ICP-MS for the analysis at the nanoscale – a tutorial review

Abstract

The scope of this tutorial review is (i) to provide an overview on ICP-MS based techniques for the analysis of ENPs and natural nanoparticles/colloids by (a) “stand alone” ICP-MS and (b) hyphenated techniques; (ii) highlighting the benefits and pitfalls of each technique as well as providing practical advice regarding method development; (iii) illustrating the possibilities and limitations of each technique by practical applications from the recent literature.

Development of a suitable detection method for silver nanoparticles in fish tissue using single particle ICP-MS

“…There is also a lack of suitable techniques for the routine environmental monitoring of ENMs in biota, 4 or to determine the bioaccumulation potential of particulate forms in fish. 5 The available methods for detecting and characterising ENMs in environmental samples have been documented (reviews, [6][7][8] ). For tissue samples, the approaches for metallic particles have been mainly concerned with the in situ detection of the presence or absence of particles, such as scanning electron microscopy coupled with energy dispersive X-ray measurements (EDX) for elemental composition.…”

Inductively coupled plasma mass spectrometry based platforms for studies involving nanoparticle effects in biological samples

“…Because of the use of such a field, the macromolecules or NPs move from the “inlet side” to the “outlet/detector side” into a laminar flow, reaching different channel heights in the channel bottom, called an “accumulation wall.” In addition to the distance from the accumulation wall and upon the establishment of equilibrium, the separation of analytes/particles using FFF is based on their diffusion coefficient and molecular weight in which the smaller species exhibit a faster diffusion, from the accumulation wall into the carrier flow, than the larger species. Then, each sample constituent leaves the channel at different times and reaches the detection system …”

“…In such cases, distinct, specific NPs–membrane interactions can be observed, resulting in changes in the retention/elution time of NPs with a subsequent reduction in the accuracy of the results for the size and concentration of the NPs . In addition, because the calibration methods used in FFF do not consider the elution time variation due to different NPs–membrane interactions, incorrect information can be generated, compromising the association of the results obtained with the real size of the NPs, especially for the aggregated ones . On the contrary, FFF‐ICP‐MS strategies, introduced in the early 1990s, expanded the applicability of the technique to distinct fields such as biotechnology, pharmaceutical, and nanomedicine, because FFF coupling to ICP‐MS has advantages such as high selectivity and sensitive detection .…”

A quantitative nanoparticle extraction method for microsecond time resolved single-particle ICP-MS data in the presence of a high background

“…These signals can be characterized as individual spikes from NPs, whereas the background (BG) represents the level of dissolved metal in the solution. 12 Several reviews summarized the capabilities and limitations of SP-ICP-MS, [13][14][15] emphasizing that there is still room for improvement. Namely, more wellcharacterized standards are needed for size and particle number concentration (PNC) determination, matrix effects require more detailed investigations and should be accounted for, the linear dynamic range of the pulse counting stage of the secondary electron multiplier (SEM) is limited, etc.…”

“…Typically, researchers use the three times the SD of the BG (3 Â SD BG ) based on the normal distribution of the data to differentiate NP signal distributions from the BG when ICP-MS data are obtained with millisecond time resolution. 14,15,27 Cornelis and Hassellöv reported a deconvolution approach to differentiate the NPs that are not fully separated from the BG by modelling the noise contribution in ICP-Q-MS. 28 Recently, Gundlach-Graham et al used a Monte Carlo simulation approach to obtain the signal distribution of noise and lowcount signals in ICP time-of-ight (TOF)-MS data at a mass spectral acquisition rate of 200 Hz. In their study, thresholds for the detection of NPs were determined using Poisson statistics.…”