Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Microplastic reference material is required for validation and harmonization purposes. This includes the analysis of the pure polymer particles and the validation of the respective measurement methods. In addition, there is the harmonization of different laboratories. On the other hand, sample preparation methods must be validated and harmonized with regard to their recovery and subsequent analysis in the respective matrices. For this purpose, we produce reference material candidates in the form of tablets with different mass fractions and particle numbers of polyethelene terephthalate. In the presentation first measurement results by µ-Raman, µ-FTIR, pyrolysis-GC-MS and TED-GC/MS will be presented.
The presentation gives an overview of BAM's activities on processing influences and application properties of MPEAs in the form of joined and machined high and medium entropy alloys (CoCrFeMnNi and CoCrNi). In the case of welding, the focus is on defect-free welded joints with sufficient mechanical properties. In the case of machining, the focus is on the possible influence on the surface quality of the materials through adequate milling parameters. In addition, the hydrogen absorption and diffusion properties as well as the electrochemical corrosion behavior are fundamentally examined.
Der Vortrag gibt einen Überblick zu ultrakurzen Laserpulsen, der Ultrakurzpulslaser-Materialbearbeitung und der dabei ggf. auftretenden unerwünschten Röntgenstrahlung. Es wird die Laserbearbeitung von technischen und biologischen Materialien diskutiert. Schutzmöglichkeiten gegenüber der Röntgenstrahlung werden erörtert.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based). This ranges from the automation of simple heuristics (oxidation states, coordination environments) to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities. For example, we can now easily make data-driven tests of well-known rules and heuristics or develop quantum chemistry-based materials descriptors for machine learning approaches. These tests and descriptors can have applications related to magnetic ground state predictions of materials relevant for spintronic applications or for predicting thermal properties relevant for thermal management in electronics. Combining high-throughput ab initio computations with fitting, fine-tuning machine learning models and predictions of such models within complex workflows is also possible and promises further acceleration in the field. In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science.
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.