Citation Link: https://doi.org/10.25819/ubsi/10739
Development and Optimization of Fast Single Particle Inductively Coupled Plasma Mass Spectrometry as a Tool for Nanomaterial Analysis in Environmental Waters
Alternate Title
Entwicklung und Optimierung der schnellen Einzelpartikel-Massenspektrometrie mit induktiv gekoppeltem Plasma als Instrument zur Analyse von Nanomaterialien in Umweltgewässern
Publication Type
Doctoral Thesis
Author(s)
Date Issued
2025
Abstract
Single particle inductively coupled plasma mass spectrometry (spICP-MS) is an instrumental analysis method that enables the determination of the size, size distribution, and particle number concentration of nanoparticles (NP) in suspensions. Due to its comparatively short total analysis time of a few minutes and minimal required sample preparation, spICP-MS has evolved to a routine research tool to characterize NP in aqueous samples, as e.g. surface water, sea water, and waste water. Because nanoparticle toxicity can be size dependent (among other factors), it is crucial to be able to detect NP in the largest possible size range. This thesis reports novel instrumental developments and data processing pathways that aim to decrease the lower size detection limit of spICP-MS with a quadrupole mass analyzer and to further enhance analysis and data evaluation speed.
First, a novel data acquisition unit is tested that provides nanosecond time resolution spICP-MS (4 ns, nanoDAQ). In contrast to previous spICP-MS analysis methods with milli- or microsecond time resolution, this fast measurement mode enables not only the detection of individual NP, but even the detection of individual atoms that make up each NP. Because the structure of the obtained data differs from conventional spICP-MS, a data processing workflow is developed to determine NP sizes and particle number concentrations. It is shown that the prototype nanoDAQ achieves a size detection limit for gold NP in bi-distilled water samples of ca. 7 nm. Subsequently, an optimized version of the nanoDAQ prototype is evaluated, which was developed to reduce measurement artifacts. The figures of merit of the improved system are showcased.
In order to reduce and facilitate data processing steps, an automated ion cloud recognition algorithm is tested and described, which had been tailor-made to detect, characterize, and count NP signals in the nanosecond time-resolved data from the nanoDAQ. The algorithm is first tested on model suspensions of spherical gold NP (4.7–125 nm diameter) in bi-distilled water samples and is compared to the previously used, elaborate data processing workflow. After careful evaluation of the method capabilities, the nanoDAQ system and the automated algorithm are utilized for the analysis of surface water and waste water samples from a field study. With the novel method, NP containing Ag, Ce, and Zr could be detected successfully in regional surface water samples from the area of Siegen and their particle number concentrations (PNC) could be estimated. The estimated PNC agree well with comparative values that were reported in the literature for river systems in Europe and North America.
First, a novel data acquisition unit is tested that provides nanosecond time resolution spICP-MS (4 ns, nanoDAQ). In contrast to previous spICP-MS analysis methods with milli- or microsecond time resolution, this fast measurement mode enables not only the detection of individual NP, but even the detection of individual atoms that make up each NP. Because the structure of the obtained data differs from conventional spICP-MS, a data processing workflow is developed to determine NP sizes and particle number concentrations. It is shown that the prototype nanoDAQ achieves a size detection limit for gold NP in bi-distilled water samples of ca. 7 nm. Subsequently, an optimized version of the nanoDAQ prototype is evaluated, which was developed to reduce measurement artifacts. The figures of merit of the improved system are showcased.
In order to reduce and facilitate data processing steps, an automated ion cloud recognition algorithm is tested and described, which had been tailor-made to detect, characterize, and count NP signals in the nanosecond time-resolved data from the nanoDAQ. The algorithm is first tested on model suspensions of spherical gold NP (4.7–125 nm diameter) in bi-distilled water samples and is compared to the previously used, elaborate data processing workflow. After careful evaluation of the method capabilities, the nanoDAQ system and the automated algorithm are utilized for the analysis of surface water and waste water samples from a field study. With the novel method, NP containing Ag, Ce, and Zr could be detected successfully in regional surface water samples from the area of Siegen and their particle number concentrations (PNC) could be estimated. The estimated PNC agree well with comparative values that were reported in the literature for river systems in Europe and North America.
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