In this study, we investigate the thermal diffusion behavior of oxygen in bulk aluminum nitride (AlN) using an aluminum oxide (Al2O3) coating layer as an oxygen source. We used dynamic secondary ion mass spectrometry (D-SIMS) and time-of-flight SIMS (ToF-SIMS) for accurate profiling of the oxygen concentration across the Al2O3 / AlN interface of as-grown and annealed samples. Initial tailing and limited dynamic range of ToF-SIMS measurements due to oxygen redeposition was addressed by employing an optimized dual-beam sputtering strategy with staged crater resizing. This eliminates all adverse effects and allows achieving detection sensitivity and depth resolution comparable to D-SIMS. Additional artifacts in the chemical profiling arise from surface roughening and localized phase transitions from amorphous Al2O3 to partially crystalline AlxOy induced by the high-temperature annealing, which are supported by correlative AFM (atomic force microscopy) and TEM (transmission electron microscopy) analyses. Eliminating all these factors reveals that, upon high-temperature annealing, a limited thermal diffusion of oxygen into AlN occurs after 12 h at 1600 degrees C , or after 4 and 1 h at 1700 degrees C , respectively, indicating an oxygen diffusion coefficient below 1.8 & times; 10(-16) cm(2) s(- 1) under these conditions.

We report on a comprehensive, international interlaboratory comparison of multiple thin film samples, including pure metals, stoichiometric oxides, multilayers consisting of three different metals, non-stoichiometric alloys, and a lithium-ion battery material based on nickel, manganese, and cobalt oxides. The thickness of the layers ranged from a few tens of nanometers to about one micrometer, depending on the type of thin film. The participants of the interlaboratory comparison analyzed the samples using X-ray fluorescence analysis and a relative standard deviation of the results ranging from about 3% to 17% was observed, with thicker alloy samples tending to perform worse. An extensive pre-characterization scheme was used in the form of different complementary analytical techniques such as X-ray reflectometry, time-of-flight secondary ion mass spectrometry, and X-ray tomography. Synchrotron-based reference-free X-ray fluorescence analysis measurements were used to determine physically traceable results. Using such a variety of independent methods ensured a robust overall validation approach. This corroborates that calibration samples for such thin films can be designed, produced, and qualified in a flexible and straightforward manner. These calibration samples can easily be integrated into process control for a variety of application fields, including X-ray fluorescence analysis and other techniques.

The continuous technological development of electronic devices and the introduction of new materials lead to ever greater demands on the fabrication of semiconductor heterostructures and their characterization. This work focuses on optimizing Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) depth profiles of semiconductor heterostructures aiming at a minimization of measurement-induced profile broadening. As a model system, a state-of-the-art Molecular Beam Epitaxy (MBE) grown multilayer homostructure consisting of natSi/28Si bilayers with only 2 nm in thickness is investigated while varying the most relevant sputter parameters. Atomic concentration-depth profiles are determined and an error function based description model is used to quantify layer thicknesses as well as profile broadening. The optimization process leads to an excellent resolution of the multilayer homostructure. The results of this optimization guide to a ToF-SIMS analysis of another MBE grown heterostructure consisting of a strained and highly purified 28Si layer sandwiched between two Si0.7Ge0.3 layers. The sandwiched 28Si layer represents a quantum well that has proven to be an excellent host for the implementation of electron-spin qubits.

Ensuring the safety and sustainability of advanced materials (AdMas) is critical for fostering innovation while protecting human health and the environment. As industries integrate AdMas into commercial products to innovate in the next stage of the value chains, there is an urgent need for robust methodologies to detect, characterize, and assess their potential risks throughout their life cycle. The MACRAMÉ Project addresses this challenge by advancing standardized testing and regulatory frameworks, supporting the EU's vision for a toxic-free environment. Through cutting-edge research and international collaboration, MACRAMÉ lays the groundwork for reliable hazard assessment, regulatory compliance, and the responsible development of next-generation materials. The MACRAMÉ Project aims to enhance the detection, characterization, and quantification of Advanced Materials (AdMas) throughout their life cycle, assessing potential human and environmental health impacts during exposure. By developing, demonstrating, and standardizing advanced methodologies, MACRAMÉ ensures their broad applicability across market-relevant AdMas-containing products. Fully aligned with EU strategies such as the Chemical Strategy for Sustainability and the European Green Deal, the project extends nanosafety approaches to the broader AdMas category, focusing on inhalable carbon-based materials - graphene-related materials, carbon nanofibers, and poly lactic-co-glycolic acid nanoparticles. Building on over 15 years of research, MACRAMÉ integrates knowledge from major European and international initiatives to establish harmonized test guidelines, guidance documents, and standards. Through five industrial Use-Cases, the project applies innovative sample preparation, detection, and toxicity assessment techniques to develop a tiered approach for AdMa safety testing. Centralized in the MACRAMÉ Information Hub, all data will support regulatory frameworks and future research. The project's outcomes - harmonization and pre-standardization proposals - will contribute to a unified European assessment framework, reinforcing the continent's leadership in safe and sustainable materials innovation.

The continuous technological development of electronic devices and the introduction of new materials leads to ever greater demands on the fabrication of semiconductor heterostructures and their characterization. This work focuses on optimizing Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) depth profiles of semiconductor heterostructures aiming at a minimization of measurement-induced profile broadening. As model system, a state-of-the-art Molecular Beam Epitaxy (MBE) grown multilayer homostructure consisting of ^natSi/^28Si bilayers with only 2 nm in thickness is investigated while varying the most relevant sputter parameters. Atomic concentration-depth profiles are determined and an error function based description model is used to quantify layer thicknesses as well as profile broadening. The optimization process leads to an excellent resolution of the multilayer homostructure. The results of this optimization guide to a ToF-SIMS analysis of another MBE grown heterostructure consisting of a strained and highly purified ^28Si layer sandwiched between two Si_0.7Ge_0.3 layers. The sandwiched ^28Si layer represents a quantum well that has proven to be an excellent host for the implementation of electron-spin qubits.

We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the identification of peptide sample TOF-SIMS spectra by machine learning. More than 1000 time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of six peptide model samples (one of them was a test sample) were collected using 27 TOF-SIMS instruments from 25 institutes of six countries, the U. S., the U. K., Germany, China, South Korea, and Japan. Because peptides have systematic and simple chemical structures, they were selected as model samples. The intensity of peaks in every TOF-SIMS spectrum was extracted using the same peak list and normalized to the total ion count. The spectra of the test peptide sample were predicted by Random Forest with 20 amino acid labels. The accuracy of the prediction for the test spectra was 0.88. Although the prediction of an unknown peptide was not perfect, it was shown that all of the amino acids in an unknown peptide can be determined by Random Forest prediction and the TOF-SIMS spectra. Moreover, the prediction of peptides, which are included in the training spectra, was almost perfect. Random Forest also suggests specific fragment ions from an amino acid residue Q, whose fragment ions detected by TOF-SIMS have not been reported, in the important features. This study indicated that the analysis using Random Forest, which enables translation of the mathematical relationships to chemical relationships, and the multi labels representing monomer chemical structures, is useful to predict the TOF-SIMS spectra of an unknown peptide.

The minimum information requirements needed to guarantee high-quality surface analysis data of nanomaterials are described with the aim to provide reliable and traceable information about size, shape, elemental composition and surface chemistry for risk assessment approaches. The widespread surface analysis methods electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) were considered. The complete analysis sequence from sample preparation, over measurements, to data analysis and data format for reporting and archiving is outlined. All selected methods are used in surface analysis since many years so that many aspects of the analysis (including (meta)data formats) are already standardized. As a practical analysis use case, two coated TiO2 reference nanoparticulate samples, which are available on the Joint Research Centre (JRC) repository, were selected. The added value of the complementary analysis is highlighted based on the minimum information requirements, which are well-defined for the analysis methods selected. The present paper is supposed to serve primarily as a source of understanding of the high standardization level already available for the high-quality data in surface analysis of nanomaterials as reliable input for the nanosafety community.

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) is a powerful tool for surface analysis, but fragmentation of molecular species during the SIMS process may lead to complex mass spectra. While the fragmentation pattern is typically characteristic for each compound, industrial samples are engineered materials, and, thus, may contain a mixture of many compounds, which may result in a variety of overlapping peak patterns in ToF‐SIMS spectra. Consequently, the process of data evaluation is challenging and time‐consuming. Principal component analysis (PCA) can be used to simplify data analysis for complex sample systems. Especially, correlation loadings were observed as an ideal tool to identify relevant signals in PCA results, which induce the separation of different sample groups. This is because correlation loadings show the relevance of signals independent from their intensity in the raw data. In correlation loadings, however, fragmentation patterns are no longer observed and the identification of peaks' sum formulas is challenging. In this study, a new approach is presented, which simplifies peak identification and assignment in ToF‐SIMS spectra after PCA is performed. The approach uses a mathematical transformation that projects PCA results, in particular loadings and correlation loadings, in the direction of specific sample groups. The approach does not change PCA results but rather presents them in a new way. This method allows to visualize characteristic spectra for specific sample groups that contain only relevant signals and, additionally, visualize fragmentation patterns. Data analysis is simplified and helps the user to focus on data interpretation rather than processing.

Amorphous silica nanoparticles comprise a class of widely used industrial nanomaterials, which may elicit acute inflammation in the lung. These materials have a large specific surface to which components of the pulmonary micro-milieu can bind. To conduct appropriate binding studies, paramagnetic Fe2O3/SiO2 core/shell nanoparticles (Fe-Si-NP) may be used as an easy-to-isolate silica surrogate, if several prerequisites are fulfilled. To this end, we investigated the distribution of Fe, Si, protein and phosphatidylcholine (PC) by Time-of-Flight secondary ion mass spectrometry (ToF-SIMS) in cryo-sections from the rat lungs to which Fe-Si-NP had been administered for 30 min. Regions-of-interest were identified and analyzed with incident light and enhanced dark-field microscopy (DFM). Fe-Si-NP particles (primary particle size by electron microscopy: 10–20 nm; aggregate size by tracking analysis: 190 ± 20 nm) and agglomerates thereof were mainly attached to alveolar walls and only marginally internalized by cells such as alveolar macrophages. The localization of Fe-Si-NP by DFM was confirmed by ToF-SIMS signals from both, Fe and Si ions. With respect to an optimized signal-to-noise ratio, Fe+, Si+, CH4N+ and the PC head group (C5H15NO4P+) were the most versatile ions to detect iron, silica, protein, and PC, respectively. Largely congruent Fe+ and Si+ signals demonstrated that the silica coating of Fe-Si-NP remained stable under the conditions of the lung. PC, as a major lipid of the pulmonary surfactant, was colocalized with the protein signal alongside alveolar septa, but was not detected on Fe-Si-NP, suggesting that silica nanoparticles do not adsorb lipids of the lung surfactant under native conditions. The study shows that ToF-SIMS is a valuable technique with adequate spatial resolution to analyze nanoparticles together with organic molecules in the lung. The paramagnetic Fe-Si-NP appear well suited to study the binding of proteins to silica nanomaterials in the lung.

The increasing use of nanoparticles (NP) in commercial products requires elaborated techniques to detect NP in the tissue of exposed organisms. However, due to the low amount of material, the detection and exact localization of NP within tissue sections is demanding. In this respect, Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and Ion Beam Microscopy (IBM) are promising techniques, because they both offer sub-micron lateral resolutions along with high sensitivities. Here, we compare the performance of the non-material-consumptive IBM and material-consumptive ToF-SIMS for the detection of ZrO2 NP (primary size 9–10 nm) in rat lung tissue. Unfixed or methanol-fixed air-dried cryo-sections were subjected to IBM using proton beam scanning or to three-dimensional ToF-SIMS (3D ToF-SIMS) using either oxygen or argon gas cluster ion beams for complete sample sputtering. Some sample sites were analyzed first by IBM and subsequently by 3D ToF-SIMS, to compare results from exactly the same site. Both techniques revealed that ZrO2 NP particles occurred mostly agglomerated in phagocytic cells with only small quantities being associated to the lung epithelium, with Zr, S, and P colocalized within the same biological structures. However, while IBM provided quantitative information on element distribution, 3D ToF-SIMS delivered a higher lateral resolution and a lower limit of detection under these conditions. We, therefore, conclude that 3D ToF-SIMS, although not yet a quantitative technique, is a highly valuable tool for the detection of NP in biological tissue.

The description of nanoparticle distributions in tissue and associated effects is an important goal of nanotoxicology.

In this paper, an improved approach to interpret results of principal component analysis (PCA) of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra is presented. Signals are typically observed in different intensity ranges in a single ToF-SIMS spectrum due to different sensitivity factors and surface concentrations. This can complicate the PCA interpretation, because loadings are reported to be strongly affected by these intensity changes. In contrast, it is shown here that correlation loadings are unaffected by these differences. In particular, correlation loadings were successfully used to identify signals with relatively low intensity but high significance. These signals may be overlooked when only loadings are used. This is particularly true in failure analysis, where ToF-SIMS is used to screen for initially unknown signals that may be relevant for the characteristics/failure of a product. As a model study, the concept was applied to investigate ageing of Li-ion batteries by ToF-SIMS. In this data set, the significance of impurities that affect the quality of Li-ion batteries was identified only by correlation loadings, whereas the loadings were found to overestimate the influence of other matrix signals. In addition, correlation loadings aid in the chemical identification and helped to successfully assign unknown peaks.

The direct detection of nanoparticles in tissues at high spatial resolution is a current goal in nanotoxicology. Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is widely used for the direct detection of inorganic and organic substances with high spatial resolution but its capability to detect nanoparticles in tissue sections is still insufficiently explored. To estimate the applicability of this technique for nanotoxicological questions, comparative studies with established techniques on the detection of nanoparticles can offer additional insights. Here, we compare ToF-SIMS imaging data with sub-micrometer spatial resolution to fluorescence microscopy imaging data to explore the usefulness of ToF-SIMS for the detection of nanoparticles in tissues. SiO2 nanoparticles with a mean diameter of 25 nm, core-labelled with fluorescein isothiocyanate, were intratracheally instilled into rat lungs. Subsequently, imaging of lung cryosections was performed with ToF-SIMS and fluorescence microscopy. Nanoparticles were successfully detected with ToF-SIMS in 3D microanalysis mode based on the lateral distribution of SiO3- (m/z 75.96), which was co-localized with the distribution pattern that was obtained from nanoparticle fluorescence. In addition, the lateral distribution of protein (CN-, m/z 26.00) and phosphate based signals (PO3-, m/z 78.96) originating from the tissue material could be related to the SiO3- lateral distribution. In conclusion, ToF-SIMS is suitable to directly detect and laterally resolve SiO2 nanomaterials in biological tissue at sufficient intensity levels. At the same time, information about the chemical environment of the nanoparticles in the lung tissue sections is obtained.

The development of cluster primary ion sources such as Aun+, Bin+, SF5+, C60+, and Arn+ has been an exciting advancement in SIMS analysis. Relative to atomic primary ion sources, cluster ion sources provide higher secondary ion yields. Furthermore, C60+ and Arn+ impart significantly less chemical damage to the sample thus enabling molecular depth profiling. Molecular depth profiling using cluster primary ion sources is routinely used to characterize a wide range of commercially important materials, including organic light emitting diode, biomaterials and pharmaceuticals, adhesives, and architectural paints and coatings. This paper highlights the application of time of flight secondary ion mass spectrometry (ToF-SIMS) to study contact lenses and acrylic-based paints. In the first application, ToF-SIMS was used to investigate the surface composition of two commercial contact lenses. Lens material I is composed of 2-hydroxy-ethyl methacrylate (HEMA) and glycerol methacrylate while lens material II is composed of HEMA and 2-methacryloxyethyl phosphorylcholine cross-linked with ethyleneglycol dimethacrylate. The ToF-SIMS data confirm the presence of the 2-methacryloxyethyl phosphorylcholine on the surface of lens material II. ToF-SIMS was also used to characterize a HEMA-based contact lens which had been worn for about 4 weeks. The analysis reveals the presence of N-containing species, fatty acids, phosphorylcholine, and dioctyldecyl dimethyl ammonium. Arn+ gas cluster ion beams (GCIB) depth profiling indicates the N-containing species, the fatty acids, and the dioctyldecyl dimethyl ammonium are concentrated at the surface. In the second application, a combination of O2+ and Arn+ GCIB depth profiling was used to study the pigment levels in acrylic-based paints. The O2+ beam was used to profile into the bulk of the dried paint film and Arn+ gas cluster beam was then used to remove the damaged material. ToF-SIMS analysis of the crater bottom reveals differences in pigment levels. The combined O2+ and Arn+ GCIB depth profiling is an effective way of characterizing materials composed of both organic and inorganic components.

Forderungen nach höherer Geschwindigkeit und die Komplexität der Fragen geben der analytischen Chemie neue Impulse. So nutzt die Prozessanalytik gepulste Ramanspektrometer, die Elemente Arsen und Quecksilber dominieren die Forschung in der Speziesanalytik, und die Omics-Techniken entwickeln sich zu Multi-Omics-Ansätzen. Ambiente MS-Techniken benötigen keine aufwendige Probenvorbereitung, multidimensionale Trenntechniken werden verstärkt in der Routine eingesetzt, und Chip-basierte Trennungen fallen durch Schnelligkeit auf. Molekülspektroskopie und Massenspektrometrie dominieren die bildgebenden Verfahren, und die Lateralauflösung der ToF-Sekundärionenmassenspektrometrie hat sich bei Oberflächenuntersuchungen verbessert.

This paper presents a case study for the application of multivariate data analysis (MVA) to time-of-flight secondary ion mass spectrometry (ToF-SIMS) data from sample sets of mainly unknown surface composition. Aged lithium-ion battery (LIB) anodes were used as the test sample set due to their relatively complex composition. For example, LIB samples typically contain a large variety of different and often unidentified degradation products that complicate manual data processing. In this work, principal component analysis (PCA) was applied as a first step to find and classify relevant but unknown peaks in the ToF-SIMS mass spectra. As a result, peak identification was simplified in such a way that the chemical nature of 76% of the characteristic but previously unknown peaks was successfully identified. In a second step, multivariate curve resolution (MCR) was applied to depth profiles of the battery anodes for the first time, and a layered structure of the model samples was successfully determined. This approach also provided an efficient way to compare the layers' structure and the thickness across different samples. In addition to MCR, PCA was used on ToF-SIMS data to investigate all of the layer compositions of the complete sample set simultaneously. It is demonstrated that ToF-SIMS data from rarely characterized data sets can be processed successfully using MVA methods even if a priori knowledge of the sample sets is very limited. With respect to the test samples, the combination ToF-SIMS and MVA proved to be an attractive method to study the influence of different additives (vinylene carbonate, fluoroethylene carbonate, and ethylene sulfite) that appeared in the mass spectra, and was therefore helpful in understanding the formation of different degradation products in LiPF6-containing battery anodes.

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