Detection of magnetic iron nanoparticles by single-particle ICP-TOFMS: case study for a magnetic filtration medical device.

Nanoparticles are increasingly used in medical products and devices. Their properties are critical for such applications, as particle characteristics determine their interaction with the biological system, and, therefore, the performance and safety of the final product. Among the most important nanoparticle characteristics and parameters are particle mass distribution, composition, total particle mass, and number concentration. In this study, we utilize single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) for the characterization of inorganic nanoparticles in complex biological fluids. We report online microdroplet calibration for reference-nanomaterial-free and matrix-matched calibration of carbon-coated iron carbide nanoparticles (C/Fe3C NPs). As a case study, we analyze C/Fe3C NPs designed for targeted blood purification. Through the analysis of NP mass distributions, we study the effect of the NP surface modification on aggregation of C/Fe3C NPs in whole blood. We also demonstrate the efficiency of removal of coated C/Fe3C NP from saline by magnetically enhanced filters. Magnetic filtering is shown to reduce the mass concentration of detectable C/Fe3C NPs by 99.99 ± 0.01% in water.

Quantification and Clustering of Inorganic Nanoparticles in Wastewater Treatment Plants across Switzerland.

Single particle Inductively Coupled Plasma Time-of-Flight Mass Spectrometry (sp-ICP-TOFMS), in combination with online microdroplet calibration, allows the determination of particle number concentrations (PNCs) and the masses of elements in individual particles. Because sp-ICP-TOFMS analyses of environmental samples produce rich datasets composed of both single-metal nanoparticles (smNPs) and many types of multimetal NPs (mmNPs), interpretation of these data is well suited to automated analysis schemes. Here, we present a data analysis approach that includes automatic particle detection and elemental mass determinations based on online microdroplet calibration, and unsupervised clustering analysis of mmNPs to identify unique classes of NPs based on their element compositions. To demonstrate the potential of our approach, we analyzed wastewater samples collected from the influent and effluent of five wastewater treatment plants (WWTPs) across Switzerland. We determined elemental masses in individual NPs, as well as PNCs, to estimate the NP removal efficiencies of the individual WWTPs. Through hierarchical clustering, we identified NP classes conserved across all WWTPs; the most abundant particle types were those rich in Ce-La, Fe-Al, Ti-Zr, and Zn-Cu. In addition, we found particle types that are unique to one or a few WWTPs, which could indicate point sources of anthropogenic NPs.

Emerging investigator series: automated single-nanoparticle quantification and classification: a holistic study of particles into and out of wastewater treatment plants in Switzerland.

Single particle inductively coupled plasma time-of-flight mass spectrometry (sp-ICP-TOFMS), in combination with online microdroplet calibration, allows for the determination of particle number concentrations (PNCs) and the amount (i.e. mass) of ICP-MS-accessible elements in individual particles. Because sp-ICP-TOFMS analyses of environmental samples produce rich datasets composed of both single-metal nanoparticles (smNPs) and many types of multi-metal NPs (mmNPs), interpretation of these data is well suited to automated analysis schemes. Here, we present a new data analysis approach that includes: 1. automatic particle detection and elemental mass determinations based on online microdroplet calibration, 2. correction of false (randomly occurring) multi-metal associations caused by measurement of coincident but distinct NPs, and 3. unsupervised clustering analysis of mmNPs to identify unique classes of NPs based on their element compositions. To demonstrate the potential of our approach, we analyzed water samples collected from the influent and effluent of five wastewater treatment plants (WWTPs) across Switzerland. We determined elemental masses in individual NPs, as well as PNCs, to estimate the NP removal efficiencies of the individual WWTPs. From WWTP samples collected at two points in time, we found an average of 90% and 94% removal efficiencies of single-metal and multi-metal NPs, respectively. Between 5% to 27% of detected NPs were multi-metal; the most abundant particle types were those rich in Ce-La, Fe-Al, Ti-Zr, and Zn-Cu. Through hierarchical clustering, we identified NP classes conserved across all WWTPs, as well as particle types that are unique to one or a few WWTPs. These uniquely occurring particle types may represent point sources of anthropogenic NPs. We describe the utility of clustering analysis of mmNPs for identifying natural, geogenic NPs, and also for the discovery of new, potentially anthropogenic, NP targets.

Monte Carlo Simulation of Low-Count Signals in Time-of-Flight Mass Spectrometry and Its Application to Single-Particle Detection.

Many modern time-of-flight mass spectrometry (TOFMS) instruments use fast analog-to-digital conversion (ADC) with high-speed digitizers to record mass spectra with extended dynamic range (compared to time-to-digital conversion). The extended dynamic range offered by ADC detection is critical for accurate measurement of transient events. However, the use of ADC also increases the variance of the measurements by sampling the gain statistics of electron multipliers (EMs) used for detection. The influence of gain statistics on the shape of TOF signal distributions is especially pronounced at low count rates and is a major contributor to measurement variance. Here, we use Monte Carlo methods to simulate low-ion-count TOFMS signals as a function of Poisson statistics and the measured pulse-height distribution (PHD) of the EM detection system. We find that a compound Poisson distribution calculated via Monte Carlo simulation effectively describes the shape of measured TOFMS signals. Additionally, we apply Monte Carlo simulation results to single-particle inductively coupled plasma (sp-ICP) TOFMS analysis. We demonstrate that subtraction of modeled TOFMS signals can be used to quantitatively uncover particle-signal distributions buried beneath dissolved-signal backgrounds. On the basis of simulated signal distributions, we also calculate new critical values ( LC) that are used as decision thresholds for the detection of discrete particles. This new detection criterion better accounts for the shape of dissolved signal distributions and therefore provides more robust identification of single particles with ICP-TOFMS.

Improvements in Nanoparticle Tracking Analysis To Measure Particle Aggregation and Mass Distribution: A Case Study on Engineered Nanomaterial Stability in Incineration Landfill Leachates.

Numerous nanometrology techniques have been developed in recent years to determine the size, concentration, and a number of other characteristics of engineered nanomaterials (ENM) in environmental matrices. Among the many available techniques, nanoparticle tracking analysis (NTA) can measure individual particles to create a size distribution and measure the particle number. Therefore, we explore the possibility to use these data to calculate the particle mass distribution. Additionally, we further developed the NTA methodology to explore its suitability for analysis of ENM in complex matrices by measuring ENM agglomeration and sedimentation in municipal solid waste incineration landfill leachates over time. 100 nm Au ENM were spiked into DI H2O and synthetic and natural leachates. We present the possibility of measuring ENM in the presence of natural particles based on differences in particle refractivity indices, delineate the necessity of creating a calibration curve to adjust the given NTA particle number concentration, and determine the instruments linear range under different conditions. By measuring the particle size and the particle number distribution, we were able to calculate the ENM mass remaining in suspension. By combining these metrics together with transmission electron microscopy (TEM) analyses, we could assess the extent of both homo- and heteroagglomeration as well as particle sedimentation. Reporting both size and mass based metrics is common in atmospheric particle measurements, but now, the NTA can give us the possibility of applying the same approach also to aqueous samples.