6.0 Abteilungsleitung und andere
Filtern
Erscheinungsjahr
- 2023 (71) (entfernen)
Dokumenttyp
- Vortrag (23)
- Zeitschriftenartikel (19)
- Posterpräsentation (14)
- Forschungsdatensatz (13)
- Video (1)
- Sonstiges (1)
Schlagworte
- Automation (21)
- Bonding analysis (13)
- Database (13)
- Materials Informatics (13)
- Computational Chemistry (12)
- DFT (8)
- Mechanochemistry (8)
- Phonons (7)
- Machine learning (5)
- In situ (4)
Organisationseinheit der BAM
- 6.0 Abteilungsleitung und andere (71) (entfernen)
Paper des Monats
- ja (1)
Eingeladener Vortrag
- nein (23)
This paper reports on the effect of the solvent viscosity on the formation of gold nanoparticles (Au NPs) during Sputtering onto Liquid (SoL) process. All other parameters related to the plasma and the host liquid are kept constant. SoL is a simple highly reproducible approach for preparation of colloidal dispersions of small naked NPs. The properties of the final product are determined by both the sputtering parameters and the host liquid characteristics. As a model system we chose to sputter a gold target by a direct-current magnetron discharge onto a line of polymerized rapeseed oils having similar surface tension (32.6 ― 33.1 mJ·m-2 at RT). It was found that well dispersed Au NPs grow in the bulk solution of oils with low viscosities (below 630 cP at 25 °C) while gold films form onto the surface of high viscosity liquids (more than 1000 cP at 25 °C). The mean diameter of the individual Au NPs slightly increases with oil viscosity and is in range about 2.1―2.5 nm according to transmission electron microscopy.
The talk gives a short overview and explains basic principles of research ethics for PhD students. We examine the most important principles in the codes of conduct for good scientific practice on EU and national level. Based on these general rules, we give some hands-on practical tips for daily scientific work and discuss in detail one of the fields most susceptible to conflict, the topic of authorship in scientific publications.
To date, there are very few technologies available for the conversion of low-temperature waste heat into electricity. Thermomagnetic generators are one approach proposed more than a century ago. Such devices are based on a cyclic change of magnetization with temperature. This switches a magnetic flux and, according to Faraday’s law, induces a voltage.
Here we give an overview on our research, covering both materials and systems. We demonstrate that guiding the magnetic flux with an appropriate topology of the magnetic circuit improves the performance of thermomagnetic generators by orders of magnitude. Through a combination of experiments and simulations, we show that a pretzel-like topology results in a sign reversal of the magnetic flux. This avoids the drawbacks of previous designs, namely, magnetic stray fields, hysteresis and complex geometries of the thermomagnetic material.
Though magnetocaloric materials had been the first choice also for thermomagnetic generators, they require some different properties, which we illustrate with Ashby plots for materials selection. Experimentally we compare La-Fe-Co-Si and Gd plates in the same thermomagnetic generator. Furthermore, we discuss corrosion and deterioration under cyclic use is a severe problem occurring during operation. To amend this, composite plates using polymer as a matrix have been suggested previously.
Mechanochemistry has proven to be a highly effective method for the synthesis of organic compounds. We studied the kinetics of the catalyst-free Knoevenagel reaction between 4-nitrobenzaldehyde and malononitrile, activated and driven by ball milling. The reaction was investigated in the absence of solvents (neat grinding) and in the presence of solvents with different polarities (liquid-assisted grinding). The reaction was monitored using time-resolved in situ Raman spectroscopy and powder X-ray diffraction (PXRD). Our results indicate a direct relationship between solvent polarity and reaction kinetics, with higher solvent polarity leading to faster product (2-(4-nitrobenzylidone)malononitrile) formation. For the first time, we were able to isolate and determine the structure of an intermediate 2-(hydroxy(4-nitrophenyl)methyl)malononitrile based on PXRD data.
Proton exchange membrane fuel cells (PEMFCs) are one of the most promising alternative green energy technologies that deliver high energy density without CO2 emissions. The proton conductivity of proton exchange membranes (PEM) contributes to the overall efficiency of a PEMFC. Materials being used as PEMs must exhibit high proton conductivity at the working conditions of the targeted PEMFC. To date, Nafion and Nafion-like polymers with acidic functionality are widely used as membrane materials due to their high proton conductivity in the range of 10-1 to 10-2 Scm-1 at higher relative humidity. However, these materials suffer from high costs, hazardous production process, and poor performance at high temperatures, limiting their versatility.
In this context, crystalline porous materials are recognized as promising proton conductors for the proton exchange membrane (PEM) in fuel cell technology, owing to their tunable framework structure. However, it is still challenging bulk synthesis for real-world applications of these materials. Herein, we present mechanochemical gram-scale synthesis of series of mixed ligand metal organic frameworks (MOFs) and metal hydrogen‐bonded organic frameworks (MHOFs) using phenylene diphosphonic acid and 1-hydroxyethylidene-1,1-diphosphonic acid with different bipyridyl type of ligands, respectively. In all cases, the existence of extensive hydrogen bonds with amphiprotic uncoordinated phosphonate hydroxyl and oxygen atoms, the frameworks exhibited high proton conductivity. The study demonstrates the potential of green mechanosynthesis for preparations of framework-based proton conducting materials in bulk scale for green energy generation.
Vibrational properties play a key role in determining the stability and thermal conductivity behaviour of materials. The quasi- harmonic approximation gives insight into the phononic properties of a compound, but in the established way, i.e. density functional theory based methods, it takes many calculation steps and consumes a lot of resources to arrive at the desired results. Machine learning (ML) trained interatomic potentials (e.g. Gaussian approximation potential, GAP) pose an alternative to the traditional computation way of phonons. We develop a Python code based workflow which combines automation tools like atomate2 with ML to ease providing interactomic potentials for (quantum chemical) computations and databases.
Chemical bonding and coordination environments are crucial descriptors of material properties. They have previously been applied to creating chemical design guidelines and chemical heuristics. They are currently being used as features in machine learning more and more frequently. I will discuss implementations and algorithms (ChemEnv and LobsterEnv) for identifying these coordination environments based on geometrical characteristics and chemical bond quantum chemical analysis. I will demonstrate how these techniques helped in testing chemical heuristics like the Pauling rule and thereby improved our understanding of chemistry. I will also show how these tools can be used to create new design guidelines and a new understanding of chemistry. To use quantum-chemical bonding analysis on a large-scale and for machine-learning approaches, fully automatic workflows and analysis tools have been developed. After presenting the capabilities of these tools, I will also point out how these developments relate to the general trend towards automation in the field of density functional based materials science.