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Single particle inductively coupled plasma mass spectrometry (spICP-MS)
CHAPTER 3.1.1
Single particle inductively coupled plasma mass spectrometry (spICP-MS) Heidi Goenaga-Infante, Dorota Bartczak LGC Limited, Teddington, United Kingdom
Introduction Single particle inductively coupled plasma mass spectrometry (spICP-MS) has proven to be a powerful technique for providing the number concentration of inorganic nanomaterials (NM) with minimal or no sample preparation [1]. Therefore, there is increasing interest in using this technique for routine, high-throughput applications. The approach, which was originally introduced by Degueldre et al. (2006) [2], leverages on the traditional ICP-MS measurement principle (Fig. 1) and relies on the introduction of highly diluted suspension of particles into the instrument, with the aim that only one particle at a time enters the plasma. The plasma atomizes and ionizes the constituents of the particle, which are then detected using the mass spectrometer. The characteristics of particles that can be measured include particle number concentration, mass concentration, and the mass of individual particle, which for objects of known geometries can be converted to particle diameter, also providing information about the number-based size distribution of particles in the sample. SpICP-MS, unlike other particle counting techniques, is also capable of detecting the dissolved element fraction within the same single measurement provided that a fair resolution between the NP fraction and the dissolved fraction is achieved. This is described more in details in the following section. The technique is compatible with aqueous suspensions of most types of metal and metal(loid) oxide particles and, to some extent, with other types of particles, which are capped or stained with ligands containing lanthanides, sulphur, halides, or phosphorus, since these are elements visible to ICP-MS with current instrumentation. However, the accessible lower particle size limit of detection will vary depending on the particle composition and the type of the element monitored (see Fig. 2). Recent developments in the ICP instrumentation, such as microsecond dwell times and/or quasi-simultaneous multiisotopic TOF detectors [4], allow multielement and/or multiisotope detection in spICP-MS mode, which is of particular interest in the characterization of complex, for example, core/shell, materials. In particular, the microsecond dwell detection capability has enabled selective detection of NPs that easily dissolve (e.g. Ag NPs) from the dissolved fraction (Ag ions) in the presence of high concentrations of the latter. Characterization of Nanoparticles https://doi.org/10.1016/B978-0-12-814182-3.00003-1
© 2020 Elsevier Inc. All rights reserved.
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Fig. 1 Principles of ICP-MS. A fine aerosol of a sample is introduced into a hot (6000–10,000 K) argon plasma of sufficient energy to dry the aerosol and form analyte atoms, which are simultaneously ionized. Ions are then sorted and quantified based on their mass-to-charge (m/z) ratio in the mass analyser [3].
Fig. 2 Illustration of the lower particle size limits of detection achievable with currently available ICP-MS instrumentation.
In spICP-MS analysis, to establish a relationship between the number of particle events detected over a defined analysis window (time scan) and the number of NPs in solution, the NP transport efficiency (TE) must be determined. The most popular methods used in the literature for calculation of the TE are the particle frequency and the particle size methods [5]. However, both of these approaches rely on the use of particle reference materials (RM), which are very scarce, and the few existing ones are neither like-for-like with NM used in real sample matrices nor certified for the number concentration. Alternatively, efforts have been made to significantly increase the amount of sample entering the plasma by means of total consumption introduction systems [6] with the purpose of avoiding the use of reference materials for TE estimation. Using such devices, Miyashita et al. achieved a TE of approximately 93%. Despite this relatively high TE value, the accurate determination of the correlation between the number of NPs in a volume or mass solution and that detected by spICP-MS analysis is still a requirement. Another method that has been used for TE estimation purposes is the waste collection method, which is based on the measurement of the sample uptake rate and the amount of sample going to waste. This method has been largely dismissed by the community, due to the reporting of biased results [5]. Nonetheless, it has the advantage of not relying on NP RMs to determine the TE. More recently, LGC has developed methodology for the determination of TE that does not rely on using RMs but only depends on continuous real-time mass measurements of the NP sample
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
uptake and the sample mass flow (nebulized sample) in a dynamic system [7]. The applicability of this method has been demonstrated so far for highly stable, nearly spherical, and narrowly dispersed colloidal suspensions. More details about this dynamic mass flow approach are given in ‘Transport efficiency, ‘η’’ section. A key advantage of spICP-MS, which makes the technique attractive to industry, is that samples are diluted to make sure that not more than one particle reaches the plasma ionization chamber within the response time of the instrument. The reduced occurrence of matrix effects as a result of the dilution means that the need for sample manipulation can be in many cases avoided. However, for some complex matrices such as creams, biological tissues, blood, and pastes, various extraction techniques (e.g. enzymatic or chemical) are required and have been investigated demonstrating that selective extraction of the NP from the sample can be achieved without affecting their stability [8,9]. This chapter will address the principles, advantages, and remaining challenges of spICP-MS for number concentration and size determination of inorganic NPs. It will also discuss the main parameters that mainly contribute to the accuracy and uncertainty of the number concentration data with particular emphasis on the determination of the NP TE and the existing approaches for its determination.
spICP-MS principle SpICP-MS works by acquiring individual element intensity readings with very short dwell times, in the range of few hundred microseconds to few milliseconds per data point (Fig. 3). Using such short dwell times in conjunction with highly dilute suspensions of the particles allows the detection of individual objects, hence the name ‘single particle’ ICP-MS.
Fig. 3 Principles of spICP-MS. The number of detected pulses (events) in the time scan corresponds to the particle concentration, whilst their intensity to the particle mass, in turn, can be converted to particle size. Dissolved element, potentially in the form of ions, appears as constant background signal.
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Appropriate sample dilution is critical to avoid violation of the ‘single particle rule’, when more than one object arrives at the detector in a given dwell time. For this reason, the dynamic range of spICP-MS for particle concentration measurements is limited to two orders of magnitude. As an example, with a dwell time of 100 μs, a suspension of 60-nm gold particles should be diluted to approximately 104 particles/g, if using standard sample introduction systems. Performing gravimetric rather than volumetric dilutions is generally recommended, to minimize errors arising from multiple dilution steps. The overall number of peaks detected per measurement, which typically is a 60-s time scan, is directly proportional to the number of NPs in the sample, whilst the intensity of the individual peaks corresponds to the particle mass and therefore spherical equivalent particle diameter to the third power, assuming a homogeneous distribution of the element in the particle. The sum of masses of all particles divided by the measured volume of sample gives the mass concentration of particles in the dilute sample. The concentration of the dissolved fraction of the same element can in turn be determined from a constant signal, which differs from the spikes (pulses) representing particulate matter. The ICP-MS instrument configuration required for analysis in the single particle (sp) mode is no different from the standard set-up, meaning that the instrument’s performance should be optimized for maximal signal-to-noise ratio at the particular m/z of interest. In case of isotopes, which suffer from polyatomic interferences, signal-to-noise ratio can be improved by using a collision reaction cell or sector field-based instrumentation. Recent developments in ICP instrumentation featuring dwell time as short as 0.05 ms opened up the potential for multiisotope and multielement analysis in the sp mode. Most instrument manufacturers nowadays also offer designated software for spICP-MS analysis allowing easy processing of the acquired data. Typically, spICP-MS analyses are performed using time-resolved analysis (TRA) with dwell times in the range of 0.05–10 ms. However, it is important to note that the probability of detecting a single particle pulse split between two adjacent measurement windows increases as the dwell time is decreased from 10 to 1 ms. For longer dwell times on the other hand, distinguishing particles from the background becomes more difficult; also, the probability of registering more than one particle per dwell time increases. Dwell times below 1 ms result in the particle signal being spread over multiple data points, which significantly improves the resolution of the technique but adds complexity to data processing. An advantage of using such short dwell times for the selective detection of particle events is illustrated in Fig. 4.
Particle number concentration measurements with spICP-MS The number concentration of particles in the sample, C (NP/g) can be calculated from the following equation: C¼
N Df ηV
(1)
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
Fig. 4 Selective detection of 40-nm Ag nanoparticles from dissolved Ag using spICP-MS with 0.1-ms dwell time.
where ‘N’ is the average number of particles detected per 60-s time scan (NP/min), ‘Df’ is the sample dilution factor (g/g), ‘η’ is the average transport efficiency, and ‘V’ is the average sample uptake rate (g/min). The error associated with the measurement, ΔC, can be calculated from Eq. (2): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ffi ΔDf 2 ΔN 2 Δη ΔV 2 ΔC ¼ C + + + (2) N η V Df where Δx indicates errors associated with the individual terms in Eq. (1). To determine the sample dilution factor, gravimetric rather than volumetric dilutions are recommended. The overall sample dilution factor is a simple multiplication of all the individual dilution steps, calculated from Eq. (3): msn Df ¼ (3) mas where msn is the total mass of the prepared, diluted suspension, whilst mas is the mass of the undiluted analyte sample. When preparing the sample dilution, special attention should be paid to the choice of diluent, to avoid artefacts arising from particle agglomeration/ aggregation arising from compromised colloidal stability or sample deagglomeration arising from reduction in the concentration of any cross-linking reagents. For example, dilute aqueous suspensions of electrostatically stabilized monodispersed colloids can be diluted with aqueous solution containing a small amount (1–10 mM concentration) of the stabilizing agent present on the surface of the particles to avoid particle destabilization.
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It is recommended to investigate the effect of the diluent used on the particle agglomeration/aggregation state on a case-by-case basis. In spICP-MS analysis, the sample is typically introduced using a peristaltic pump. For this reason, it is necessary to determine the sample uptake rate (meaning the amount of sample acquired per minute of the analysis) to be able to relate the number of particle events seen in a time scan to the amount of NPs in suspension. Typically, the sample uptake rate (defined as a mass flow) is measured 2–3 times per day at the beginning, in the middle, and at the end of the measurement session. This can either be done as a single point measurement, when a diluent or particle suspension is acquired over a defined time from a tube containing a known amount of this diluent or suspension (measured by weight rather than volume) or by measuring the loss of weight from the diluent or suspension continuously over at least 15 min [7]. Since the sample dilution and sample uptake can be determined by weight and the acquisition time can be measured accurately, the remaining parameters that include the transport efficiency and number of particles detected are considered the main contributing factors to the uncertainty associated with the number concentration measurement. Therefore, these are discussed more in details in the next sections.
Transport efficiency, ‘η’ Typical sample introduction systems used in the ICP instrumentation do not deliver the entire volume of the acquired sample into the plasma. The proportion of the suspension that actually goes in (usually in the range of 5%–10%) is called the transport efficiency (TE). In recent years, efforts have been made to significantly increase the amount of sample entering the plasma by means of total consumption introduction systems. Using such devices, Miyashita et al. achieved a TE of approximately 93%. Despite such promising results, compared with more traditional sample introduction systems, to correlate the number of particles in a mass of suspension and that detected by spICP-MS, accurate determination of the TE parameter is still a requirement. In fact, TE is often considered the most critical parameter in spICP-MS, as inaccuracy in its determination leads to large biases and uncertainties in the number concentration data. The most popular methods used so far for calculation of the TE are the particle frequency and the particle size methods [10, 11] as discussed in ‘Introduction’ section. For the particle frequency method, a nano-RM is introduced into the ICP-MS, and the number of detected particles is measured in, for example, 60-s long time scan. Based on such measurements and for RMs with known particle size and element mass fraction (present in the particulate form), the TE can be calculated from Eq. (4): η¼
N 4 d3 ρ π 109 100% Cm 3 8 V
(4)
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
where ‘N’ is the average number of particles detected per 60-s time scan (NP/min); ‘d’ is the mean spherical volume-equivalent particle diameter, typically measured by transmission electron microscopy ‘TEM’ (nm); ‘ρ’ is the particle density (g/cm3); ‘Cm’ is the mass concentration of particle suspension (pg/g); and ‘V’ is the average sample uptake rate (g/min). Often, the particle density is assumed to be similar to bulk material, which has been shown to be in relatively close agreement for particles composed of gold, but it is known to significantly differ for many other types of particles. For example, silicon dioxide may have densities ranging from below 1.9 g cm3 in the hydrated amorphous form of St€ ober silica 3 to above 2.6 g cm for quartz. Because all parameters of Eq. (4) come with their associated measurements errors, the overall uncertainty of TE estimated following this approach is relatively large and is the main contributing factor to the overall uncertainty associated with the particle number-based concentration measurements by spICP-MS. Only recently, a quality control material (LGCQC5050: 30-nm colloidal gold NPs) with an assessed value for number concentration has been made commercially available. For this material, the TE can be calculated from Eq. (5): η¼
N 100% Cp V
(5)
where ‘N’ is the average number of particles detected per 60-s time scan (NP/min), ‘Cp’ is the particle number-based concentration (NP/g), and ‘V’ is the average sample uptake rate (g/min). In this case, the uncertainty associated with TE would depend mostly on the errors related to the ‘Cp’ parameter given for the RM. In case of spICP-MS analysis using a nano-RM for which only the particle size (as spherical diameter equivalent) is known, the TE can be calculated following the particle size method. Here, a series of dilution of an ionic standard containing the same element of interest are measured as well as the nano-RM suspension. The TE is calculated from Eq. (6): η¼
Rionic 100% RNP
(6)
where ‘Rionic’ is the instrument’s response to ions (cps/μg) whilst ‘RNP’ is the instrument’s response to the particle suspension (cps/μg). The ‘Rionic’ and ‘RNP’ can in turn be calculated from Eqs. (7), (8), respectively: Rionic ¼
RF ion 6 107 V td
(7)
where ‘RFion’ is the instrument’s response factor to ionic standard, derived from regression analysis of the calibration curve [cps/(μg/kg)]; ‘td’ is the dwell time used (ms); and ‘V’ is the sample uptake rate (g/min): RNP ¼
INP Idiss mNP
(8)
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where ‘INP’ is the average particle intensity (cps), ‘Idiss’ is the average intensity of the dissolved background, and ‘mNP’ is the mass of element in a single particle (μg). The ‘mNP’ can be calculated from Eq. (9): mNP ¼
d3 ρ π 6 1015
(9)
where ‘d’ is the average particle diameter measured by TEM (nm) and ‘ρ’ is the particle density (g/cm3). Similarly to the particle frequency method, the particle size method is also based on several assumptions, including particle sphericity. Particle TEM size and particle density also feed into the equation, meaning that the uncertainty associated with TE estimated following the both approaches is similar. However, there are several literature reports highlighting differences in the TE values obtained with the two approaches, which have been attributed to particle losses in the containers and tubing and other effects such as possible off axis trajectories of particles in the plasma. LGC has recently developed and validated a methodology for TE determination, which does not need a RM, and it is directly traceable to the SI unit of a kilogramme. This approach, called ‘dynamic mass flow’ method, relies on the continuous real-time mass measurements of the NP sample uptake and the sample mass flow (nebulized sample) in a dynamic system [7]. Using this method, the sample waste is continuously returned to the sample vessel (i.e. no separate waste collection), and the difference in weight between the sample mass uptake and the waste mass is regularly recorded by continuous weighing in a balance during the experiment over a time period whilst the system is in equilibrium. Hence, continuous measurements of such differences with multiple data points over time are acquired. This greatly improves precision and reduces bias by obviating the need to flush the system when using the conventional waste collection method. Using the dynamic mass flow approach, the TE can be calculated from the ratio of slopes generated by monitoring the mass uptake of sample continuously over time and the mass of nebulized sample continuously over time in a dynamic system. The suitability of the dynamic mass flow method has already been demonstrated for gold and silver particles in aqueous and biological matrices following alkali extraction, but the method’s performance in applications concerning wider range of materials (such as metal oxides) and matrices (such as cosmetics or food) is yet to be investigated. The main disadvantage of this methodology is that it is more time consuming in comparison with conventional approaches that rely on RMs, so its use in routine analysis of multiple samples, as required in toxicology studies, is very limited. However, this reference method is rather useful for the characterization of other NP quality control (QC) materials for their number concentration so that they can be used in routine high-throughput applications using the frequency method for TE determination. Of course, this relies on finding QC materials that have similar properties to those of NPs present in real samples to achieve accurate results.
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
Number of detected particles in time scan, ‘N’ To optimize the number of particles detected in time scan (particle counting), several parameters have to be taken into account, including the instrument dwell time, threshold set-up between the background signal and the particle events, and the sample dilution factor. This is because the uncertainty associated with particle counting is related to systematic and random errors arising from these parameters [12]. Using quadrupole ICP-MS, the frequency of data acquisition is directly controlled by the dwell time. The effect of dwell times ranging from milliseconds to microseconds on the quality of the number concentration data has been studied earlier; lower precision from counting statistics and bias (from multiple particle events recorded as single events) has been obtained with microsecond dwell times [12]. Therefore, the use of dwell times in the microsecond range has become common practice in the past few years with the advances in ICP-MS instrumentation [12–14]. Also, with dwell times 100 μs, wider linear ranges have been obtained due to lower occurrence of multiple events counted as a single event. Finally, a reduced impact of background/dissolved fraction contribution on the selectivity of NP detection was achieved when using microsecond dwell times in comparison with those in the millisecond range. Of course, despite of the advantages of microsecond dwell times discussed earlier, one should be aware that the wrong selection of the particle concentration in solution may still lead to bias in the number concentration results. Fig. 5 shows that the use of 3-ms dwell time (Fig. 5A) under optimal particle concentration has led to a relatively poor resolution of the NP population for 30-nm Au NP
Fig. 5 Effect of dwell time selection on the selective detection of 30-nm Au NPs using spICP-MS.
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100,000
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10,000,000
100,000,000
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100,000
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Fig. 6 Effect of the detection threshold selection on the selective detection of Au NPs.
from the background signal compared with the performance achieved for 0.1-ms dwell time (Fig. 5B) for which the background signal is reduced in terms of counts but the particle signal, because it is a discrete event, is not. Using millisecond dwell times, processing of the intensity data in particular for small particles often involves discriminating data corresponding to nanoparticle events (recorded as individual pulses) from those of the baseline, which include the contribution of background and/or dissolved species. To achieve this, different algorithms have been proposed [15–17]. Finally, the selection of the detection threshold is critical to the accurate determination of the NP number. Fig. 6 shows how, for smaller nanoparticles (10 nm) that are closer to the background signal, the selection of detection threshold is likely to have a higher impact in the overall uncertainty of the number concentration data even when working at the microsecond dwell range (0.1-ms dwell time).
Particle size determination with spICP-MS Conversely to particle number-based concentration, NP size cannot be determined directly using spICP-MS. The instrument measured the signal intensity, which typically using a calibration curve is converted to particle mass. For particles of known geometry (typically spherical geometry is assumed), stoichiometry and density particle size (spherical diameter equivalent) ‘d’ can be calculated from the following equation: rffiffiffiffiffiffiffiffi 3 6m d¼ 104 (10) πρ where ‘m’ is the particle mass (ng) and ‘ρ’ is the particle density (g/cm3).
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
Particle mass ‘m’ can in turn be calculated from Eq. (11) m¼
ðINP Idis Þ η Cal slope
(11)
where ‘INP’ is the particle signal intensity (cps), ‘Idiss’ is the dissolved background signal intensity (cps), ‘η’ is the transport efficiency, and ‘Calslope’ is the slope of a calibration curve derived by plotting the signal intensity recorded for a series of standards of a known mass concentration. Calibration curve used in Eq. (11) typically is based on ionic standards containing the same element of interest as the analysed particle sample, meaning that the calculated particle mass is based on the assumption that the behaviour of an element in the form of dissolved ions and as particulate in the plasma is the same. For this reason in some cases, using particle rather than ionic standard for the preparation of calibration curve might be advisable. Another issue to consider when performing mass calibration is potential matrix effects if the particle sample is suspended in a different diluent/matrix than the mass calibration standards [1]. This might lead to signal enhancement or reduction and often considerable biases in the estimated particle diameters. It is therefore recommended to check if such effects are present and, if so, to use matrix-matched approaches. It is also possible to use internal rather than external calibration strategies (such as isotope dilution) for the determination of the particle mass then its diameter [18]. These strategies are associated with better accuracy and lower uncertainty in comparison with those based on external calibration, but these might not be practical for high-throughput routine work, since they are expensive (as they require isotopically enriched standards) and time consuming. In the recent years, efforts have also been made to extend the applicability of the technique to anisotropic particle shapes, such as rodlike particles [19]. However, to obtain meaningful results, the particle geometry has to be very well defined, often by means of characterization with alternative techniques such as electron microscopy. An example of particle size distribution histogram obtained with the technique is shown in Fig. 7.
Normalised frequency
100
50
0
20
25
30
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Particle size (nm)
Fig. 7 Typical particle size distribution histogram obtained for 30-nm spherical gold particles using an Agilent 8900 ICP-MS and the external calibration approach.
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Conclusions SpICP-MS technique has evolved in the recent years, as a powerful tool allowing particle number-based concentration measurements, being one of the very few capable of producing data traceable to the SI unit of a kilogramme. Key advantages are the ability to distinguish between the particulate and dissolved element fraction and the potential for multiisotope and multielement analysis, allowing quantitative determination of particle chemical composition. The technique is suitable for liquid samples and compatible with the variety of complex matrices (such as biological, food, and cosmetics) and materials, containing elemental tags visible to ICP-MS. Particle size determination is also possible with this technique, but only for particles with very well-defined geometry, stoichiometry, and density. Finally, the analytical performance of single particle detection by ICPMS is compromised by the attainable size limit of detection. This depends on the presence of dissolved species of the target NP, on the signal contribution of high procedural blanks, and on the instrumentation used (e.g. with millisecond or microsecond dwell time detection).
References [1] M.D. Montanˇo, J.W. Olesik, A.G. Barber, K. Challis, J.F. Ranville, Single particle ICP-MS: advances toward routine analysis of nanomaterials, Anal. Bioanal. Chem. 408 (19) (2016) 5053–5074. [2] C. Degueldre, P.-Y. Favarger, S. Wold, Gold colloid analysis by inductively coupled plasma-mass spectrometry in a single particle mode, Anal. Chim. Acta 555 (2006) 263–268. [3] A. Montaser, Inductively Coupled Plasma Mass Spectrometry, Wiley, 1998. [4] S. Naasz, S. Weigel, O. Borovinskaya, A. Serva, C. Cascio, A.K. Undas, F.C. Simeone, H.J.P. Marvin, R.J.B. Peters, Multi-element analysis of single nanoparticles by ICP-MS using quadrupole and time-offlight technologies, J. Anal. At. Spectrom. 33 (2018) 835–845. [5] H.E. Pace, N.J. Rogers, C. Jarolimek, V. Coleman, C.P. Higgins, J.F. Ranville, Determining transport efficiency for the purpose of counting and sizing nanoparticles via single particle inductively coupled plasma mass spectrometry, Anal. Chem. 83 (2011) 9361–9369. [6] S.I. Miyashita, H. Mitsuhashi, S.-I. Fujii, A. Takatsu, K. Inagaki, T. Fujimoto, High transport efficiency of nanoparticles through a total-consumption sample introduction system and its beneficial application for particle size evaluation in single-particle ICP-MS, Anal. Bioanal. Chem. 409 (2017) 1531–1545. ´ lvaro, M. Estela del Castillo Busto, D.A. Ramsay, D. Bartczak, F. Pellegrino, [7] S. Cuello-Nun˜ez, I. Abad-A H. Goenaga-Infante, The accurate determination of number concentration of inorganic nanoparticles using spICP-MS with the dynamic mass flow approach, 2019 (manuscript under review). [8] Y. Dan, H. Shi, C. Stephan, X. Liang, Rapid analysis of titanium dioxide nanoparticles in sunscreens using single particle inductively coupled plasma–mass spectrometry, Microchem. J. 122 (2015) 119–126. [9] E.P. Gray, J.G. Coleman, A.J. Bednar, A.J. Kennedy, J.F. Ranville, C.P. Higgins, Extraction and analysis of silver and gold nanoparticles from biological tissues using single particle inductively coupled plasma mass spectrometry, Environ. Sci. Technol. 47 (2013) 14315–14323. [10] ISO/TS 19590:2017, Nanotechnologies—Size Distribution and Concentration of Inorganic Nanoparticles in Aqueous Media via Single Particle Inductively Coupled Plasma Mass Spectrometry, ISO, Geneva, 2017. [11] K.E. Murphy, J. Liu, A.R. Montoro Bustos, M.E. Johnson, M.R. Winchester, NIST Special Publication 1200-21, 2015. https://doi.org/10.6028/NIST.SP.1200-21. (Last Assessed 21/02/2019).
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[12] I. Abad-A´lvaro, E. Pen˜a-Vza´quez, E. Bolea, P. Bermejo-Barrera, J.R. Castillo, F. Laborda, Evaluation of number concentration quantification by single-particle inductively coupled plasma mass spectrometry: microsecond vs. millisecond dwell times, Anal. Bioanal. Chem. 408 (2016) 5089–5097. [13] A. Hineman, C. Stephan, Effect of dwell time on single particle inductively coupled plasma mass spectrometry data acquisition quality, J. Anal. At. Spectrom. 29 (2014) 1252–1257. [14] M.D. Montan˜o, H.R. Badiei, S. Bazargan, J.F. Ranville, Improvements in the detection and characterization of engineered nanoparticles using spICP-MS with microsecond dwell times, Environ. Sci. Nano 1 (2014) 338–346. [15] J. Tuoriniemi, G. Cornelis, M. Hassell€ ov, Size discrimination and detection capabilities of singleparticle ICPMS for environmental analysis of silver nanoparticles, Anal. Chem. 84 (2012) 3965–3972. [16] F. Laborda, J. Jimenez-Lamana, E. Bolea, J.R. Castillo, Critical considerations for the determination of nanoparticle number concentrations, size and number size distributions by single particle ICP-MS, J. Anal. At. Spectrom. 28 (2013) 1220–1232. [17] G. Cornelis, M. Hassell€ ov, A signal deconvolution method to discriminate smaller nanoparticles in single particle ICP-MS, J. Anal. At. Spectrom. 29 (2014) 134–144. [18] L. Telgmann, C.D. Metcalfe, H. Hintelmann, Rapid size characterization of silver nanoparticles by single particle ICP-MS and isotope dilution, J. Anal. At. Spectrom. 29 (2014) 1265–1272. [19] T.M. Nguyen, J. Liu, V.A. Hackley, Fractionation and characterization of high aspect ratio gold nanorods using asymmetric-flow field flow fractionation and single particle inductively coupled plasma mass spectrometry, Chromatography 2 (2015) 422–435.
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Single particle inductively coupled plasma mass spectrometry (spICP-MS) Heidi Goenaga-Infante, Dorota Bartczak LGC Limited, Teddington, United Kingdom
Introduction Single particle inductively coupled plasma mass spectrometry (spICP-MS) has proven to be a powerful technique for providing the number concentration of inorganic nanomaterials (NM) with minimal or no sample preparation [1]. Therefore, there is increasing interest in using this technique for routine, high-throughput applications. The approach, which was originally introduced by Degueldre et al. (2006) [2], leverages on the traditional ICP-MS measurement principle (Fig. 1) and relies on the introduction of highly diluted suspension of particles into the instrument, with the aim that only one particle at a time enters the plasma. The plasma atomizes and ionizes the constituents of the particle, which are then detected using the mass spectrometer. The characteristics of particles that can be measured include particle number concentration, mass concentration, and the mass of individual particle, which for objects of known geometries can be converted to particle diameter, also providing information about the number-based size distribution of particles in the sample. SpICP-MS, unlike other particle counting techniques, is also capable of detecting the dissolved element fraction within the same single measurement provided that a fair resolution between the NP fraction and the dissolved fraction is achieved. This is described more in details in the following section. The technique is compatible with aqueous suspensions of most types of metal and metal(loid) oxide particles and, to some extent, with other types of particles, which are capped or stained with ligands containing lanthanides, sulphur, halides, or phosphorus, since these are elements visible to ICP-MS with current instrumentation. However, the accessible lower particle size limit of detection will vary depending on the particle composition and the type of the element monitored (see Fig. 2). Recent developments in the ICP instrumentation, such as microsecond dwell times and/or quasi-simultaneous multiisotopic TOF detectors [4], allow multielement and/or multiisotope detection in spICP-MS mode, which is of particular interest in the characterization of complex, for example, core/shell, materials. In particular, the microsecond dwell detection capability has enabled selective detection of NPs that easily dissolve (e.g. Ag NPs) from the dissolved fraction (Ag ions) in the presence of high concentrations of the latter. Characterization of Nanoparticles https://doi.org/10.1016/B978-0-12-814182-3.00003-1
© 2020 Elsevier Inc. All rights reserved.
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Fig. 1 Principles of ICP-MS. A fine aerosol of a sample is introduced into a hot (6000–10,000 K) argon plasma of sufficient energy to dry the aerosol and form analyte atoms, which are simultaneously ionized. Ions are then sorted and quantified based on their mass-to-charge (m/z) ratio in the mass analyser [3].
Fig. 2 Illustration of the lower particle size limits of detection achievable with currently available ICP-MS instrumentation.
In spICP-MS analysis, to establish a relationship between the number of particle events detected over a defined analysis window (time scan) and the number of NPs in solution, the NP transport efficiency (TE) must be determined. The most popular methods used in the literature for calculation of the TE are the particle frequency and the particle size methods [5]. However, both of these approaches rely on the use of particle reference materials (RM), which are very scarce, and the few existing ones are neither like-for-like with NM used in real sample matrices nor certified for the number concentration. Alternatively, efforts have been made to significantly increase the amount of sample entering the plasma by means of total consumption introduction systems [6] with the purpose of avoiding the use of reference materials for TE estimation. Using such devices, Miyashita et al. achieved a TE of approximately 93%. Despite this relatively high TE value, the accurate determination of the correlation between the number of NPs in a volume or mass solution and that detected by spICP-MS analysis is still a requirement. Another method that has been used for TE estimation purposes is the waste collection method, which is based on the measurement of the sample uptake rate and the amount of sample going to waste. This method has been largely dismissed by the community, due to the reporting of biased results [5]. Nonetheless, it has the advantage of not relying on NP RMs to determine the TE. More recently, LGC has developed methodology for the determination of TE that does not rely on using RMs but only depends on continuous real-time mass measurements of the NP sample
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
uptake and the sample mass flow (nebulized sample) in a dynamic system [7]. The applicability of this method has been demonstrated so far for highly stable, nearly spherical, and narrowly dispersed colloidal suspensions. More details about this dynamic mass flow approach are given in ‘Transport efficiency, ‘η’’ section. A key advantage of spICP-MS, which makes the technique attractive to industry, is that samples are diluted to make sure that not more than one particle reaches the plasma ionization chamber within the response time of the instrument. The reduced occurrence of matrix effects as a result of the dilution means that the need for sample manipulation can be in many cases avoided. However, for some complex matrices such as creams, biological tissues, blood, and pastes, various extraction techniques (e.g. enzymatic or chemical) are required and have been investigated demonstrating that selective extraction of the NP from the sample can be achieved without affecting their stability [8,9]. This chapter will address the principles, advantages, and remaining challenges of spICP-MS for number concentration and size determination of inorganic NPs. It will also discuss the main parameters that mainly contribute to the accuracy and uncertainty of the number concentration data with particular emphasis on the determination of the NP TE and the existing approaches for its determination.
spICP-MS principle SpICP-MS works by acquiring individual element intensity readings with very short dwell times, in the range of few hundred microseconds to few milliseconds per data point (Fig. 3). Using such short dwell times in conjunction with highly dilute suspensions of the particles allows the detection of individual objects, hence the name ‘single particle’ ICP-MS.
Fig. 3 Principles of spICP-MS. The number of detected pulses (events) in the time scan corresponds to the particle concentration, whilst their intensity to the particle mass, in turn, can be converted to particle size. Dissolved element, potentially in the form of ions, appears as constant background signal.
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Appropriate sample dilution is critical to avoid violation of the ‘single particle rule’, when more than one object arrives at the detector in a given dwell time. For this reason, the dynamic range of spICP-MS for particle concentration measurements is limited to two orders of magnitude. As an example, with a dwell time of 100 μs, a suspension of 60-nm gold particles should be diluted to approximately 104 particles/g, if using standard sample introduction systems. Performing gravimetric rather than volumetric dilutions is generally recommended, to minimize errors arising from multiple dilution steps. The overall number of peaks detected per measurement, which typically is a 60-s time scan, is directly proportional to the number of NPs in the sample, whilst the intensity of the individual peaks corresponds to the particle mass and therefore spherical equivalent particle diameter to the third power, assuming a homogeneous distribution of the element in the particle. The sum of masses of all particles divided by the measured volume of sample gives the mass concentration of particles in the dilute sample. The concentration of the dissolved fraction of the same element can in turn be determined from a constant signal, which differs from the spikes (pulses) representing particulate matter. The ICP-MS instrument configuration required for analysis in the single particle (sp) mode is no different from the standard set-up, meaning that the instrument’s performance should be optimized for maximal signal-to-noise ratio at the particular m/z of interest. In case of isotopes, which suffer from polyatomic interferences, signal-to-noise ratio can be improved by using a collision reaction cell or sector field-based instrumentation. Recent developments in ICP instrumentation featuring dwell time as short as 0.05 ms opened up the potential for multiisotope and multielement analysis in the sp mode. Most instrument manufacturers nowadays also offer designated software for spICP-MS analysis allowing easy processing of the acquired data. Typically, spICP-MS analyses are performed using time-resolved analysis (TRA) with dwell times in the range of 0.05–10 ms. However, it is important to note that the probability of detecting a single particle pulse split between two adjacent measurement windows increases as the dwell time is decreased from 10 to 1 ms. For longer dwell times on the other hand, distinguishing particles from the background becomes more difficult; also, the probability of registering more than one particle per dwell time increases. Dwell times below 1 ms result in the particle signal being spread over multiple data points, which significantly improves the resolution of the technique but adds complexity to data processing. An advantage of using such short dwell times for the selective detection of particle events is illustrated in Fig. 4.
Particle number concentration measurements with spICP-MS The number concentration of particles in the sample, C (NP/g) can be calculated from the following equation: C¼
N Df ηV
(1)
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
Fig. 4 Selective detection of 40-nm Ag nanoparticles from dissolved Ag using spICP-MS with 0.1-ms dwell time.
where ‘N’ is the average number of particles detected per 60-s time scan (NP/min), ‘Df’ is the sample dilution factor (g/g), ‘η’ is the average transport efficiency, and ‘V’ is the average sample uptake rate (g/min). The error associated with the measurement, ΔC, can be calculated from Eq. (2): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ffi ΔDf 2 ΔN 2 Δη ΔV 2 ΔC ¼ C + + + (2) N η V Df where Δx indicates errors associated with the individual terms in Eq. (1). To determine the sample dilution factor, gravimetric rather than volumetric dilutions are recommended. The overall sample dilution factor is a simple multiplication of all the individual dilution steps, calculated from Eq. (3): msn Df ¼ (3) mas where msn is the total mass of the prepared, diluted suspension, whilst mas is the mass of the undiluted analyte sample. When preparing the sample dilution, special attention should be paid to the choice of diluent, to avoid artefacts arising from particle agglomeration/ aggregation arising from compromised colloidal stability or sample deagglomeration arising from reduction in the concentration of any cross-linking reagents. For example, dilute aqueous suspensions of electrostatically stabilized monodispersed colloids can be diluted with aqueous solution containing a small amount (1–10 mM concentration) of the stabilizing agent present on the surface of the particles to avoid particle destabilization.
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It is recommended to investigate the effect of the diluent used on the particle agglomeration/aggregation state on a case-by-case basis. In spICP-MS analysis, the sample is typically introduced using a peristaltic pump. For this reason, it is necessary to determine the sample uptake rate (meaning the amount of sample acquired per minute of the analysis) to be able to relate the number of particle events seen in a time scan to the amount of NPs in suspension. Typically, the sample uptake rate (defined as a mass flow) is measured 2–3 times per day at the beginning, in the middle, and at the end of the measurement session. This can either be done as a single point measurement, when a diluent or particle suspension is acquired over a defined time from a tube containing a known amount of this diluent or suspension (measured by weight rather than volume) or by measuring the loss of weight from the diluent or suspension continuously over at least 15 min [7]. Since the sample dilution and sample uptake can be determined by weight and the acquisition time can be measured accurately, the remaining parameters that include the transport efficiency and number of particles detected are considered the main contributing factors to the uncertainty associated with the number concentration measurement. Therefore, these are discussed more in details in the next sections.
Transport efficiency, ‘η’ Typical sample introduction systems used in the ICP instrumentation do not deliver the entire volume of the acquired sample into the plasma. The proportion of the suspension that actually goes in (usually in the range of 5%–10%) is called the transport efficiency (TE). In recent years, efforts have been made to significantly increase the amount of sample entering the plasma by means of total consumption introduction systems. Using such devices, Miyashita et al. achieved a TE of approximately 93%. Despite such promising results, compared with more traditional sample introduction systems, to correlate the number of particles in a mass of suspension and that detected by spICP-MS, accurate determination of the TE parameter is still a requirement. In fact, TE is often considered the most critical parameter in spICP-MS, as inaccuracy in its determination leads to large biases and uncertainties in the number concentration data. The most popular methods used so far for calculation of the TE are the particle frequency and the particle size methods [10, 11] as discussed in ‘Introduction’ section. For the particle frequency method, a nano-RM is introduced into the ICP-MS, and the number of detected particles is measured in, for example, 60-s long time scan. Based on such measurements and for RMs with known particle size and element mass fraction (present in the particulate form), the TE can be calculated from Eq. (4): η¼
N 4 d3 ρ π 109 100% Cm 3 8 V
(4)
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
where ‘N’ is the average number of particles detected per 60-s time scan (NP/min); ‘d’ is the mean spherical volume-equivalent particle diameter, typically measured by transmission electron microscopy ‘TEM’ (nm); ‘ρ’ is the particle density (g/cm3); ‘Cm’ is the mass concentration of particle suspension (pg/g); and ‘V’ is the average sample uptake rate (g/min). Often, the particle density is assumed to be similar to bulk material, which has been shown to be in relatively close agreement for particles composed of gold, but it is known to significantly differ for many other types of particles. For example, silicon dioxide may have densities ranging from below 1.9 g cm3 in the hydrated amorphous form of St€ ober silica 3 to above 2.6 g cm for quartz. Because all parameters of Eq. (4) come with their associated measurements errors, the overall uncertainty of TE estimated following this approach is relatively large and is the main contributing factor to the overall uncertainty associated with the particle number-based concentration measurements by spICP-MS. Only recently, a quality control material (LGCQC5050: 30-nm colloidal gold NPs) with an assessed value for number concentration has been made commercially available. For this material, the TE can be calculated from Eq. (5): η¼
N 100% Cp V
(5)
where ‘N’ is the average number of particles detected per 60-s time scan (NP/min), ‘Cp’ is the particle number-based concentration (NP/g), and ‘V’ is the average sample uptake rate (g/min). In this case, the uncertainty associated with TE would depend mostly on the errors related to the ‘Cp’ parameter given for the RM. In case of spICP-MS analysis using a nano-RM for which only the particle size (as spherical diameter equivalent) is known, the TE can be calculated following the particle size method. Here, a series of dilution of an ionic standard containing the same element of interest are measured as well as the nano-RM suspension. The TE is calculated from Eq. (6): η¼
Rionic 100% RNP
(6)
where ‘Rionic’ is the instrument’s response to ions (cps/μg) whilst ‘RNP’ is the instrument’s response to the particle suspension (cps/μg). The ‘Rionic’ and ‘RNP’ can in turn be calculated from Eqs. (7), (8), respectively: Rionic ¼
RF ion 6 107 V td
(7)
where ‘RFion’ is the instrument’s response factor to ionic standard, derived from regression analysis of the calibration curve [cps/(μg/kg)]; ‘td’ is the dwell time used (ms); and ‘V’ is the sample uptake rate (g/min): RNP ¼
INP Idiss mNP
(8)
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where ‘INP’ is the average particle intensity (cps), ‘Idiss’ is the average intensity of the dissolved background, and ‘mNP’ is the mass of element in a single particle (μg). The ‘mNP’ can be calculated from Eq. (9): mNP ¼
d3 ρ π 6 1015
(9)
where ‘d’ is the average particle diameter measured by TEM (nm) and ‘ρ’ is the particle density (g/cm3). Similarly to the particle frequency method, the particle size method is also based on several assumptions, including particle sphericity. Particle TEM size and particle density also feed into the equation, meaning that the uncertainty associated with TE estimated following the both approaches is similar. However, there are several literature reports highlighting differences in the TE values obtained with the two approaches, which have been attributed to particle losses in the containers and tubing and other effects such as possible off axis trajectories of particles in the plasma. LGC has recently developed and validated a methodology for TE determination, which does not need a RM, and it is directly traceable to the SI unit of a kilogramme. This approach, called ‘dynamic mass flow’ method, relies on the continuous real-time mass measurements of the NP sample uptake and the sample mass flow (nebulized sample) in a dynamic system [7]. Using this method, the sample waste is continuously returned to the sample vessel (i.e. no separate waste collection), and the difference in weight between the sample mass uptake and the waste mass is regularly recorded by continuous weighing in a balance during the experiment over a time period whilst the system is in equilibrium. Hence, continuous measurements of such differences with multiple data points over time are acquired. This greatly improves precision and reduces bias by obviating the need to flush the system when using the conventional waste collection method. Using the dynamic mass flow approach, the TE can be calculated from the ratio of slopes generated by monitoring the mass uptake of sample continuously over time and the mass of nebulized sample continuously over time in a dynamic system. The suitability of the dynamic mass flow method has already been demonstrated for gold and silver particles in aqueous and biological matrices following alkali extraction, but the method’s performance in applications concerning wider range of materials (such as metal oxides) and matrices (such as cosmetics or food) is yet to be investigated. The main disadvantage of this methodology is that it is more time consuming in comparison with conventional approaches that rely on RMs, so its use in routine analysis of multiple samples, as required in toxicology studies, is very limited. However, this reference method is rather useful for the characterization of other NP quality control (QC) materials for their number concentration so that they can be used in routine high-throughput applications using the frequency method for TE determination. Of course, this relies on finding QC materials that have similar properties to those of NPs present in real samples to achieve accurate results.
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
Number of detected particles in time scan, ‘N’ To optimize the number of particles detected in time scan (particle counting), several parameters have to be taken into account, including the instrument dwell time, threshold set-up between the background signal and the particle events, and the sample dilution factor. This is because the uncertainty associated with particle counting is related to systematic and random errors arising from these parameters [12]. Using quadrupole ICP-MS, the frequency of data acquisition is directly controlled by the dwell time. The effect of dwell times ranging from milliseconds to microseconds on the quality of the number concentration data has been studied earlier; lower precision from counting statistics and bias (from multiple particle events recorded as single events) has been obtained with microsecond dwell times [12]. Therefore, the use of dwell times in the microsecond range has become common practice in the past few years with the advances in ICP-MS instrumentation [12–14]. Also, with dwell times 100 μs, wider linear ranges have been obtained due to lower occurrence of multiple events counted as a single event. Finally, a reduced impact of background/dissolved fraction contribution on the selectivity of NP detection was achieved when using microsecond dwell times in comparison with those in the millisecond range. Of course, despite of the advantages of microsecond dwell times discussed earlier, one should be aware that the wrong selection of the particle concentration in solution may still lead to bias in the number concentration results. Fig. 5 shows that the use of 3-ms dwell time (Fig. 5A) under optimal particle concentration has led to a relatively poor resolution of the NP population for 30-nm Au NP
Fig. 5 Effect of dwell time selection on the selective detection of 30-nm Au NPs using spICP-MS.
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Characterization of nanoparticles
40
20
0
10 nm
10,000
100,000
1,000,000
10,000,000
100,000,000
40
20
0
30 nm
10,000
100,000
1,000,000
10,000,000
100,000,000
Fig. 6 Effect of the detection threshold selection on the selective detection of Au NPs.
from the background signal compared with the performance achieved for 0.1-ms dwell time (Fig. 5B) for which the background signal is reduced in terms of counts but the particle signal, because it is a discrete event, is not. Using millisecond dwell times, processing of the intensity data in particular for small particles often involves discriminating data corresponding to nanoparticle events (recorded as individual pulses) from those of the baseline, which include the contribution of background and/or dissolved species. To achieve this, different algorithms have been proposed [15–17]. Finally, the selection of the detection threshold is critical to the accurate determination of the NP number. Fig. 6 shows how, for smaller nanoparticles (10 nm) that are closer to the background signal, the selection of detection threshold is likely to have a higher impact in the overall uncertainty of the number concentration data even when working at the microsecond dwell range (0.1-ms dwell time).
Particle size determination with spICP-MS Conversely to particle number-based concentration, NP size cannot be determined directly using spICP-MS. The instrument measured the signal intensity, which typically using a calibration curve is converted to particle mass. For particles of known geometry (typically spherical geometry is assumed), stoichiometry and density particle size (spherical diameter equivalent) ‘d’ can be calculated from the following equation: rffiffiffiffiffiffiffiffi 3 6m d¼ 104 (10) πρ where ‘m’ is the particle mass (ng) and ‘ρ’ is the particle density (g/cm3).
Single particle inductively coupled plasma mass spectrometry (spICP-MS)
Particle mass ‘m’ can in turn be calculated from Eq. (11) m¼
ðINP Idis Þ η Cal slope
(11)
where ‘INP’ is the particle signal intensity (cps), ‘Idiss’ is the dissolved background signal intensity (cps), ‘η’ is the transport efficiency, and ‘Calslope’ is the slope of a calibration curve derived by plotting the signal intensity recorded for a series of standards of a known mass concentration. Calibration curve used in Eq. (11) typically is based on ionic standards containing the same element of interest as the analysed particle sample, meaning that the calculated particle mass is based on the assumption that the behaviour of an element in the form of dissolved ions and as particulate in the plasma is the same. For this reason in some cases, using particle rather than ionic standard for the preparation of calibration curve might be advisable. Another issue to consider when performing mass calibration is potential matrix effects if the particle sample is suspended in a different diluent/matrix than the mass calibration standards [1]. This might lead to signal enhancement or reduction and often considerable biases in the estimated particle diameters. It is therefore recommended to check if such effects are present and, if so, to use matrix-matched approaches. It is also possible to use internal rather than external calibration strategies (such as isotope dilution) for the determination of the particle mass then its diameter [18]. These strategies are associated with better accuracy and lower uncertainty in comparison with those based on external calibration, but these might not be practical for high-throughput routine work, since they are expensive (as they require isotopically enriched standards) and time consuming. In the recent years, efforts have also been made to extend the applicability of the technique to anisotropic particle shapes, such as rodlike particles [19]. However, to obtain meaningful results, the particle geometry has to be very well defined, often by means of characterization with alternative techniques such as electron microscopy. An example of particle size distribution histogram obtained with the technique is shown in Fig. 7.
Normalised frequency
100
50
0
20
25
30
35
40
Particle size (nm)
Fig. 7 Typical particle size distribution histogram obtained for 30-nm spherical gold particles using an Agilent 8900 ICP-MS and the external calibration approach.
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Conclusions SpICP-MS technique has evolved in the recent years, as a powerful tool allowing particle number-based concentration measurements, being one of the very few capable of producing data traceable to the SI unit of a kilogramme. Key advantages are the ability to distinguish between the particulate and dissolved element fraction and the potential for multiisotope and multielement analysis, allowing quantitative determination of particle chemical composition. The technique is suitable for liquid samples and compatible with the variety of complex matrices (such as biological, food, and cosmetics) and materials, containing elemental tags visible to ICP-MS. Particle size determination is also possible with this technique, but only for particles with very well-defined geometry, stoichiometry, and density. Finally, the analytical performance of single particle detection by ICPMS is compromised by the attainable size limit of detection. This depends on the presence of dissolved species of the target NP, on the signal contribution of high procedural blanks, and on the instrumentation used (e.g. with millisecond or microsecond dwell time detection).
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Single particle inductively coupled plasma mass spectrometry (spICP-MS)
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