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Eingeladener Vortrag
- ja (57) (entfernen)
In this lecture, the safety assessment of hydrogen gas storage systems is presented using the example of composite pressure vessels. The main element is a probabilistic approval approach based on five steps. Firstly, the testing and evaluation of properties at the beginning of life are shown. Secondly, methods for artificial aging and the effect on the residual strength are presented. Thirdly, testing against dedicated accidents is introduced. Fourthly, effects on the surveillance of production quality are discussed. Finally, degradation and the end of life are estimated. Background information and examples are given for each step. The assessment presented is a method applicable for many safety-related systems.
The variability and heterogeneity in their structural composition is significant for nearly all synthetic and natural polymers. Beside molar mass also functionality type distribution, copolymer composition distribution and architectural features are responsible for changing the material properties.
A wide range of analytical techniques are available to get insight into these parameters. However, in most cases it is essential to combine different sophisticated techniques to get closer to actual structures and to avoid statistical averages.
Different liquid chromatographic separation modes and their coupling in a two-dimensional way (SEC, LCCC, HILIC, 2D-LC) in combination with suitable detection techniques (e.g. UV, RI, LS, IR, MALDI/ESI-MS) as well as techniques like BET, DCS, SEM/TEM were applied to make structural features visible.
A few aspects are discussed for structural different polylactides from various synthesis routes and their behavior in LC-MS.
In this lecture, the safety assessment of hydrogen gas storage systems is presented using the example of composite pressure vessels. The main element is a probabilistic approval approach based on five steps. Firstly, the testing and evaluation of properties at the beginning of life are shown. Secondly, methods for artificial aging and the effect on the residual strength are presented. Thirdly, testing against dedicated accidents is introduced. Fourthly, effects on the surveillance of production quality are discussed. Finally, degradation and the end of life are estimated. Background information and examples are given for each step. The assessment presented is a method applicable for many safety-related systems.
Manipulating ceramic powder compacts and ceramic suspensions (slurries) within their volume with light requires a minimum transparency of the materials. Compared to polymers and metals, ceramic materials are unique as they offer a wide electronic band gap and thus a wide optical window of transparency. The optical window typically ranges from below 0.3 µm up to 5µm wavelength. Hence, to penetrate with laser light into the volume of a ceramic powder compound its light scattering properties need to be investigated and tailored. In the present study we introduce the physical background and material development strategies to apply two-photon-polymerization (2PP) for the additive manufacture of filigree structures within the volume of ceramic slurries.
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.
The presentation will start with an overview of ceramic additive manufacturing (AM) technologies and will discuss the potential of AM in the field of bioceramics.
The presentation will then focus on two possible use cases of binder jetting technologies.
In the first example, standard powder-based binder jetting is used to manufacture a porous implant design for large scale bone defects. The related challenges in the process chain will be discussed, from powder synthesis to sintering and characterization of the printed part.
In the second example, the LSD-print slurry-based binder jetting technology is presented as a possibility to adapt powder-bed AM to produce dense ceramic parts. The use case will focus on an application in the field of dental ceramics, specifically for the manufacturing of patient individualized single tooth restorations (veneers, crowns) with a high throughput process chain.
The variability and heterogeneity in their structural composition is significant for nearly all synthetic and natural polymers. Beside molar mass also functionality type distribution, copolymer composition distribution and architectural features are responsible for changing the material properties.
A wide range of analytical techniques are available to get insight into these parameters. However, in most cases it is essential to combine different sophisticated techniques to get closer to actual structures and to avoid statistical averages.
Different liquid chromatographic separation modes and their coupling in a two-dimensional way (SEC, LCCC, HILIC, 2D-LC) in combination with suitable detection techniques (e.g. UV, RI, LS, IR, MALDI/ESI-MS) as well as techniques like BET, DCS, SEM/TEM were applied to make structural features visible.
A few aspects are discussed for structural different polylactides from various synthesis routes and their behavior in LC-MS. Moreover, some analytical results for technical lignins valorized by mechanochemical treatment or modified by functional polymers are presented.
Glow discharge optical emission spectroscopy (GD-OES) is a technique for the analysis of solids such as metals, semiconductors, and ceramics. A low-pressure glow discharge plasma is applied in this system, which ‘sputters’ and promotes the sample atoms to a higher energy state. When the atoms return to their ground state, they emit light with characteristic wavelengths, which a spectrometer can detect. Thus, GD-OES combines the advantages of ICP-OES with solid sampling techniques, which enables it to determine the bulk elemental composition and depth profiles. However, direct solid sampling methods such as glow-discharge spectroscopy require reference materials for calibration due to the strong matrix effect.
Reference materials are essential when the accuracy and reliability of measurement results need to be guaranteed to generate confidence in the analysis. These materials are frequently used to determine measurement uncertainty, validate methods, suitability testing, and quality assurance. In addition, they guarantee that measurement results can be compared to recognized reference values. Unfortunately, the availability of certified reference materials suited to calibrate all elements in different matrix materials is limited. Therefore various calibration strategies and the preparation of traceable matrix-matched calibration standards will be discussed.
Machine learning is an essential component of the growing field of data science. Through statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. Therefore, it was tried in our work to combine GD-OES with machine learning strategies to establish a new and robust calibration model, which can be used to identify the elemental composition and concentration of metals from a single spectrum. For this purpose, copper reference materials from different manufacturers, which contain various impurity elements, were investigated using GD-OES. The obtained spectra information are evaluated with different algorithms (e.g., gradient boosting and artificial neural networks), and the results are compared and discussed in detail.
Driven by recent technological advances and the need for improved viral diagnostic applications, mass spectrometry-based proteomics comes into play for detecting viral pathogens accurately and efficiently. However, the lack of specific algorithms and software tools presents a major bottleneck for analyzing data from host-virus samples. For example, accurate species- and strain-level classification of a priori unidentified organisms remains a very challenging task in the setting of large search databases. Another prominent issue is that many existing solutions suffer from the protein inference issue, aggravated because many homologous proteins are present across multiple species. One of the contributing factors is that existing bioinformatic algorithms have been developed mainly for single-species proteomics applications for model organisms or human samples. In addition, a statistically sound framework was lacking to accurately assign peptide identifications to viral taxa. In this presentation, an overview is given on current bioinformatics developments that aim to overcome the above-mentioned issues using algorithmic and statistical methods. The presented methods and software tools aim to provide tailored solutions for both discovery-driven and targeted proteomics for viral diagnostics and taxonomic sample profiling. Furthermore, an outlook is provided on how the bioinformatic developments might serve as a generic toolbox, which can be transferred to other research questions, such as metaproteomics for profiling microbiomes and identifying bacterial pathogens.