-to-noise ratios of 48% and 25%, respectively.

The greatest improvements were observed for Ti, Se, and Ca (Table IV). Altogether, LoD_size decreased by 32% on average from the values reported by Lee and associates (13). Other improvements of note are those involving the noble metals and nickel. These improvements are doubly interesting due to the smaller dwell time used in the current work (5 ms instead of 10 ms by Lee and associates), which would be expected to decrease sensitivity and increase LoD (18). The benefit of using a 5-ms dwell time for spICP-MS instead of 10 ms, which decreases the proportion of coincident particles while minimizing the decrease in sensitivity, was reported previously (19).

Conclusions

This study represents the first application of Ar-N₂ and Ar-N₂-H₂ mixed-gas plasmas to spICP-MS, revealing that, even under conditions providing a compromise sensitivity for multielemental analysis, the Ar-N₂ mixed-gas plasma improves the LoD_size for NPs containing S, P, and Ca due to the scavenging of oxygen by nitrogen. For other elements investigated, however, an Ar plasma provides the lowest LoD_size. In fact, despite the dwell time being halved (5 ms instead of 10 ms), the LoD_size reported in this work, under conditions providing a compromise sensitivity for multielemental analysis with an Ar plasma, are significantly improved for most of the elements compared to previously reported values. The multielement aqueous standard provides a model system suitable for the investigation of matrix-induced suppression and method robustness for the determination of NP LoD_size for a range of elements.

As the limits of detection in this study were calculated in the absence of dissolved analyte, future work will use NPs in liquid matrices with and without known dissolved metal concentrations to determine their effect on real LoD_size. The use of bimetallic NPs may also provide information on the effect of concomitant and potentially interfering species on LoD_size. The mixed-gas plasma should also be optimized for each element, to see the lowest LoD_size that can be achieved under robust conditions.

Acknowledgments

This research was conducted as part of the Engineered Nickel Catalysts for Electrochemical Clean Energy project administered from Queen’s University and supported by Grant No. RGPNM 477963-2015 under the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Frontiers Program. Joshua Kofsky thanks NSERC for an Undergraduate Student Research Award, Andrew Williams for his assistance in conducting the experiments, and Ram Lamsal for his guidance and expertise.

References

1. A. Nel, T. Xia, L. Mädler, and N. Li, Science 311, 622–627 (2006).
2. A. López-Serrano, R.M. Olivas, J.S. Landaluze, and C. Cámara, Anal. Meth. 6, 38–56 (2014).
3. M.R. Wiesner, G.V. Lowry, P. Alvarez, D. Dionysiou, and P. Biswas, Environ. Sci. Technol. 40, 4336–4345 (2006).
4. S.F. Hansen, A. Maynard, A. Baun, and J.A. Tickner, Nat. Nanotech. 3, 444–447 (2008).
5. M. Hassellöv, J.W. Readman, J.F. Ranville, and K. Tiede, Ecotoxicol. 17, 344–361 (2008).
6. F. Laborda, E. Bolea, and J. Jiménez-Lamana, Anal. Chem. 86, 2270–2278 (2014).
7. H.E. Pace, N.J. Rogers, C. Jarolimek, V.A. Coleman, E.P. Gray, C.P. Higgins, and J.F. Ranville, Environ. Sci. Technol. 46, 12272–12280 (2012).
8. C. Degueldre, P.Y. Favarger, Coll. and Surf. A 217, 137–142 (2003).
9. C. Degueldre and P.Y. Favarger, Talanta 62, 1051–1054 (2004).
10. C. Degueldre, P.Y. Favarger, and S. Wold, Anal. Chim. Acta 555, 263–268 (2006).
11. C. Degueldre, P.Y. Favarger, and C. Bitea, Anal. Chim. Acta 518, 137–142 (2004).
12. C. Degueldre, P.Y. Favarger, R. Rossé, and S. Wold, Talanta 68, 623–628 (2006).
13. S. Lee, X. Bi, R.B. Reed, J.F. Ranville, P. Herckes, and P. Westerhoff, Environ. Sci. Technol. 48, 10291–10300 (2014).
14. Y. Makonnen and D. Beauchemin, Spectrochim. Acta B 99, 87–93 (2014).
15. R.P. Lamsal, G. Jerkiewicz, and D. Beauchemin, Microchem. J. 137, 485–489 (2018).
16. J.W.H. Lam and G. Horlick, Spectrochim. Acta B 45, 1313–1325 (1990).
17. I. Ishii, D.W. Golightly, and A. Montaser, J. Anal. At. Spectrom.3, 965–968 (1988).
18. J. Tuoriniemi, G. Cornelis, and M. Hasselöv, J. Anal. At. Spectrom. 29, 743–752 (2014).
19. I. Abad-Álvaro, E. Peña-Vázquez, E. Bolea, P. Bermejo-Barrera, J. Castillo, and F. Laborda, Anal. Bioanal. Chem. 408, 5089–5097 (2016).

Joshua Kofsky and Diane Beauchemin are with the Department of Chemistry at Queen’s University in Kingston, Ontario, Canada. Direct correspondence to: diane.beauchemin@queensu.ca