Chemische Charakterisierung und Spurenanalytik
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In this contribution, I present a digital twin-based workflow designed to optimize experimental parameters in X-ray emission spectroscopy (XES), with a focus on reproducibility, data integration, and alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles in materials science and engineering (MSE). The developed pipeline begins with automated retrieval of crystallographic information from the Materials Project database based on a given sample composition. This structural data is then used to simulate the corresponding XES spectra using FDMNES, allowing for accurate prediction of element-specific emission lines.
The simulated emission lines are fed into an X-ray tracing (XRT) module, which builds a virtual replica of the experimental setup. This digital twin environment enables predictive modeling of spectrometer performance based on geometric configurations. The optimization focuses on two key parameters: the choice of the analyzing crystal and the distance between the sample and crystal (which also defines the crystal–detector distance due to Bragg condition constraints). An active learning algorithm is employed to iteratively adjust these parameters in order to achieve a desired energy-per-pixel (E/pixel) resolution with minimal intensity loss, enabling efficient, data-driven experimental planning.
By integrating data-driven simulations with real-time optimization strategies, this workflow supports efficient experiment planning while minimizing resource consumption and human error. Furthermore, all stages of the process—from data collection and simulation to optimization and visualization—are structured to ensure traceability and interoperability, facilitating future reuse and collaborative research.
I hope this contribution aligns well with the topic “Workflows for FAIR MSE Data” and offers a concrete example of how digital twins can be harnessed to improve the design, execution, and documentation of spectroscopy experiments in the MSE domain.
There is a growing interest in the exploitation of the short-wave infrared (SWIR), which refers to the wavelength band of light between 900 nm and 2500 nm. Luminophores that emit in the SWIR are used in various areas of telecommunications, photovoltaics, security systems (night vision), and in biomedicine. In particular for biomedical applications, the SWIR range is highly promising because light scattering, absorption, and autofluorescence of tissue and biological compounds are strongly reduced compared to the visible (400–700 nm) and NIR (~700–900 nm). The benefits of SWIR-emissive QDs have been demonstrated for a variety of applications, such as in thermal sensing, as photoelectrochemical biosensor, in in vivo vascular imaging, and for fluorescence-guided surgery.
Full exploitation of SWIR photoluminescence (PL) imaging and sensing is currently hampered by i.) a lack of suitable advanced nanomaterials with a high PL quantum yield (PL QY) and a high brightness, that can be used safely in vivo and ii.) a lack of quantitative and reliable data on the optical properties of many SWIR emitters. Promising nanomaterials for the SWIR are heavy metal-free Ag2S quantum dots (QDs).
Aiming for the development of SWIR advanced nanomaterials with optimum performance, we have dived deeper into the photophysical processes occurring in these nanomaterials, thereby exploring in depth how the environment such as temperature, surface ligand composition, and the incorporation of transition metals influence the optical properties Ag2S QDs. We observed a strong enhancement of the SWIR emission of upon addition of metal ions such as Zn2+, yielding PL quantum yields of about 10% and thus making them highly suitable for non-invasive deep imaging of vascular networks and 3D fluid flow mapping.
Within a project by the German Environmental Agency (Umweltbundesamt – UBA) soil samples are taken at different sites all over Germany. The concentration of 30 PFAS - 13 carboxylic acids (C4-C18), 5 sulfonic acids (C4-C10) and 12 precursor substances) - was determined in these soil samples using three different sample preparation approaches: 1) ultrasonic extraction of the soil samples with Methanol; 2) preparation of eluates to test the leaching behaviour of the PFAS and 3) TOP assays following the method published by Houtz & Sedlak. Targeted LC-MS/MS is used to determine PFAS concentration after all three preparation steps. For the TOP assay, the concentration of all 30 PFAS is compared prior to and after the oxidation reaction. Besides the ‘classic’ TOP assay, two other TOP assay approaches, the dTOP assay and the photoTOP assay, are tested and compared. First, all three TOP assay approaches will be tested on a reference soil spiked with a solution including all 30 PFAS measured in the project. Later, soil samples taken within the the priorly described project will were tested.
This work is financed by the UBA via the ReFoPlan2021 project, FKZ372171.
fioRa
(2025)
R package providing a wrapper for the python module 'FIORA' as well as a 'shiny'-App to facilitate data processing and visualization. 'FIORA' allows to predict Mass-Spectra based on the SMILES code of chemical compounds. It is described in the Nature Communications article by Nowatzky et al. (2025).
This study evaluates the performance of three reference equations of state (EoS), AGA8-DC92, GERG-2008, and SGERG-88, in predicting the density of regasified liquefied natural gas (RLNG) mixtures. A synthetic nine-component RLNG mixture was gravimetrically prepared. High-precision density measurements were obtained using a single-sinker magnetic suspension densimeter over a temperature range of (250 to 350) K and pressures up to 20 MPa. The experimental data were compared with EoS predictions to evaluate their accuracy. AGA8-DC92 and GERG-2008 showed excellent agreement with the experimental data, with deviations within their stated uncertainty. In contrast, SGERG-88 exhibited significantly larger deviations for this RLNG mixture, particularly at low temperatures of (250 to 260) K, where discrepancies reached up to 3 %. Even at 300 K, deviations larger than 0.4 % were observed at high pressures, within the model’s uncertainty, but notably higher than those of the other two EoSs. The analysis was extended to three conventional 11-component natural gas mixtures (labeled G420 NG, G431 NG, and G432 NG), previously studied by our group using the same methodology. While SGERG-88 showed reduced accuracy for the RLNG mixture, it performed reasonably well for these three mixtures, despite two of them have a very similar composition to the RLNG. This discrepancy is attributed to the lower CO2 and N2 content typical in RLNG mixtures, demonstrating the sensitivity of EoS performance to minor differences in composition. These findings highlight the importance of selecting appropriate EoS models for accurate density prediction in RLNG applications.
The presence of microplastics (MPs) in the food chain is increasingly documented, raising concerns over potential risks to human health. Despite growing efforts, standardized methods for MPs detection in food matrices remain limited. This study presents an interlaboratory comparison (ILC) aimed at assessing the accuracy and comparability of an analytical approach for the identification and quantification of small MPs (5–100 μm) in infant milk powder using μ-Raman spectroscopy and a representative polyethylene terephthalate (PET) reference material (RM). The RM, formulated as water-soluble tablets, was designed to replicate the morphology, size distribution, and polymer composition of environmentally relevant MPs, and was previously assessed for homogeneity and stability for mass fraction and particle numbers. The approach was assessed using two PET RM batches with different MPs particle numbers (high load batch: 1759 ± 141 MPs; low load batch: 160 ± 22 MPs), subjected to an enzymatic–chemical digestion, followed by μ-Raman analysis performed independently in two laboratories with different instruments and operators. Results are reported as absolute particle counts per analyzed sample and demonstrated excellent recovery across all size classes, including the smallest particles (down to 5 μm), with recovery rates ranging from 82 % to 88 %, in good agreement with the RM reference values. The analytical approach proved to be robust, reproducible, and suitable for low-level MPs quantification in complex food matrices, supporting ongoing efforts toward method harmonization and standardization for reliable MPs monitoring in the food sector.
Fibers of textile origin are named as high contributor for microplastic pollution in the environment next to e.g. plastics from packaging, tire wear and paints. Small fibers and fragments (microplastics: 1-1000 µm) can be released from textiles by mechanical abrasion during application (e.g. wearing) or washing. To date, the occurrence, transport pathways, and impacts of such small fibers on the environment and human health remain unclear.
The European Commission revised the Urban Wastewater Treatment Directive (Directive (EU) 2024/3019) and made monitoring of microplastics mandatory in the next years. Additionally, standards for microplastic detection are developed in the ISO standardization body ISO/TC 147/SC 2 with the documents ISO/DIS 16094-2 and ISO/DIS 16094-3. Detection mainly occurs by thermoanalytical methods for mass or vibrational methods with microscopes for numbers. Such standards and regulatory efforts need validated methods, which themselves need reference materials for validation.
Reference materials are well characterized materials that are produced for a specific purpose and are homogeneous and stable within one property of interest. The shape and the composition of the reference particles/fibers will affect the detection, hence it is important to validate the instruments with fibers, if this is the task for monitoring campaigns.
This work describes the production of realistic fibers as reference material for microplastic analysis. The individual samples contain of a small number (< 1000) and mass (< 20 µm) of fibres. Homogeneity and stability control occurred by thermogravimetric analysis and µ-Raman. Further characteristics were determined for type, shape and size distribution.
In den letzten 20 Jahren ist die Aufmerksamkeit vieler Forschender sowie der Politik und der Bürger selbst auf ein wachsendes Kunststoffproblem unserer Zeit stark gestiegen. Mangelnde Wertstoffkonzepte, Sammelstellen und unbedachtes Einbringen von Kunststoffprodukten in die Umwelt führen dort zu einer Anreicherung. Durch äußere Witterungseinflüsse können diese Produkte degradieren und fraktionieren, so dass heute in allen Teilen der Welt Mikroplastik (1-1000 µm, ISO/TR 21960:2020) unterschiedlicher Polymermaterialien nachgewiesen werden kann.
Bei Mikroplastik gilt das Vorsorgeprinzip. Die Partikel können weiter zerfallen in Nanoplastik (< 1 µm, ISO/TR 21960:2020). Ob eine toxikologische Gefährdung von Mikro- oder Nanoplastik ausgeht, wird vielfältig untersucht. Bisher stehen valide Ergebnisse aus. Untersuchungen zur Häufigkeit, dem Transport und möglicher Senken und Eintragspfade muss aber Rechenschafft getragen werden, so dass ein Monitoring von Mikroplastik bereits in der Revision der Trinkwasserrahmen-Richtlinie und der Abwasserrahmen-Richtlinie gefordert wird. Bedenklich ist auch die Verordnung über Mindestanforderungen an die Wasserwiederverwendung (2020/741), in welcher nicht verbindlich gesagt wird, Wasser welcher Reinigungsstufe eines Klärwerks auf die Felder zur Wiederverwendung aufgebracht werden darf.
Einer Regulierung über die EU-Plastikstrategie hinaus mit z.B. Verboten für Einwegplastik-Produkte und Rezyklingquoten kann nur entgegengewirkt werden, wenn valide Messmethoden, Referenzmaterialien und Standards existieren, die das tatsächliche Ausmaß von Mikroplastik-Konzentrationen in Luft, Wasser, Boden und Biota zuverlässig und genau aufzeigen.
This repository contains source data for training and testing the Fiora model (see GitHub and DOI: 10.5281/zenodo.14651774). As part of the Fiora study (Nowatzky et al., 2025), which introduces a GNN-based fragmentation algorithm for simulating MS/MS spectra, this dataset enables the reproduction of the study’s findings and the training of new spectral prediction models.
The use of supplementary cementitious materials (SCM) is an important part of the roadmap for reducing CO2 emissions and extending the service life of reinforced concrete structures. To accelerate the adoption of SCMs, the RILEM Technical Committee 298-EBD evaluates scaled-down cement paste test methods to assess the effect of SCM on resistance to chloride and sulfate ingress and reactivity, which are critical to concrete durability. This review focuses on methods for measuring chloride diffusivity and is divided into four sections: diffusivity models and parameters, diffusion test methods (including NMR and chloride measurements), migration test methods and implications for future research. Key insights highlight the complexities of multi-species ionic and molecular diffusion/migration, including various binding interactions, and compares the different measurement methodologies. The review also addresses the test scale and aggregate effects, noting the pros and cons of testing at the paste, mortar, and concrete scales. The review underscores the need for further investigation into testing protocols and the influence of SCM on chloride diffusion, emphasizing that comprehensive testing across different scales provides complementary information for assessing durability performance.