Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Iron aluminides, already reported in the late 19th century, did not cease to attract the interest of scientists and engineers ever since. Besides good oxidation resistance, low density and resource availability, potentials for hightemperature strengths that compete with high-alloy steels were unlocked by low alloy contents. Still, research on alloy design continues, as alloying usually comes at the price of brittleness in low-temperature regimes. A potential candidate is the quinary Fe–Al–Mo–Ti–B system which is strengthened by solid solution and eutectic borides. It was shown to have good strength and outstanding creep resistance under compressive loading up to elevated temperatures. Although the individual effect of alloy additions is well understood in iron aluminides, little is known about the combined effects of alloying concentrations on microstructure, phase stability and mechanical properties. Therefore a systematic study of two Ti-doped near-Fe3Al alloys with varying contents of Mo (2–4 at.%) and B (0.5–1 at.%) was conducted. In total eight different alloys were fabricated by investment casting into ceramic shell molds. Alloys were characterized and compared by grain size, phase transitions, microstructure evolution as well as elemental compositions and volume fractions of phases. For mechanical characterization, macrohardness and microhardness tests as well as tensile tests at ambient and high tempera tures were conducted. Independent of alloy additions, alloys with 24–25 at.% Al exhibit superior proof strength due to a higher matrix hardness. Decreasing B content generally decreases strength by lower secondary phase fractions which contribute via particle hardening. Reducing Mo content decreases both the solute concentration in the matrix and secondary phase fractions. Surprisingly, strength is similar or even superior to alloys with higher Mo content. Strength relations are discussed with a focus on solid-solution hardening theory and other competing strengthening mechanisms.
The increasing importance of resource availability and closed-loop material cycles are driving materials research to reduce alloying content in conventional materials or even substitute them with more sustainable alternatives. Intermetallic iron aluminide alloys (FeAl) present a potential alternative. Many alloy concepts for improved high-temperature properties or ductility have already been successfully implemented in casting technologies on a laboratory scale. However, successful testing of FeAl alloys on an industrial scale was still pending at the beginning of the project.
Therefore, the aim of the project was to develop simulation-based casting concepts for industrial casting processes using the base alloy Fe-26Al-4Mo-0.5Ti-1B and to narrow down process limits by means of hot cracking tests. Findings were transferred into practice-oriented guidelines for casting of iron aluminides, which is accessible to future applicants in SMEs. The focus was placed on centrifugal casting combined with investment casting or die casting. In addition to numerous design and casting process parameters, heat treatments and alloying additions (Al, Mo, B) were varied to determine the influence of alloying elements on castability, microstructure and mechanical properties. Data from microstructure analyses (microscopic imaging, determination of grain sizes as well as phase compositions and volume fractions, fractography), mechanical tests (hardness measurements, compression tests, ambient and high-temperature tensile tests, creep tests) as well as measurements of thermophysical properties could be generated on the base alloy. Correlations of materials data with process variables allowed conclusions to be drawn on strengthening mechanisms and ductility of the alloy and how they can be controlled in terms of processing and component design. Successful casting of highly complex components with thin wall thicknesses and optimised alloy compositions points out prospects for new fields of application.
The increasing importance of resource availability and closed-loop material cycles are driving materials research to reduce alloying content in conventional materials or even substitute them with more sustainable alternatives. Intermetallic iron aluminide alloys (FeAl) present a potential alternative. Many alloy concepts for improved high-temperature properties or ductility have already been successfully implemented in casting technologies on a laboratory scale. However, successful testing of FeAl alloys on an industrial scale was still pending at the beginning of the project.
Therefore, the aim of the project was to develop simulation based casting concepts for industrial casting processes using the base alloy Fe-26Al-4Mo-0.5Ti-1B and to narrow down process limits by means of hot cracking tests. Findings were transferred into practice-oriented guidelines for casting of iron aluminides, which is accessible to future applicants in SMEs. The focus was placed on centrifugal casting combined with investment casting or die casting. In addition to numerous design and casting process parameters, heat treatments and alloying additions (Al, Mo, B) were varied to determine the influence of alloying elements on castability, microstructure and mechanical properties. Data from microstructure analyses (microscopic imaging, determination of grain sizes as well as phase compositions and volume fractions, fractography), mechanical tests (hardness measurements, compression tests, ambient and high-temperature tensile tests, creep tests) as well as measurements of thermophysical properties could be generated on the base alloy. Correlations of materials data with process variables allowed conclusions to be drawn on strengthening mechanisms and ductility of the alloy and how they can be controlled in terms of processing and component design. Successful casting of highly complex components with thin wall thicknesses and optimised alloy compositions points out prospects for new fields of application.
Iron Oxide Nanocubes as a New Certified Reference Material for Nanoparticle Size Measurements
(2023)
The rational design and increasing industrial use of nanomaterials require a reliable characterization of their physicochemical key properties like size, size distribution, shape, and surface chemistry. This calls for nanoscale reference materials (nanoRMs) for the validation and standardization of commonly used characterization methods closely matching real-world nonspherical nano-objects. This encouraged us to develop a nonspherical nanoRM of very small size consisting of 8 nm iron oxide nanocubes (BAM-N012) to complement spherical gold, silica, and polymer nanoRMs. In the following, the development and production of this nanoRM are highlighted including the characterization by transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS) as complementary methods for size and shape parameters, homogeneity and stability studies, and calculation of a complete uncertainty budget of the size features. The determination of the nanocubes’ edge length by TEM and SAXS allows a method comparison. In addition, SAXS measurements can also provide the mean particle number density and the mass concentration. The certified size parameters, area equivalent circular diameter and square edge length, determined by TEM with a relative expanded uncertainty below 9%, are metrologically traceable to a natural constant for length, the very precisely known (111) lattice spacing of silicon. Cubic BAM-N012 qualifies as a certified nanoRM for estimating the precision and trueness, validation, and quality assurance of particle size and shape measurements with electron microscopy and SAXS as well as other sizing methods suitable for nanomaterials. The production of this new iron oxide nanocube RM presents an important achievement for the nanomaterial community, nanomaterial manufacturers, and regulators.
Tracking waterborne microplastic (MP) in urban areas is a challenging task because of the various sources and transport pathways involved. Since MP occurs in low concentrations in most wastewater and stormwater streams, large sample volumes need to be captured, prepared, and carefully analyzed. The recent research in urban areas focused mainly on MP emissions at wastewater treatment plants (WWTPs), as obvious entry points into receiving waters. However, important transport pathways under wet-weather conditions are yet not been investigated thoroughly. In addition, the lack of comprehensive and comparable sampling strategies complicated the attempts for a deeper understanding of occurrence and sources. The goal of this paper is to (i) introduce and describe sampling strategies for MP at different locations in a municipal catchment area under dry and wet-weather conditions, (ii) quantify MP emissions from the entire catchment and two other smaller ones within the bigger catchment, and (iii) compare the emissions under dry and wet-weather conditions. WWTP has a high removal rate of MP (>96%), with an estimated emission rate of 189 kg/a or 0.94 g/[population equivalents (PEQ · a)], and polyethylene (PE) as the most abundant MP. The specific dry-weather emissions at a subcatchment were ≈30 g/(PEQ · a) higher than in the influent of WWTP with 23 g/(PEQ · a). Specific wet-weather emissions from large sub-catchment with higher traffic and population densities were 1952 g/(ha · a) higher than the emissions from smaller catchment (796 g/[ha · a]) with less population and traffic. The results suggest that wet-weather transport pathways are likely responsible for 2–4 times more MP emissions into receiving waters compared to dry-weather ones due to tire abrasion entered from streets through gullies. However, more investigations of wet-weather MP need to be carried out considering additional catchment attributes and storm event characteristics.
A technical-safety evaluation of the detonation effects of pyrotechnic compositions can be performed on the basis of TNT/PETN equivalence. The equivalence determination can be carried out by characterization of the blast wave generated because of detonation in free field tests, which however can be highly resource intensive and prone to uncertainties. Here, we present underwater ‘small-scale’ experiments for the determination of such equivalents.
Underwater experiments, as described in the European standard EN 13763-15:2004, are performed to test the capability of detonators to initiate secondary explosives by determining the released energy. At BAM this test was modified to compare the energy output of the pyrotechnic mixtures (those used in air bag gas generators and firework flash compositions) and thus to determine their equivalents of high explosives like TNT or PETN. In the modified tests, small cylindrical copper containers were filled with pyrotechnic substances, which were then attached to standard detonators. This explosive charge assembly was then lowered into a water tank of about 1000 l capacity. At the same depth as the charge assembly, a piezoelectric pressure sensor was immersed in the water at a horizontal distance of about 400 mm from the charge. By recording the time-dependent pressure during the test, the shock energy as well as the energy associated with the expanding gas bubble were determined.
The embrittlement of metallic alloys by liquid metals leads to catastrophic material failure and severely impacts their structural integrity. The weakening of grain boundaries by the ingress of liquid metal and preceding segregation in the solid are thought to promote early fracture. However, the potential of balancing between the segregation of cohesion-enhancing interstitial solutes and embrittling elements inducing grain boundary decohesion is not understood. Here, we unveil the mechanisms of how boron segregation mitigates the detrimental effects of the prime embrittler, zinc, in a Σ5 [0 0 1] tilt grain boundary in α −Fe (4 at.% Al). Zinc forms nanoscale segregation patterns inducing structurally and compositionally complex grain boundary states. Ab-initio simulations reveal that boron hinders zinc segregation and compensates for the zinc induced loss in grain boundary cohesion. Our work sheds new light on how interstitial solutes intimately modify grain boundaries, thereby opening pathways to use them as dopants for preventing disastrous material failure.
The complex nature of liquid water saturation of polymer electrolyte fuel cell (PEFC) catalyst layers (CLs) greatly affects the device performance. To investigate this problem, we present a method to quantify the presence of liquid water in a PEFC CL using small-angle X-ray scattering (SAXS). This method leverages the differences in electron densities between the solid catalyst matrix and the liquid water filled pores of the CL under both dry and wet conditions. This approach is validated using ex situ wetting experiments, which aid the study of the transient saturation of a CL in a flow cell configuration in situ. The azimuthally integrated scattering data are fitted using 3D morphology models of the CL under dry conditions. Different wetting scenarios are realized in silico, and the corresponding SAXS data are numerically simulated by a direct 3D Fourier transformation. The simulated SAXS profiles of the different wetting scenarios are used to interpret the measured SAXS data which allows the derivation of the most probable wetting mechanism within a flow cell electrode.
This presentation summarizes outcomes of the CUSP projects POLYRISK and PlasticsFatE according to thermoanalytical methods.It highlights the importants of analytical methods to measure physical and chemical properties of the test materials. These informations are neccessary for interpretation of the toxicological test results.
Material Selection Strategy
(2023)
Preliminary results of an interlaboratory comparison on microplastics organised by plasticsfate
(2023)
Microplastics are everywhere in the environment, but analytics is challenging. Since harmonisation is missing as well es suitable reference materials, BAM did under th umbrella of VAMAS funded by the EU Horizon 2020 project PlasticsFate a ILC for microplastic detection methods. Methods adressed were IR, Raman, Py-GC/MS and TED-GC/MS. The talk gives a first presentation and evaluation on the results.
In recent years, thermoextraction/desorption-gas chromatography/mass spectrometry (TED-GC/MS) has been developed as a rapid detection method for the determination of microplastics (MP) mass contents in numerous environmentally relevant matrices and, in particular, for the measurement of polymers in water samples without time-consuming sample preparation. The TED-GC/MS method was applied to investigate a typical European municipal wastewater system for possible MP masses. Such investigations are important in view of the recent revision of the Urban Wastewater Treatment Directive. Four different representative sampling sites were selected: greywater (domestic wastewater without toilet), combined sewer, and influent and effluent of a wastewater treatment plant (WWTP). All samples were collected by fractional filtration. Filtration was carried out over mesh sizes of 500, 100, 50, and in some cases, 5 µm. Polyethylene (PE), polypropylene (PP), and polystyrene (PS) were detected in all samples, with the PE fraction dominating in all cases. Styrene-butadiene rubber which serves as an indication of tire abrasion, was only found in the influent of the WWTP. The highest MP mass contents were found in the combined sewer, so MP can become a source of pollution during heavy rain events when the capacity limits of the effluent are reached, and the polluted effluent is released uncontrolled into the environment. Based on the studies, MP retention from the WWTP could be estimated to be approximately 96%. Few trends in polymer type or mass contents were detected within the different fractions of the samples or when comparing samples to each other.
In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model. This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat- FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed.
In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model.
This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat-FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed.