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Enhanced Fe and Ni bonded NbC Laser Surface Engineered based Hardmetals: Alternative Cutter Materials for Electric Vehicle Applications
No full textThe efforts to substitute both tungsten carbide (WC) and cobalt (Co) has gained prominence in recent years due to the classification of Co as a carcinogen and the classification of Co and W as critical raw materials in the EU as well as within regulations of the U.S. National Toxicology Program. In this study, substitution of both WC and Co with advanced hardmetals consisting of NbC with Ni and Fe-based metal binders are investigated for their use of machining of metals used electric vehicle manufacturing. The developed NbC-Ni/Fe based hardmetals employ a Machining Property Led Tailored Design (MPLTD) approach. This reverse engineering strategy uses data from machining performance to guide the development of microstructural, mechanical, and behavioral properties. Four advanced NbC-based hardmetals were produced, two with Ni-based binders and two with Fe-based binders, along with two reference materials for comparison (WC-Co and straight NbC-12Ni). Hardmetals were characterized using field emission scanning electron microscopy (FE-SEM), annular dark-field scanning transmission electron microscopy (ADF-STEM), Vickers hardness, fracture toughness, and elastic moduli. Cutting tool inserts were manufactured from the developed hardmetals and enhanced using femto-second laser surface engineering. The inserts’ performance was evaluated through face milling tests on AZ31 automotive magnesium alloy, providing insights into their suitability for high-demand industrial applications
Automated In-situ Monitoring and Analysis of Process Signatures and Build Profile During Arc-based Directed Energy Deposition
No full textAutomated in-situ synchronous monitoring and analysis of key process signatures during arc-based directed energy deposition (DED) process are the key challenges for layer-by-layer printing of large-scale parts. An attempt is presented here for real-time monitoring of process transients, deposit profile, and quantitative assessment of arc power, energy input and its influence on deposit dimensions. The workflow including setup, job generation and data analysis is fully automated in Python to allow large scale experiments with fast analysis results
Roughness of selected friction plates and its influence on friction sensitivity measurements of explosives
No full textRoughness of selected friction plates from different sources and their influence on the measurement of the friction sensitivity of explosives were investigated. This test plays a decisive role for the work of notified bodies in the context of conformity assessment of explosives and pyrotechnics as well as transport classification, and differences were observed that should be taken into account in the work
New Approaches to PFAS Quali- and Quantification using GC-MS
No full textKnown as “forever chemicals”, per- and polyfluoroalkyl substances (PFAS) are a class of synthetically produced chemicals that includes an estimated 10.000 compounds. Due to the persistence, toxicity and ubiquitous occurrence, research has focused on the qualification and quantification of the most important compounds as well as on the investigation of toxicity and possible routes of entry over the last 10 years. Liquid chromatography - mass spectrometry (LC-MS) is the analytical standard to test for PFAS, as the spectrum of detectable compounds is significantly more comprehensive than it is currently the case with gas chromatography - mass spectrometry (GC-MS). However, to be able to test for PFAS contamination in a more process-independent manner and to make the analysis more widely accessible, methods based on GC-MS are currently being developed. Since GC-MS methods only cover a fraction of the compounds belonging to the PFAS group so far, further development of the corresponding measurement methods is inevitable [1, 2].
The here described work is part of the EU project 23IND13 ScreenFood [3]. The aim is to develop sensitive analytical GC-MS methods that contribute to an improved identification and quantification of various PFAS (both currently regulated and emerging PFAS) in selected food and food packaging matrices. Of particular interest as a food contact material are native and recycled polymers such as PET. Besides, various techniques, including solvent-free variants such as thermal desorption GC-MS, will be tested for a quick and easy analysis. Multiple derivatization approaches, which cover different PFAS subgroups, will also be tested and evaluated. This poster will present the overall project objectives and first results
Nano- And Advanced Materials Synthesis In A Self-driving Lab (SDL)
No full textDevelopment of new nano- and advanced materials - or improvement of existing ones - are important drivers in materials research due to the high importance of these material classes for various applications. Traditional laboratory methods for material development often suffer from reproducibility issues, inefficiencies, human errors, and long experimental optimization times. To overcome these challenges and thus accelerate and optimize the process of material synthesis and discovery, we are building a Self-Driving Lab (SDL), in which we integrate robotics for autonomous nanomaterial synthesis, and automated characterization and data analysis for a complete and reliable nanomaterial synthesis workflow. We also leverage artificial intelligence (AI) and machine learning (ML) algorithms to analyze characterization results and plan new experiments to optimize material properties in an ML-guided active learning feedback loop.
Our SDL is very agnostic towards the types of nano- and advanced materials it can synthesize. On the same SDL platform, we successfully synthesized Stober silica, mesoporous silica, copper-oxide, and gold nanoparticles, as well as metal-organic frameworks and more complex structures from multi-step reactions, such as Au@SiO2 and CuO@SiO2 core-shell nanoparticles. All these material syntheses showed excellent reproducibility when run on the SDL platform multiple times.
Automated, in-line characterization measurements of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence) of the nanomaterials have also been incorporated in the SDL, along with automating data analysis of at-line or off-line characterization techniques such as electron microscopy image analyses [1]. Incorporating these characterization results alongside a machine learning feedback loop that suggests new experimental parameters for obtaining materials with target properties is a key step for developing autonomous, closed-loop optimization processes. In such a process, we typically start by using random sampling to suggest initial experimental parameters, followed by ML-guided active learning algorithms such as Bayesian optimization, artificial neural networks, or downhill simplex optimizers (e.g., Nelder-Mead) that suggest new synthesis parameters to finally arrive at a material with the targeted or enhanced properties. Further improvement and optimization of our SDL has the potential to mitigate challenges faced by traditional approaches and open a way for rapid and reproducible nano- and advanced material synthesis, optimization, and discovery
Quantifying ergot alkaloids in food using stable isotopically labeled standards
No full textErgot alkaloids (EAs) are toxic secondary metabolites produced by fungi of the genus Claviceps. They grow on rye and wheat, and are introduced into the food chain through the harvest of infected cereals. Therefore, the European Union has established a maximum level for the 12 most abundant EAs. In order to improve the quantification, stable isotopically labelled EAs were synthesized for the first time and their performance was evaluated in comparison to the current European standardmethod in different foodstuff
Nanoparticle Synthesis by Precursor Irradiation with Low-Energy Electrons
No full textNanoparticles (NPs) and their fabrication routes are intensely studied for their wide range of application in optics, chemistry, and medicine. Γ-ray and ion irradiation of precursor matter are established methods that facilitate tailored NP synthesis without complicated chemistry. Here, we develop and explore NP synthesis based on irradiating precursor microparticles with low-energy electron beams. We specifically demonstrate the fabrication of plasmonic gold nanoparticles of sizes between 3 and 350 nm on an amorphous SiOx substrate using a 30 kV electron beam. By detailed comparison with electron scattering simulations and thermodynamic modeling, we reveal the dominant role of inelastic electron–matter interaction and subsequent localized heating for the observed vaporization of the precursor gold microparticles. This general principle suggests the suitability of electron-beam irradiation for synthesizing NPs of a wide class of materials
Multifunctional hybrid microparticles for rapid cytometric and microfluidic mix-&-detect bioassays
No full textBesides the application-oriented parameters selectivity and sensitivity, simplicity and speed as well as robustness and reliability are the key features that determine success, dissemination and acceptance of analytical assays and methods for use in the field. For bioassays based on antibodies or nucleic acid binders, bead-based assays have become one of the main working horses of lab-based methods. However, these approaches are often limited by complicated and time-consuming workflows, which are mainly compensated by massive parallelisation, keeping the average cost and time-to-result within acceptable limits due to the sheer number and constant flow of samples processed. In on-site applications, where analyses need to be performed on suspicion or on demand because results are needed as quickly as possible for proactive decision making, the number, frequency and type of samples are much more diverse, so analytical assays need to be adapted to remain cost-effective and operable by non-expert users in environments where no lab infrastructure is available. With this in mind, we have developed a multifunctional hybrid microparticle platform that enables fast and simple mix-&-detect bioassays in combination with fluidic cytometry- and chip-based analytical methods.
The present contribution will give an overview of the beads, their functions and how they can be combined with immunoanalytical and nucleic acid-based detection technologies. Starting from simple polymeric core/silica shell particles [1], we have progressively introduced coding [2,3], anti-fouling [2], high-surface area [4] and magnetic schemes [5] and in recent years have shown how these beads can be used in simple mix-&-detect assays for a range of different environmental, health and food analytes [1-5]. In addition to cytometry, we have also successfully implemented such assays using miniaturised microfluidic and strip-based approaches [6]
Abschlussbericht zum FGS Vorhaben "Gefügeveränderungen bei trockener Grobbearbeitung"
No full textDer Vortrag stellt die Ergebnisse des Forschungsvorhabens zum Thema Gefügeveränderungen bei trockener Grobbearbeitung vor. Ziel des Vorhabens war die Identifizierung und Quantifizierung möglicher mechanisch- oder thermomechanisch-bedingter Gefügeveränderungen nach trockener Oberflächenbearbeitung. Es wurden opfische und metallografische Untersuchungen sowie Härtemessungen und elektrochemische Korrosionsuntersuchungen durchgeführt
Primary References for the Determination of Sulphur Impurities in Hydrogen
No full textFuel cell electric vehicles are expanding quickly from light-duty to heavy-duty applications, such as buses or trucks. Hydrogen fuel quality needs to comply with ISO 14687:2025 to avoid any harmful impact on the vehicles. Total sulphur is one of the most impactful contaminants to a fuel cell system and has a threshold of 4 nmol/mol. In the European Partnership for Metrology (EPM) project Met4H2, BAM together with VSL, the National Metrology Institute of the Netherlands, developed novel gaseous primary reference materials (PRM) to improve the accuracy of the analysis of 7 sulphur compounds (hydrogen sulphide, carbonyl sulphide, methyl mercaptan, ethyl mercaptan, dimethyl sulphide, diethyl sulphide, and tetrahydrothiophene) for the quality control of hydrogen as fuel gas at this challenging amount fraction. These PRM were cross-validated using thermal desorption gas chromatography with a sulphur chemiluminescence detector (TD-GC/SCD). The results are presented and limits discussed