Single Particle Inductively Coupled Plasma Time-of-Flight Mass Spectrometry—A Powerful Tool for the Analysis of Nanoparticles in the Environment

Abstract

Single-particle inductively coupled plasma-mass spectrometry (SP-ICP-MS) has emerged as an important tool for the characterization of inorganic nanoparticles (NPs) in the environment. Although most SP-ICP-MS applications rely on the quadrupole ICP-MS (ICP-QMS), it is limited by the slow scanning speed of the quadrupole. Recent advancements in instrumentation have led to the development of inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS) which offers a viable solution. In this review, we discuss the recent advances in instrumentation and methodology of ICP-TOF-MS, followed by a detailed discussion of the applications of SP-ICP-TOFMS in analyzing NPs in the environment. SP-ICP-TOFMS has the potential to identify and quantify both anthropogenic and natural NPs in the environment, providing valuable insights into their occurrence, fate, behavior, and potential environmental risks.

1. Introduction

The widespread use of nanoparticles (NPs) in industry continues to pose threats to the environment and increase health risks to organisms [1,2]. The release of engineered NPs from industrial products (such as metal nanoparticles and carbon nano-tubes) inevitably results in human exposure to NPs and is increasing with the rapidly expanding production of engineered NPs [3]. In this context, controlling NP discharge, evaluating NP health risks, and developing new regulations on NPs depend on improving the current knowledge about the occurrence, fate, behavior, and potential risks of NPs in the environment [4]. Therefore, there is an urgent need for the development of innovative and reliable methods of NP analysis [5], which require increasingly sophisticated nanometrology capable of providing accurate and robust quantitation and characterization of NPs.

NPs are generally heterogeneous in size, composition, crystallinity, and surfaces, and these characteristics significantly impact the industrial performance and environmental fate of NPs. Although instrumentation and standardized methods have been developed for decades to examine nano-scale features [6,7], the laborious sample preparation and insufficient detection limits (e.g., much higher than the actual concentration of μg·L⁻¹ in environmental samples) hinder reliable, accurate, and high-throughput analysis. Moreover, many of the available methods are not suitable for analyzing NPs in a real environmental sample with complex matrixes and interferences. Furthermore, there is even less characterization of individual nanoparticles, and analysis of NPs at a single particle level continues to be a limiting factor for risk assessment and the development of NP pollution monitoring approaches.

To address the above challenges in analyzing NPs, inductively coupled plasma-mass spectrometry (ICP-MS) has been adapted to provide sensitive, element-specific, and high throughput single particle analysis [8]. ICP-MS involves desolvating, atomizing, and ionizing a sample in solution in the ion source of a high-temperature plasma (~6000 K) with the resulting ions analyzed by a mass analyzer [9]. By introducing a sufficiently diluted suspension of NPs into ICP-MS, only one particle is statistically introduced at a time. This transforms ICP-MS into a versatile technique with unique advantages that can provide information on the elemental composition, number concentration, and size distribution of NPs with mass concentrations down to the ng L⁻¹ at a single particle level [10,11]. This technique is commonly referred to as single particle ICP-MS (SP-ICP-MS).

SP-ICP-MS has a high-throughput capability and relatively low running cost for single particle analysis and is considered an emerging tool for detecting and characterizing NPs in the environment. Initially, single particle analysis was carried out using inductively coupled plasma-optical emission spectroscopy (ICP-OES) [12]. Later, ICP-MS was introduced to improve the sensitivity and detection limits for single particle analysis [13,14,15,16,17]. The spectral interference in ICP-MS was a challenge, but it was addressed by using collision/reaction cell technology [18], a high-resolution mass analyzer [19], or a triple quadrupole ICP-MS (ICP-MS/MS) system [20,21].

Currently, most applications of SP-ICP-MS rely on the quadrupole ICP-MS (ICP-QMS). It is important to note that scanning-type mass analyzers (e.g., a quadrupole) can only monitor ions with a single m/z ratio at a time [22,23]. However, both anthropogenic and natural NPs in the real world are complex when found in water [24,25], soil [26,27], and indoor dust [28], often containing multiple elements. Moreover, NPs become even more complex after undergoing chemical and physical transformations due to interactions with environmental matter. Therefore, multi-element analysis in SP-ICP-MS is crucial for the comprehensive characterization of NPs in real-world samples.

Inductively coupled plasma time-of-flight mass spectrometry (ICP-TOFMS) allows for multi-element analysis in a short time period, thus making it a promising tool for the analysis of NPs in real-world samples [29,30,31,32]. TOFMS was first combined with an ICP in the early 1990s [33] and, after several decades of development, it has achieved better detection limits and analytical speed. The new generation of ICP-TOFMS can determine the entire mass spectrum (usually from 7 to 275 Th) in tens of microseconds, enabling the analysis of multi-elements in a single NP [34,35].

This review will describe recent advances in the instrumentation and methodology of ICP-TOF-MS, followed by a detailed discussion of the applications of SP-ICP-TOFMS in analyzing NPs in the environment. The future prospects of SP-ICP-TOFMS methods and applications will also be discussed.

2. SP-ICP-TOFMS: Instrumentation and Methodology

2.1. TOF Analyser for Single Particle Analysis

SP-ICP-MS has become a widely used and routine tool in many fields [28,36,37,38]. Commercial instruments typically use one of the following mass analyzers: quadrupole (Q), sector field (SF), or time-of-flight (TOF). For the ICP-MS equipped with a scanning mass analyzer, fast and continuous switching between different m/z channels is required for monitoring a transit signal [31]. The settling time and dwell time are key parameters for the accurate analysis of NPs. The dwell time is the time required to measure a single m/z value in a single run while the settling time is the time required for the analyzers to stabilize for the measurement of the next m/z value. Due to the short duration of a single particle in the plasma (usually a few hundred microseconds [6]), it is hard to detect more than one or two isotopes per NP in a single run, even using the shortest dwell time and settling time in the modern quadrupole ICP-MS [39].

To improve the accuracy of particle analysis, the signal can be elongated to several milliseconds using collision cell technology to achieve the accurate and precise analysis of more than one isotope in a single NP [40]. Multi-collector ICP-MS(MC-ICP-MS) is an alternative technique to the multi-element analysis of a single NP due to its capability to simultaneously monitor multiple isotopes [41]. Equipped with a fast detector (e.g., a multichannel ion counter), MC-ICP-MS achieves the simultaneous acquisition of several isotopes with a very short dwell time (e.g., 30 μs [42]), making it a powerful tool for the multi-element analysis of a single NP. However, when analyzing NPs with more complex elements, only a limited number of isotopes within a restricted mass range can be detected. If the sample is unknown, the target elements must be screened first, followed by targeted analysis, which may reduce sample utilization rates.

In contrast to the above mass analyzers, time-of-flight (TOF) mass analyzers provide a quasi-simultaneous detection of all elements and have great advantages for multi-element and high throughput analysis of single particles. Commercial TOF mass spectrometry was introduced in the 1950s and its outstanding features have been confirmed. The principle of TOFMS involves generating ions, followed by ion acceleration, and measuring the flight time of ions in a drift tube. During the process, all ions acquire the same kinetic energy in the acceleration region, with differences in velocity arising from variations in mass-to-charge. If the flight distance is known, it is possible to determine the mass-to-charge ratio of the ions by measuring the flight time of the ions, as demonstrated in Equations (1) and (2):

$$v=\sqrt{\frac{2zeV}{m}}$$

(1)

$$t=\frac{L}{v}=\sqrt{\frac{m}{2zeV}}·L$$

(2)

where $v$ is the speed of an ion;$z$ is the charge of the ion;$e$ is the electron charge; $V$ is the acceleration voltage; $m$ is the mass of the ion; $t$ is the flight time; and $L$ is the flight distance of the ion.

In a commercial ICP-TOFMS, the heaviest ions reach the detector in the tens of microseconds, which means that a few full mass scans can be completed for a transit signal from a single NP [43]. A new generation of ICP-TOFMS instruments is currently on the market, including the icpTOF from TOFWERK, Vitesse from Nu Instruments, and CyTOF from Standard BioTools (formerly known as Fluidigm). These instruments use the same orthogonal design and single-pass reflectron TOF design. With a fast acquisition speed (i.e., 30 μs for one TOF full mass spectrum extractions [34]), ICP-TOFMS can determine multi-elements in a single NP and become a promising technique for single cell analysis and single particle analysis [6,44,45].

2.2. Sample Introduction Systems for Single Particle Analysis

The sample introduction system is regarded as the Achilles’ heel of ICP-MS. A standard sample introduction system contains a chamber and a nebulizer with a typical transport efficiency of less than 5%, which decreases with an increase in the sample uptake rate [46]. However, by using a modified sample introduction system with a single-pass chamber and a low-consumption nebulizer, transportation efficiency can be improved [47]. For example, Tharaud et al. achieved ~100% transport efficiency by using a direct injection high-efficiency nebulizer [48].

Standard sample introduction systems generate polydisperse aerosols that lead to an inaccurate analysis with SP-ICP-MS due to different ionization processes and sampling biases that NPs undergo. Moreover, standard sample introduction systems often suffer from severe matrix effects. To address these issues, monodisperse droplets generated by either a commercial piezoelectric dispenser [49] or a microfluidics-based droplet dispenser [50] have been used to create a better sample introduction method. Hendriks et al. demonstrated that the microdroplets generated by an online introduction system can be used as an accurate and matrix-independent calibration for single particle analysis with SP-ICP-TOFMS [51].

In addition to solution analysis, in situ solid sampling can be achieved using laser ablation (LA) as a sample introduction system. In LA-ICP-MS analysis, solid samples are ablated by high-power laser shots, and the resulting aerosols are transported and analyzed by ICP-MS. When the laser fluence is attenuated at a suitable level, LA-ICP-MS could be used as a sensitive tool for analyzing and imaging NPs as the intact NPs are transported into the ion source by a carrier gas. This method provides in situ information on particle size and number. Metarapi et al. used LA-SP-ICP-MS to image and discriminate silver NPs (AgNPs) and silver ions in sunflower roots [52]. Additionally, Wang et al. imaged AgNPs and released Ag ions in the organs of mice exposed to AgNPs using LA-SP-ICP-MS, providing a valuable tool to study the uptake, translocation, and degradation characteristics of NPs in organisms [53].

The sample introduction system is crucial for successful SP-ICP-MS analysis, and different sample introduction methods are gradually overcoming the challenges faced by standard methods [48,51]. Together with recent developments in TOF instrumentation, the sample introduction systems make it more feasible to analyze single particles by ICP-TOFMS.

2.3. Identification of NPs from Backgrounds

The complexity of signals in SP-ICP-MS surpasses that of traditional ICP-MS solution analysis. In SP-ICP-MS, a single particle is statistically introduced into the plasma at a time, producing an ion cloud that represents the elemental composition of the particle. The ion cloud is then passed through the ion optics and mass analyzer, resulting in a transient signal with typical durations between 300 and 1000 μs [6,54,55]. Separating the transient signal from the steady-state background is a critical aspect of SP-ICP-MS. An accurate measurement of the size and concentration of NPs is only possible if the NP signal can be distinguished from the background signal.

The most common strategy for detecting NPs in SP-ICP-MS is to treat NP signals as outliers from background signals. An iterative algorithm is used to discriminate a NP from the dissolved background when the NP signal exceeds the critical value ($L_C$), as shown in Equation (3) [56,57,58].

$$L_C=n{\sigma }_b$$

(3)

where $L_C$ is the critical value; ${\sigma }_b$ is the standard deviation of the background; n is the abscissa of the standardized normal distribution defined by false-positive errors (α).

There is no agreement on the value of n, and different values are found in the literature, typically ranging from 3σ [59,60] to 5σ [59,61,62]. It should be noted that the n-σ threshold method assumes that the background signals in SP-ICP-MS follow a Gaussian distribution.

Some researchers have also developed critical value approaches based on Poisson-distributed background signals [5]. As defined by Currie [63] and adopted by IUPAC [64], there are two detection criteria: the critical value ($L_c$, the minimum detectable signal) and the detection limit ($L_D$, the minimum signal level that results in reliably detected signals), which are defined by false-positive errors (α) and false-negative errors (β) [65]. Poisson distributions show a significant asymmetry for the low values of its mean. The typical value usually used for α and β is 0.05 as shown in Equations (4) and (5) [63].

$$L_C=1.64\sqrt{{\lambda }_b} (\mathsf{\alpha }=0.05)$$

(4)

$$L_D=2.71+3.29\sqrt{{\lambda }_b} (\mathsf{\alpha }=\mathrm{0.05},\mathsf{\beta }=\mathrm{0.05})$$

(5)

where ${\lambda }_b$ is the average count rate of the background signal.

However, the shape of mass spectrometric signals obtained by an analog-to-digital conversion (ADC) with high-speed digitizers in modern ICP-TOFMS instruments often does not follow a Gaussian distribution, especially for low-count signals [65,66]. This is because the use of such high-speed digitizers increases the variance from Poisson noise and causes the output signals of an electron multiplier detector to have a distribution (i.e., the pulse-height distribution, PHD) [65]. In this case, the shape of ICP-TOFMS signals can be described by a compound Poisson distribution of the measured PHD of the detector and a Poisson distributed ion arrival at the detector [65,66]. Gundlach-Graham et al. developed a Monte Carlo simulation of the TOF signals of single particle analysis by ICP-TOFMS. They proposed a new method to calculate Lc and L_D, which is used as the threshold for single particle analysis by ICP-TOFMS. The new α and β values are 0.001 and 0.05, respectively [65]. As shown in Equations (6) and (7)

$$L_C={\lambda }_b+3.41\sqrt{{\lambda }_b}+1.6 (\mathsf{\alpha }=0.001)$$

(6)

$$L_D={\lambda }_b+5.24\sqrt{{\lambda }_b}+5.54 (\mathsf{\alpha }=0.001,\mathsf{\beta }=0.05)$$

(7)

where ${\lambda }_b$ is the average count rate of the background signal.

This new method can effectively separate the overlapping background from NP distributions, resulting in a more accurate detection threshold and size measurement of NPs [65]. Moreover, it can be applied to other mass spectrometers that are equipped with electron multiplier detectors and fast digitizers.

2.4. Quantification for SP-ICP-TOFMS

Calibration is an essential step in SP-ICP-TOFMS, as it enables the accurate and quantitative analysis of NPs in solution. In SP-ICP-TOFMS, the intensity of NP signals is proportional to the mass of the NPs, and the number of events detected is proportional to the number of NPs in the sample solution. Currently, many methods are used for quantitative analysis by SP-ICP-MS [5,45].

The first quantitative method involves utilizing NP standard materials to establish a functional relationship between the particle size and signal response. However, the lack of standard NP materials of similar composition, shape, and size as those of NP samples limits its applicability.

The second quantitative method, widely used by researchers, relies on the calibration curve of a standard solution and the measurement of transport efficiency. This method assumes that the ionization efficiency difference between the standard solution and NPs can be disregarded, which is generally true. To achieve accurate quantification, measuring the transport efficiency (η_neb) is crucial. Three methods have been proposed for measuring transport efficiency, including the waste collection, particle frequency, and particle size methods [57].

The waste collection method indirectly determines transport efficiency by collecting the waste solution out of the spray chamber and calculating the actual amount of analyte entering the ICP-MS. However, this method may overestimate transport efficiency due to the presence of water vapor and residual liquid in the spray chamber [57]. The particle frequency method calculates transport efficiency by dividing the number of detected events by the total number of NPs sampled during the data acquisition time. However, determining the accurate concentration of NPs is challenging due to the lack of NP standard materials and potential NP aggregation. The particle size method involves comparing the sensitivity of an element in NPs with that obtained from the standard solution of the same element. The transport efficiency can be calculated by dividing the two sensitivities. Many studies show that the particle size method provides superior accuracy [45]. However, if there is a difference in the ionization efficiency of the NPs and standard solutions, it may introduce additional errors [67].

The two quantitative methods mentioned above do not fully utilize the benefits of the full-element analytical capability available with SP-ICP-TOFMS. The third quantitative method is the use of monodisperse microdroplets as quantitative standards of NPs [68,69]. This method, illustrated in Figure 1 [70], uses a two-sample introduction system where a microdroplet containing a known element concentration is merged into an aerosol produced by a pneumatic nebulizer and then introduced into the ICP-TOFMS. The online microdroplet calibration technique offers an automatic matrix-matching calibration of signals from individual NPs [51,71]. Mehrabi et al. determined the size and concentration of NPs by the online microdroplet calibration method while accounting for matrix effects in the single particle analysis in a single step [70]. Harycki et al. conducted a study to evaluate the effectiveness of online microdroplet calibration for quantifying nanoparticles in three organic matrices—ethanol, acetone, and acetonitrile. Despite these matrices causing signal attenuation up to 35 times and having a nebulizer transport efficiency 4 times higher than pure water matrices, the NP sizes and particle number concentration (PNC) in the organic matrices were determined with 98% accuracy [72].

Compared to other single-particle calibration methods, the microdroplet calibration method offers the advantage of the elimination of matrix effects. In addition, mass quantification is not reliant on measuring the sample transfer efficiency. Furthermore, the mass is calibrated for each particle measurement, compensating for the instrument drift and other possible negative effects during a long run.

3. SP-ICP-TOFMS: Applications

3.1. Simultaneous Quantification of Multiple Elements in a Single Particle

SP-ICP-TOFMS is a promising approach that enables the multiplexed detection and quantification of diverse metal and metal-oxide NPs [35]. ICP-TOFMS is non-targeted multi-element measurement that allows the quantification of individual particles, enabling the accurate measurement of high-throughput and in situ multi-elements. For diverse environmental samples, SP-ICP-MS still has the potential to measure real environmental samples at a level of 10²–10⁶ NPs·mL⁻¹ [73]. This method is practical for quantifying natural and anthropogenic nanoparticles in complex or unclear environmental matrices, which is critical for the ecotoxicological risk assessment of NPs, including engineered nanoparticles and natural nanoparticles [74,75,76].

The complexity of their composition and structure makes it difficult to determine the properties of NPs. At present, the research on composite nanoparticles mainly focuses on the core/shell structure of spherical nanoparticles with an uneven distribution of element components. Au in core and Ag in shell structure has usually been used for the evaluation of multi-element accuracy [66]. Generally, the measurement sensitivity of these elements is relatively high without much interference [29]. However, their behavior cannot be generalized and extrapolated to other composite nanoparticles, such as nano-steel (a Fe, Cr, Ni, Mo alloy used in composites) and bismuth vanadate particles (BiVO₄) [77,78]. Naasz et al. [31] provided a systematic and critical evaluation of the performance of ICP-TOFMS and ICP-QMS instruments for the analysis of nanoparticles used in a variety of industrial applications with complex structures and compositions. They found that only SP-ICP-TOFMS can accurately assess the elemental composition of nano-steel particles. Erhardt et al. achieved full element quantification of ice core samples in the environment by a combination of SP-ICP-TOFMS with continuous flow analysis [79]. This setup allows for accurately measuring target element concentrations over the entire mass range without losing sensitivity as the number of analytes increases.

Different elemental compositions on a single particle can often indicate the source of the particle. Multi-element analysis by SP-ICP-TOFMS has been applied to more complex samples such as air samples (e.g., road dust [80], samples from the International Space Station [81], and biomass-burning aerosol and ash [82]), water samples (e.g., wastewater [83] and rainfall [84]), and geological samples (e.g., soil [30] and minerals [32]). These applications provide guidelines for exploring how trace elements are transported into the environment. In addition, SP-ICP-TOFMS is expected to be used in medical research. Nanoparticles are increasingly used in medical products and devices, and their properties are critical for such applications. Recently, Mehrabi et al. detected magnetic iron nanoparticles by SP-ICP-TOFMS and applied it to a case study of magnetic filtering medical devices. Magnetic filtration was shown to reduce the mass concentration of detectable C/Fe₃C NPs by 99.99 ± 0.01% in water [85].

For many analytical techniques, it is difficult to assign the particle type in a sample that contains mixtures of NPs with similar major and minor element compositions. The elemental composition of a single particle can provide much information, especially in the environment. For example, the origin of Ce-NPs is related to the presence of other rare earth elements. Based on this characteristic, Szakas et al. reported a new class of anthropogenic accidental Ce-NPs, which cannot be distinguished from natural Ce-NPs by the previous binary classification approach [86].

Another major challenge is to distinguish and quantify anthropogenic particles from naturally occurring particles [1]. The unknown multi-element NPs constitute the bulk of accidental particles as the sources are often composed of many complex elements (e.g., brake and tire wear [87]). Particles from different regions have different elemental compositions that form specific clusters, which is called “elemental fingerprints”. Elemental fingerprinting can be used for tracing and migration clustering of particulate matter [82,83]. Due to elemental complexity, the need for the analysis and integration of data generated by SP-ICP-TOFMS is gradually increasing. There is no complete inventory of commercial or industrial-engineered NPs, and few data are available on the abundance of natural NPs. Establishing inventories of engineered and natural NPs depends on the development of high-throughput analytical methods. SP-ICP-TOFMS provides a direct way to build such databases.

For the huge data obtained by SP-ICP-TOFMS, some studies have developed new datum processing methods. Baalousha et al. characterized soil NPs at the single particle level in order to determine the purity, composition, association, and ratio of the elements in the NPs [88]. To identify unique metallic fingerprints in natural NPs, cluster analysis was performed using MATLAB to identify clusters/groups of natural nanoparticles with similar elemental composition and to determine their average elemental composition. This method has also been applied to the element cluster analysis of mineral dust aerosols [89]. In addition, Mehrabi et al. proposed a method that employed automated single-nanoparticle quantification and classification for an unsupervised clustering analysis of multi-metal NPs to identify unique classes of NPs based on their element compositions [83,90]. Furthermore, Holbrook et al. built a machine-learning model using Pearson correlations and unsupervised t-distributed stochastic neighbor embedding (t-SNE) to find patterns of co-occurring elements and attribute possible particle sources based on the values reported in the literature [91]. As shown in Figure 2, Pearson correlations were used to find trends in element correlations, and t-SNE projects the high-dimensional dataset (a 25-element feature set with a 1–4 element dimension target) into a lower-dimensional space of 2 dimensions [91]. The factor that often has the strongest impact is the perplexity argument in t-SNE analysis, which was tested using values of 5, 30, and 50 by the author. The performance of the data result is the space between apparent clusters and data points (a value of 30 was chosen for these samples, as shown in Figure 2B). The information obtained from the correlation and t-SNE analysis was combined with the reported document element tags to create an efficient data processing pipeline using the LightGBM multi-class classifier.

The machine learning model ultimately automates the dataset labeling and classification work, providing a fast and efficient method for inter/intra sample comparison in terms of multi-element NP elemental correlations. The pipeline can be further developed in the future to fully automate the analysis process for large particle datasets. In addition, a binomial logistic regression (LR) written by Bland et al. used the Python Sci-Kit learning module to compile a binomial LR combined with the SP-ICP-MS dataset to discriminate engineered titanium dioxide nanomaterials from natural titanium nanomaterials in soil [26].

Table 1. The selected applications of SP(SC)-ICP-TOFMS.

ICP-TOFMS Instrument	Sample Type	Sample Treatment	Sample Introduction System	Key Elements to Observe	Main Conclusion	Ref.
Prototype icpTOF	CeO2 ENPs and CeO2 NNPs in soil	Colloid extraction procedure	Pneumatic nebulizer and cyclonic spray chamber	CeO2 ENPs, elemental fingerprint (Ce/La) associated with Ce- containing NNPs	CeO2 ENPs and Ce-NNPs were found to have elemental associations with La, Nd, and Th. CeO2 ENPs could be quantified in the matrix of Ce-NNPs based on multi-element NP element fingerprinting.	[30]
icpTOF R	TiO2 ENPs and TiO2 NNPs in lake water	Centrifugation to remove large particles, sonication, shaking in vortex, and dialysis	NC	Ti/Al, Ti/Mn, Ti/Fe, Ti/Pb, and Fe/Pb in a single particle	TiO2 ENPs, Ti-containing NPs, and associations with Al, Mn, Fe, and Pb have been proposed as indicators of Ti-NPs.	[92]
icpTOF R	Polar ice ice-core sample	Combined with Continuous flow analysis, the ice was melted and introduced	Bern CFA system, micro mist, and glass expansion	Element ratio in Fe-containing NPs (Mg/Al, Fe/Al, and Mg/Fe)	ICP-TOFMS improved the resolution of the CFA; the iron-bearing aerosol concentration covaried with atmospheric particulate dust concentration. Further evidence of particle traceability was provided by the isotope.	[79]
icpTOF R	Steels	Dilution, acid treatment, sonication, centrifugation, and redispersion	Pneumatic nebulizer and cyclonic spray chamber	Ti and Nb	Quantified TiCN, NbCN, and TiNbCN NPs.	[93]
icpTOF R	NPs in a heavy metal matrix, acid, and PBS matrix	Dilution and sonication	PFA MicroFlow pneumatic nebulizer, double pass cyclonic spray chamber, and microdroplet generator	Cs, Au, and Ag isotope	The study focused on the accurate calibration of NP size in various matrices using an online microdroplet calibration.	[51]
icpTOF 2R	River and urban runoff	Dilution, and sonication	PFA MicroFlow pneumatic nebulizer and quartz cyclonic spray chamber	Zn, with Fe, Mn, Al, and Si	The multiple elements in each particle were quantified and tracked. It was possible to develop the basis of the field of particle-by-particle geology.	[94]
Vitesse	Runoff from outdoor stains and paints	Sonication and filtration	NC	TiO2, Al, Si, Fe, Zr, and Ce	TiO2 ENPs and Ti-NNPs associated with Al, Si, Fe, Zr, and Ce were proposed to indicate the assignment of particles as Ti-NNP.	[95]
icpTOF R	Biomass-burning aerosol and ash	Dilution and filtration	PFA MicroFlow pneumatic nebulizer and cyclonic spray chamber	Zn, Al, Si, Fe	The source of burned biomass was discussed. The source of burned biomass Zn and other crustal elements after biomass burning were more likely to be present in ash than in the biomass burning aerosol.	[82]
icpTOF R	Topsoil samples from the surface to 15 cm below the surface	Wet sieve (45 μm), freeze-drying, dilution, and extraction by tetrasodium pyrophosphate	MicroFlow PFA pneumatic nebulizer and cyclonic spray chamber	Al, Fe, Ti, Si, Ce, Zr, Zn, Sb, and Sn	The elemental composition and associations of natural nanomaterials were analyzed at the single-particle level. Clustering analysis was used to distinguish NNPs. This study provided a methodology and baseline information on NNPs that can be used to differentiate NNPs from ENPs in environmental systems.	[88]
icpTOF R	Sedimentation basin, road dust, and tunnel road dust	Cloud point extraction and applied to slide	Microflow PFA pneumatic nebulizer and quartz cyclonic spray chamber	Cu, Zn, Sr, Y, Zr, Nb, Rh, Ru, Pd, Sn, Sb, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Lu, Hf, Pt, Au, and Pb	Machine learning was developed to label and classify particle samples, providing a fast and effective method for inter- and intra-sample comparisons based on multi-element particle correlations.	[91]
icpTOF R	Vacuum bag dust samples collected from the International Space Station (ISS)	Dilution, resting, filtration, cleaning with compressed air, and redispersion	Pneumatic nebulizer and cyclonic spray chamber	Zr, Al, Ti, Fe, Ag, Pb, Mo, Cu, Sn, Ni, and Cr	The particle populations composed of different elements in the ISS and their sources were analyzed.	[81]
icpTOF 2R	WWTPs in Switzerland	Sonication, stewing, and dilution for the top sample	MicroFlow pneumatic nebulizer and PFA T-piece baffled cyclonic spray chamber/microdroplet generator	All elements	The continued development of elemental fingerprints to achieve the automatic quantification and classification of individual particles.	[83,90]
icpTOF R	NbCN, TiNbCN extracted by steel, and ENPs in soil	Sonication, dilution in ultrahigh purity water	Pneumatic nebulizer and cyclonic spray chamber	All elements	Total element screening and single particle fingerprinting were found to effectively avoid the false results caused by the complex samples of inorganic particles containing organic compounds.	[27]
icpTOF R	Surface waters following rainfall	Well-shaking, sonication, and centrifugation to remove large particles	Pneumatic nebulizer and cyclonic spray chamber	Ti, Nb, Ce, and La	TiO2 was used as a tracer to monitor urban runoff. The study found that naturally occurring particles had the same elemental ratios and origin.	[84]
icpTOF R	Yeast cells and algal cells	Dilution in Milli Q water	NC	Mg, P, Ru, Ca, and Fe	After cells were stained with Ru red, it was found that the Ru content was directly related to cell volume, and cell size could be calculated by combining it with known cell shapes, leading to the calculation of the concentration of the target element in individual cells.	[96]
Vitesse	Global surface waters and precipitation	Sonication and filtration	Aridus II desolator	Ti, Ce, and Ag	The concentrations of Ti-, Ce-, and Ag-containing NPs were presented for both surface waters and precipitation. The origin was determined from the size and composition of the nanoparticles.	[97]
icpTOF 2R	Pt NPs	Leached with diluted nitric acid and dilution	Microdroplet generator introduction, control, and autosampler system	Pt isotope and W isotope	Using the online isotope dilution analysis method, particles were characterized with a 194Pt/195Pt ratio while monitoring 182W/183W for mass bias correction, allowing an accurate quantification at a high matrix concentration.	[98]
icpTOF R	Soil spiked TiO2	<500 nm particle extraction from soil and sludge, enrichment with cloud point extraction, and dilution for analysis	Concentric borosilicate glass nebulizer and baffled cyclonic, high-purity quartz spray chamber	Ti, Ce, Ba, Rb, Fe, Mg, Mn, Nb, Pb, and other earth-abundant elements	Machine learning models of elemental fingerprinting and mass distribution were used to identify TiO2 ENPs and NNPs in soil; this method effectively reduced the effect of a high matrix.	[26]
icpTOF R	Soil	Same as the last one	Concentric borosilicate glass nebulizer and baffled cyclonic, high-purity quartz spray chamber	Ti isotope	This study is to evaluate the traceability of isotopically enriched ENPs at the individual particle level in soil and provides guidance on the isotope enrichment requirements for the quantification of ENPs from earth-abundant elements in soils.	[99]
icpTOF 2R	Gunshot residues	Settling to remove large particles and collecting the suspension’s surface	PFA MicroFlow pneumatic nebulizer and quartz cyclonic spray chamber	Mg-U (65 species)	The GSR particles were classified and their particle size was determined. In addition, the composition of the GSR particles was analyzed.	[100]
icpTOF S2	Nano-scale mineral dust aerosols (MDAs) in snow	Sonication and filtration	Micro FAST MC autosampler, PFA pneumatic nebulizer, and cyclonic spray chamber	Al, Ti, Mn, Fe, Cu, Zn, Y, Zr, Nb, La, Ce, Nd, Pb, Th, and U	The particle size and composition of MDAs in wet deposits could be effectively analyzed by SP-ICP-TOFMS, but the quantification of the particle number has a greater uncertainty. The characterization of nanoscale MDAs can be used to better understand particle dynamics in the atmosphere.	[89]
icpTOF S2	TiO2 in organic matrices	Sonication and dilution in ultrapure water	PFA MicroFlow pneumatic nebulizer and piezoelectric droplet generator cyclonic spray chamber	Cs and Ti	TiO2 NPs in the organic matrix were accurately quantified by using the online microdroplet calibration method.	[72]
icpTOF S2	Microplastic containing metals	Aqueous dispersions	Cyclonic spray chamber, quartz nebulizer with nanoparticle measurement, and pneumatic nebulizer with an autosampler for microplastic measurement and microdroplet introduction	C, Ag, Au, Ce, Eu, Ho, and Lu	Low m/z detection capabilities were explored by analyzing carbon and metals in both microdroplets and uniform polystyrene (PS) beads.	[101]
icpTOF R	Anisotropic copper crystals	Dilution	Microdroplet introduction	Cu, Au, Ag, and Pd	Bimetallic physical mixtures (CuAg + CuPd) could be distinguished from multi-metallic NPs. Nanoscale structures relevant to bulk phenomena could be easily quantified and characterized with ensemble-representative reliability.	[102]
icpTOF 2R	C/Fe3C NPs in whole blood	106× dilution	Pneumatic nebulizer and cyclonic spray chamber	Cr, Fe, and Ce	By analyzing the NP mass distributions, the study showed the effect of NP surface modification on the aggregation of C/Fe3C NPs in whole blood. Magnetic filtration was able to significantly reduce detectable particles in water.	[85]
icpTOF 2R	River and its surrounding tributaries	Soaking with diluted nitric acid, rinsing with Milli-Q water, and filtration	Quartz cyclonic spray chamber	Si, Al, Fe, Pb, Mn, and Ce	Major element distributions showed diverse mineral populations. The elemental symbiosis of Ce/La and symbiosis of Fe, Mn, and Pb were found.	[32]
icpTOF 2R	Pb NPs were added to lake sediment (LDSK) samples	Centrifugation and colloid extraction procedure	Concentric pneumatic nebulizer combined with a membrane desolvation unit	Pb, Fe, Mg, Mn, Pb, and Zn	The SP-ICP-TOFMS method was developed to extract Pt NPs from LDSK, and its multi-element analysis was used to analyze the symbiotic elements of Pt in LDSK.	[103]
icpTOF 2R	Ag NPs and algal cells exposed to Ag NPs	Centrifugation and dilution	PFA MicroFlow pneumatic nebulizer and quartz cyclonic spray chamber	Ag isotope	The ability to monitor AgNPs and intracellular silver isotope ratios was investigated.	[104]
icpTOF R	Road dust particles	Sieving to remove large particles, well-dispersed with tube rotator, centrifugation, and top suspension collected	MicroFlow PFA pneumatic nebulizer and cyclonic spray chamber	Al, Ti, V, Cr, Fe, Co, Ni, Cu, Zn, Sn, Ce, Zr, Pb, and W	Samples were analyzed for total metal concentrations, particle elemental composition and ratios, and clustering. The study provided a reliable comprehensive approach to the characterization of road dust particles.	[80]
icpTOF 2R	Yeast cells	Any consequent dilutions before the injections	Quartz cyclonic spray chamber and SC-175 nebulizer	P and Pb	Coupling Asymmetric Flow Field Fractionation (AF4) with SC-ICP-TOFMS effectively removes the influence of heavy metal ions in the mass spectrum and simplifies the sample analysis process.	[105]
CyTOF	Intrahepatic and peripheral natural killer (NK) cells	Enzymatical digestion, filtration, and density gradient centrifugation	NC	32 species transition-metal elements	Revealed the landscape of NK cell phenotypes in HCC patients to find potential immunotherapy targets by profiling the status of 32 surface markers in individual healthy and hepatocarcinoma cells.	[106]
CyTOF	Human immune cells, stem cells, and tumor cells	Antibody labeling	NC	194Pt, 195Pt, 196Pt,198Pt, 115In,113In, and Pd	Metallic antibodies were used to label cells and a live cell barcode was established for the analysis of samples containing heterogeneous populations, such as mixtures of tumor cells and tumor-infiltrating leukocytes.	[107]
CyTOF	Dorsal root ganglia from C57/BL6 mice of both sexes	All samples were thawed, barcode labeled, and uniformly stained	NC	50 species transition-metal elements and isotopes	A total of 30 molecularly distinct somatosensory glial and 41 distinct neuronal states across all time points in C57/BL6 mice of both sexes from embryonic day 11.5 to postnatal day 4 were quantified.	[108]

NC: Not clear. ENPs: Engineered nanoparticles. NNPs: Natural nanoparticles.

shows the selected applications of SP-ICP-TOFMS.

3.2. SP-ICP-TOFMS Isotope Analysis

Specialized ICP-MS instruments such as the Multi-Collector ICP-MS (MC-ICP-MS) and ICP-TOFMS are used to measure accurate isotopic ratios [34,109]. MC-ICP-MS can provide high-precision isotopic ratios. Also, MC-ICP-MS has been shown to be capable of detecting multiple isotopes in single particles with an excellent accuracy [41,42,110].

Although MC-ICP-MS can measure a number of isotopes simultaneously, the m/z range and number of the isotopes are limited, which is depending on the number of the detectors installed on the instrument, making it difficult to apply to extensive elemental analysis. In addition, Faraday detectors on MC-ICP-MS instruments have a slow response time [110]. Consequently, the detector may not be able to detect the transient signals generated from NPs.

An advantage of SP-ICP-TOFMS over ICP-QMS is the ability to measure isotopic ratios in single particles. Tian et al. used several types of ICP-MS (ICP-QMS, ICP-TOFMS, and MC-ICP-MS) to simultaneously detect ¹⁰⁷Ag and ¹⁰⁹Ag in single AgNPs and single cyanobacterial cells exposed to AgNPs [104]. The results showed that ICP-QMS has a poor performance in isotope ratio analysis, but accurate silver ratios can be obtained by ICP-TOFMS and MC-ICP-MS. Compared to MC-ICP-MS, ICP-TOFMS can detect almost 100% paired events of single particles [104]. Bland et al. determined ⁴⁷Ti-enriched TiO₂ NPs in soil using SP-ICP-TOFMS and evaluated the tracking ability of isotope-enriched engineered NPs at the single particle level in soil [102]. The selected applications of SP-ICP-TOFMS in isotope ratio analysis of single particles are shown in Table 1.

3.3. Single Cell (SC)-ICP-TOFMS

In recent years, single-cell analysis has become a growing field and has widely applied in biomedical research. Single-cell analysis is essential to reveal population heterogeneity, identify minority subpopulations of interest, and discover the unique characteristics of individual cells. Although several methods are available to analyze single cells, they are usually time-consuming and unable to detect elements in single cells [111,112]. Single cell-inductively coupled plasma-mass spectrometry (SC-ICP-MS) can be used to quantify elements in single cells. When ICP-TOFMS is used for single cell analysis, full mass spectrum can be obtained and there is no need to compromise on the analytes measured. All intrinsic elements in single cells can be measured, providing more insights into single cells [113]. This type of analysis does not require labeling or staining, as cells are detected based on their “native” elemental fingerprints [114]. Cell species can be distinguished by measuring elemental micronutrients unique to a particular cell type. For example, algal cells are rich in Mg [115], a core component of the chlorophyll pigment that is essential for photosynthesis. Therefore, the elemental composition can be used as a unique fingerprint to clearly identify different cell species.

Mass cytometry (CyTOF) is a recently developed method that combines ICP-TOFMS with flow cytometry [116,117,118]. This technology uses metal isotopes instead of fluorophores for antibodies labelling. Compared to traditional flow cytometry, the number of analytical channels in CyTOF is over 100 and the interference between adjacent channels is as low as ~0.1% [117], solving the problem of fluorescence crosstalk. CyTOF has the limitation on analytical throughput (~1000 events/s [119]) but provides more complex and multidimensional data than traditional flow cytometry. With the increasing demand for high throughput in bioanalytical research, it is critical to maximize the ability to produce information about multiple markers in a single run [120].

Currently, CyTOF allows for the simultaneous detection of up to 50 metal-isotope labels on a single cell [34]. Such highly multiparametric detection has provided new insights into the complexity of biology in applications ranging from the deep phenotyping of tumors to signaling pathways of the immune system [101,121]. Antibodies are mainly used as cell staining agents in CyTOF, which fails to detect most intrinsic elements (less than 75 Th) in single cells. Bendall et al. used labeled antibodies to bind to human bone marrow cells and simultaneously analyzed up to 34 different cell parameters by CyTOF [122]. Recently, Wen et al. explored the potential of ruthenium red as a stain for single-cell analysis [96], and ruthenium red allows the elemental content to be directly correlated with cell volume to accurately calculate the intracellular concentration of target elements in single cells. By measuring metal atoms at the cellular level, the fundamental biological processes regulated by metalloproteins and metalloenzymes can be better understood [123]. Table 1 also shows the selected application of CyTOF in single cell analysis.

4. Summary and Prospect

SP-ICP-TOFMS is a powerful analytical technique used for the characterization of NPs. Rapid advances in ICP-TOFMS instrumentation have made it possible to detect smaller NPs with greater efficiency and accuracy. The development of SP-ICP-TOFMS methodologies has also been successful in many research fields, allowing for the analysis of individual NPs with high sensitivity and specificity. SP-ICP-TOFMS is continuously evolving to overcome its current limitations and it is expected that new generations of ICP-TOFMS will further improve their ability to detect smaller NPs with better accuracy. Furthermore, the data processing for SP-ICP-TOFMS will become more automated with the development of advanced data processing programs. In addition, other analytical methods will also be coupled with SP-ICP-TOFMS to provide more comprehensive and useful information about NPs at the same time. As a result, it is expected that SP-ICP-TOFMS will continue to expand its applications and become a valuable tool in many fields, including nanotechnology, environmental science, and biomedicine.