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Single-particle inductively coupled plasma-mass spectrometry (sp-ICP-MS) is one of the most powerful tools in the thriving field of nanomaterial analysis. Along the same lines, single-cell ICP-MS (sc-ICP-MS) has become an invaluable tool in the study of the variances of cell populations down to a per-cell basis. Their importance and application fields have been listed numerous times, across various reports and reviews. However, not enough attention has been paid to the immense and ongoing development of the tools that are currently available to the analytical community for the acquisition, and more importantly, the treatment of single-particle and single-cell-related data. Due to the ever-increasing demands of modern research, the efficient and dependable treatment of the data has become more important than ever. In addition, the field of single-particle and single-cell analysis suffers due to a large number of approaches for the generated data—with varying levels of specificity and applicability. As a result, finding the appropriate tool or approach, or even comparing results, can be challenging. This article will attempt to bridge these gaps, by covering the evolution and current state of the tools at the disposal of sp-ICP-MS users.
Graphical Abstract
The analysis of per- and polyfluoroalkyl substances (PFAS) via sum parameters like extractable organic fluorine (EOF) in combination with high resolution-continuum source-graphite furnace molecular absorption spectrometry (HR–CS–GFMAS) is highly promising regarding fluorine sensitivity and selectivity. However, the HR–CS–GFMAS method includes several drying and heating steps which can lead to losses of volatile PFAS before the molecular formation step using e.g., GaF formation. Hence, the method leads to a strong discrimination of PFAS within the EOF depending on their physical/chemical properties and is therefore associated with reduced accuracy. To reduce this discrepancy and to indicate realistic PFAS pollution values, an optimization of the HR–CS–GFMAS method for PFAS analysis is needed. Hence, we determined fluorine response factors of several PFAS with different physical/chemical properties upon application of systematic optimization steps. We could therefore improve the method's sensitivity for PFAS analysis using a modifier drying pre-treatment step followed by a sequential injection of sample solutions. The highest improvement in sensitivity of volatile PFAS was shown upon addition of a Mg modifier during drying pre-treatment. Thereby, during optimization the relative standard deviation of fluorine response factors could be reduced from 55 % (initial method) to 27 % (optimized method) leading to a more accurate determination of organofluorine sum parameters. The method provides an instrumental LOD and LOQ of β(F) 1.71 μg/L and 5.13 μg/L, respectively. Further validation aimed to investigate several matrix effects with respect to water matrices. Here, substance-specific behavior was observed. For example, perfluorooctanoic acid (PFOA) which was used as calibrator, showed signal suppressions upon high chloride concentrations (>50 mg/L). Hence, a thorough separation of Cl from analytes during sample preparation is needed for accurate sum parameter analysis.
An accurate measurement of the amount fraction of hydrogen in gas mixtures is mandatory for practical applications, requiring methods that are fast, continuous, robust, and cost-effective. This study compares the performance of Raman and benchtop NMR process spectroscopy for determining the hydrogen amount fraction in gas mixtures. A setup was designed to integrate both techniques, enabling measurements of the same sample.
Tests were conducted with gravimetrically prepared gas mixtures of reference quality ranging from 1.20 cmol/mol to 85.83 cmol/mol of hydrogen. The results demonstrate that Raman spectroscopy provides superior performance, with a minimal root mean square error (RMSE) of 0.22 cmol/mol and excellent linearity. In contrast, benchtop NMR spectroscopy faced challenges, such as overlapping peaks and longer measurement times, resulting in a higher RMSE of 0.71 cmol/mol. Raman spectroscopy proves to be particularly well-suited for
practical applications due to its high accuracy and linearity. Meanwhile, benchtop NMR spectroscopy holds potential for future enhancements through ongoing technological advances, such as higher magnetic field strengths. In summary, the results from our study indicate that Raman spectroscopy is already a serviceable method for precise hydrogen quantification, whereas benchtop NMR spectroscopy can be attributed potential for future applications.
Quantum dots (QDs) possess unique optoelectronic properties, which make them very attractive to be used as optical probes in biosensing and bioimaging applications. The strong absorbance and light scattering of biological compounds like tissue and blood in the visible range pose a problem. However, if optical probes emitting in the near-infrared (NIR) range are used, scattering, absorption, and autofluorescence of biological components are strongly reduced. This allows for an increased light penetration depth and higher spatial and temporal resolution for the investigation of biological processes. The synthesis and application of NIR emitting QDs is a fast-growing research field and the benefits of using QDs were demonstrated for a variety of applications, such as photoelectrochemical biosensor, in vivo vascular imaging, and fluorescence-guided surgery. This article reviews the state-of-the-art developments in the preparation of NIR/IR QDs and highlights the latest research about their utilization in biosensing and bioimaging applications.
The analysis of the presence and content of substances that are toxic to aquatic life in waste is essential for classification of waste with regard to hazard property (HP) 14 ‘ecotoxic’. For the determination of HP14 classified copper (Cu) and zinc (Zn) compounds in various municipal solid waste incineration bottom ashes (IBA) and one fly ash (FA) from Germany we applied X-ray absorption near-edge structure (XANES) spectroscopy in combination with linear combination fitting. The analysis showed that approx. 50–70% of Cu in the IBA are Cu(I) compounds and elemental Cu(0), but these compounds were not equally distributed in the different IBA. In contrast, the majority (approx. 50–70%) of Zn in all IBA is elemental zinc, which originates from brass or other alloys and galvanized metals with a large content of zinc in the waste. The FA contain higher mass fraction on Zn and other toxic elements, but similar Cu and Zn species. Additional performed selective extraction at a pH of 4 with an organic acid of some IBA showed that the ecotoxic Zn fraction is mainly elemental zinc and zinc oxide. In contrast, for the ecotoxic Cu fraction within the IBA no specific compound could be identified. Furthermore, the XANES analysis showed that the HP14 properties of especially Cu in IBA is overestimated with current best-practice guidelines for sample processing for the current substance-related approach with the 0.1% cut-off rule for each substance. However, it should be considered whether it would not be better from an environmental point of view to take the ecotoxicologically leachable copper and zinc as a reference value.
Non‐targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance–based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field.In this work, we illustrate which ML‐based methods are available for compound annotation in non‐targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies.
The gravimetric isotope mixture method is the primary method to determine absolute isotope ratios. This method, however, depends on the existence of suitable spike materials and knowledge of their purities. Determining the purity of the spikes can be tedious and labour-intensive. In this publication, an advancement of the gravimetric isotope mixture method, rendering the determination of the purity of the spike materials unnecessary, is presented. The advancement combines mass spectrometry and ion chromatography leading to an approach being independent of the purity of the spike materials. In the manuscript the mathematical background and the basic idea of the novel approach are described using a two-isotope system like copper or lithium.
Fenton-like processes using persulfate for oxidative water treatment and contaminant removal can be enhanced by the addition of redox-active biochar, which accelerates the reduction of Fe(III) to Fe(II) and increases the yield of reactive species that react with organic contaminants. However, available data on the formation of non-radical or radical species in the biochar/Fe(III)/persulfate system are inconsistent, which limits the evaluation of treatment efficiency and applicability in different water matrices. Based on competition kinetics calculations, we employed different scavengers and probe compounds to systematically evaluate the effect of chloride in presence of organic matter on the formation of major reactive species in the biochar/Fe(III)/persulfate system for the transformation of the model compound N,N‑diethyl-m-toluamide (DEET) at pH 2.5. We show that the transformation of methyl phenyl sulfoxide (PMSO) to methyl phenyl sulfone (PMSO2) cannot serve as a reliable indicator for Fe(IV), as previously suggested, because sulfate radicals also induce PMSO2 formation. Although the formation of Fe(IV) cannot be completely excluded, sulfate radicals were identified as the major reactive species in the biochar/Fe(III)/persulfate system in pure water. In the presence of dissolved organic matter, low chloride concentrations (0.1 mM) shifted the major reactive species likely to hydroxyl radicals. Higher chloride concentrations (1 mM), as present in a mining-impacted acidic surface water, resulted in the formation of another reactive species, possibly Cl2•−, and efficient DEET degradation. To tailor the application of this oxidation process, the water matrix must be considered as a decisive factor for reactive species formation and contaminant removal.
This research group has been demonstrating the significant advantages of using Nb2O5 coatings for functionalizing titanium, aluminium, and stainless steels. Regarding the biomedical sector and considering Ti-6Al-4V alloy, the reactive sputtering technique improved the cell viability, the osteogenic performance of cells involved in the osseointegration process as well as the ability to delay bacterial proliferation. The characteristics of the Nb2O5 coatings were assessed before by using standard methods, which provide information only a few tens of nanometers depth. Given that the Nb2O5 coating fabricated in this work exhibits a thickness of approximately 300 nm, the GE-XANES technique emerges as the most suitable method for this analysis. Additional information was provided with the aid of nanoindentation load-depth (P-h) curves. GE-XANES results indicated the formation of a homogeneous layer of Nb2O5 coating on the Ti-6Al-4V surfaces. The deposition process improved the surface hardness of the Ti-6Al-4V alloy (4.38 GPa versus 5.62 GPa) considering the 2 mN load.