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Paper des Monats
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There is no applicable existing standard for the determination of safety characteristics for hybrid mixtures. While developing a new standard in a joint research project in Germany first results from parameter studies led to a standard procedure that can be adopted by laboratories that are already testing dusts in the so called 20L-sphere with as little additional effort as necessary. In fact, one of the main objectives of this research project was to keep modifications and adjustments from the generally accepted dust testing procedures as easy and minimal as possible so as to limit potential deviations from one laboratory to another.
In this first round robin test on hybrid mixtures ever, with methane as gas component and a specific corn starch as dust sample, the practicality of the whole procedure, the scattering of the results and the deviation between the testing apparatuses is investigated. This paper summarizes the experimental procedure adopted and objectives of the first round-robin phase involving three of the four original German companies, plus volunteering laboratories from Australia, Belgium, Czech Republic, France, Poland and P.R. China. The results will have an impact on the new standard and may lead to robust data for later simulation purposes.
AbstractThis work addresses the critical need for multifunctional materials and substrate‐independent high‐precision surface modification techniques that are essential for advancing microdevices and sensing elements. To overcome existing limitations, the versatility of mussel‐inspired materials (MIMs) is combined with state‐of‐the‐art multiphoton direct laser writing (DLW) microfabrication. In this way, 2D and 3D MIM microstructures of complex designs are demonstrated with sub‐micron to micron resolution and extensive post‐functionalization capabilities. This study includes polydopamine (PDA), mussel‐inspired linear, and dendritic polyglycerols (MI‐lPG and MI‐dPG), allowing their direct microstructure on the substrate of choice with the option to tailor the patterned topography and morphology in a controllable manner. The functionality potential of MIMs is demonstrated by successfully immobilizing and detecting single‐stranded DNA on MIM micropattern and nanoarray surfaces. In addition, easy modification of MIM microstructure with silver nanoparticles without the need of any reducing agent is shown. The methodology developed here enables the integration of MIMs in advanced applications where precise surface functionalization is essential.
Quaternary ammonium compounds (QACs) are widely used as active agents in disinfectants, antiseptics, and preservatives. Despite being in use since the 1940s, there remain multiple open questions regarding their detailed mode-of-action and the mechanisms, including phenotypic heterogeneity, that can make bacteria less susceptible to QACs. To facilitate studies on resistance mechanisms towards QACs, we synthesized a fluorescent quaternary ammonium compound, namely N-dodecyl-N,N-dimethyl-[2-[(4-nitro-2,1,3-benzoxadiazol-7-yl)amino]ethyl]azanium-iodide (NBD-DDA). NBD-DDA is readily detected by flow cytometry and fluorescence microscopy with standard GFP/FITC-settings, making it suitable for molecular and single-cell studies. As a proof-of-concept, NBD-DDA was then used to investigate resistance mechanisms which can be heterogeneous among individual bacterial cells. Our results reveal that the antimicrobial activity of NBD-DDA against Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa is comparable to that of benzalkonium chloride (BAC), a widely used QAC, and benzyl-dimethyl-dodecylammonium chloride (BAC12), a mono-constituent BAC with alkyl-chain length of 12 and high structural similarity to NBD-DDA. Characteristic time-kill kinetics and increased tolerance of a BAC tolerant E. coli strain against NBD-DDA suggest that the mode of action of NBD-DDA is similar to that of BAC. As revealed by confocal laser scanning microscopy (CLSM), NBD-DDA is preferentially localized to the cell envelope of E. coli, which is a primary target of BAC and other QACs. Leveraging these findings and NBD-DDA‘s fluorescent properties, we show that reduced cellular accumulation is responsible for the evolved BAC tolerance in the BAC tolerant E. coli strain and that NBD-DDA is subject to efflux mediated by TolC. Overall, NBD-DDA’s antimicrobial activity, its fluorescent properties, and its ease of detection render it a powerful tool to study resistance mechanisms of QACs in bacteria and highlight its potential to gain detailed insights into its mode-of-action.
The most common method for determination of the carbonation depth is the sprayed application of indicator solutions, Phenolphthalein in particular. Since the aerosols of Phenolphthalein are suspected to be carcinogenic, efforts have been intensified to use alternative indicators for the determination of carbonation depth. The subject and aim of this research paper are to examine indicator solutions from Alizarin Yellow R, Thymol Blue and a Mixed Indicator. These indicator solutions are commercially available and are not suspected to be carcinogenic.
For the purpose of this research, samples previously stored under accelerated carbonation conditions (1% CO2) were examined by thermogravimetric analysis (TGA) and examination of the pH value. Additionally, the electrolytic back-titration for determination of total CO2 amount was carried out comparatively to TGA.
Lithium-ion batteries are a key technology to achieve the goals of limiting climate change due to the important role as traction technology for Electric Vehicles and in stationary energy storage systems. Over(dis) charge, mechanical damage due to accidents or thermal abuse such as fires can initiate an accelerated self-heating process of the batteries, called thermal runaway. A thermal runaway can propagate from cell to cell within a larger assembly of cells such as modules or battery packs and can cause rapid heat and toxic gas emissions. The resulting battery fire can spread to adjacent facilities, e.g. other cars in underground car parks or to a whole building in case of a large stationary energy storage.
For proof of fire protection requirements or to design suitable fire protection systems, Computational Fluid Dynamic (CFD) simulations are getting more and more important. The aim of CFD fire simulations is to predict the global hazards of a fire to its surroundings, that is mainly characterized by the release of heat and smoke and its spread in the fire environment. There are many numerical investigations of lithium-ion batteries in the literature. One class of models is used to simulate the charge and discharge process of lithium-ion batteries and to predict the temperature or voltage evolution inside the battery. On the other hand, there are models describing batteries under abuse conditions to predict the consequences of a thermal runaway event to the local environment, like the temperatures inside a battery or at the battery surface. Henriksen et al. use a generic battery gas mixture to simulate an explosion of vented gases from a Lithium Iron Phosphate battery and compare experimental results for the explosion pressure and the position of the flame front to the outcomes of a simulation with Xifoam. Larsson et al. used a combination of CFD simulations with FDS and thermal model with COMSOL to predict the temperature development of neighboring cells in a thermal runaway propagation. Truchot et al. use a design Heat Release Rate (HRR) curve for a battery based on experimental measurements to build up an overall HRR curve for a truck loaded with 100 lithium-ion batteries. This summed up HRR and corresponding smoke production curve is then used as an input for a simulation of a truck fire in a tunnel with Fire Dynamics Simulator (FDS). The pre-definition of the HRR curve is a frequently used method in fire engineering. It has the disadvantage, that the heat release cannot be influenced by physical processes, such as changed ventilation conditions or extinguishing measures. In this paper, a model is presented that determines the release of heat and gases based on the thermal runaway mechanisms of the battery, which can be used in CFD fire simulations with focus on prediction of fire hazards to nearby environment.
Aqueous Dispersions of Polypropylene: Toward Reference Materials for Characterizing Nanoplastics
(2023)
Microplastics and nanoplastics pollute the natural environment all over the world, but the full extent of the hazards posed by this waste is unclear. While research on microplastics is well advanced, little work has been done on nanoplastics. This discrepancy is mainly due to the lacking ability to detect nanoplastics in biologically and environmentally relevant matrices. Nanoplastics reference materials can help the development of suitable methods for identifying and quantifying nanoplastics in nature. The aim is to synthesize nanoplastics made from one of the most commonly used plastics, namely polypropylene. An easy way to produce long-term stable aqueous dispersions of polypropylene nanoparticles (nano polypropylene) is reported. The nanoplastic particles, prepared by mechanical breakdown, show a mean hydrodynamic diameter of D h = 180.5 ± 5.8 nm and a polydispersity index of PDI = 0.084 ± 0.02. No surfactant is needed to obtain dispersion which is stable for more than 6 months. The colloidal stability of the surfactant-free nano polypropylene dispersions is explained by their low zeta potential of 𝜻 = −43 ± 2 mV.
Efficient water electrolysis requires highly active electrodes. The activity of corresponding catalytic coatings strongly depends on material properties such as film thickness, crystallinity, electrical conductivity, and chemical surface speciation. Measuring these properties with high accuracy in vacuum-free and nondestructive methods facilitates the elucidation of structure−activity relationships in realistic environments. Here, we report a novel approach to analyze the optical and electrical properties of highly active oxygen evolution reaction (OER) catalysts via spectroscopic ellipsometry (SE). Using a series of differently calcined, mesoporous, templated iridium oxide films as an example, we assess the film thickness, porosity, electrical resistivity, electron concentration, electron mobility, and interband and intraband transition energies by modeling of the optical spectra. Independently performed analyses using scanning electron microscopy, energy-dispersive X-ray spectroscopy, ellipsometric porosimetry, X-ray reflectometry, and absorption spectroscopy indicate a high accuracy of the deduced material properties. A comparison of the derived analytical data from SE, resonant photoemission spectroscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy with activity measurements of the OER suggests that the intrinsic activity of iridium oxides scales with a shift of the Ir 5d t2g sub-level and an increase of p−d interband transition energies caused by a transition of μ1-OH to μ3-O species.
Various test methods are available for assessing the susceptibility of materials to solidification cracking during welding. In the widely used Varestraint test, the crack length is selected as a criterion as a function of the applied bending strain.
Unfortunately, the crack length does not characterize the material behavior alone but depends to varying degrees on the individual test parameters used, which makes the interpretation of the results difficult. In addition, the crack length is not comparable under different test conditions. To overcome these disadvantages, we have developed a novel evaluation methodology that decouples the machine influence from the material behavior. The measured crack length is related to the maximum possible value specified by welding speed and deformation time. This relative crack length is calculated numerically, considering the orientation of the cracks. Experiments on two high-alloy martensitic welding consumables show that, in contrast to the conventional evaluation, a comparison of different welding parameters becomes possible. Furthermore, the strain rate proved to be a suitable crack criterion in agreement with Prokhorov's hot cracking model.
Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future ofmonitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results.
Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.