Current Research


SEM image with superposed EBSD measurement of a pearlitic steel cross-section after wear on the microscale by a nanoindenter. The wear particle is heavily deformed.

Friction and wear occurs in a variety of conventional and modern applications ranging from conventional and electric cars to the contact of rail-wheels and tracks. During contact, the deformation of the contact pair depends on the microstructural elements of the materials, on the environment and the lubricant.
Anti-wear lubricants have in the past relied on sulfur-based components that ensured a glassy layer on the moving metals. However, the negative environmental impact of these sulfur-components has lead to the development of new lubricants that still form resilient coatings on the contact pair without the need for sulfur.
The microstructure of the contact materials greatly influences wear and friction. Hard materials have a high deformation resistance but are less compliant to a change in loading-condition and are more likely to fail in a brittle and catastrophic manner. Soft materials easily adopt to the change in geometry and loading and those materials exhibit tough failure; on the other hand they often result in a reduction of the beneficial surface pores and seizure of the contact. We aim to understand the fundamental deformation mechanisms to balance the material between the hard and ductile extreme conditions.

Effects of green hydrogen on microstructures

Nanoindentation curves for polymers that are used in electrolyzer cells and corresponding hardness and Young’s modulus values. The properties of the polymer depend greatly on the humidity and the process parameters.

We investigate the material properties of different materials and thereby support the development of different materials that contribute to the change to a green and hydrogen-supported energy mixture.
On the one hand, fuel and electrolyzers cells are required as energy conversion devices. Naturally the requirements for these devices depend on the application itself. The nanoparticle thin-film based cells depend on a large set of parameters which influence the efficiency and integrity of the respective cells. The conventional evaluation of the cells requires significant time- and financial costs for each individual parameter set, which makes the direct efficiency and lifetime evaluation as a function of all process parameters impossible. We use helper indicators and artificial intelligence to significantly reduce the process parameter space in nanoparticle coatings and cell assembly. To this end, we evaluate samples by using conventional and advanced (nanoindentation and nanocompression) high-throughput tests to produce a large set of helper indicators which enter the machine learning algorithm.
On the other hand, we aim to improve the understanding of metals that are used in gas-grids of different scale and in valves. To this end, we develop novel experimental setups to focus the deformation and the evaluation on specific microstructural elements in the metal. By understanding the building blocks of the material, we can identify beneficial and detrimental elements and we can guide the development of future hydrogen resistant metals.

Electronic lab-notebooks

Screenshot from the Google Play store that shows the PASTA-ELN cellphone client and the information from the central materials science database.

Currently, most experimental researchers employ paper notebooks that are cumbersome to search and store and it is difficult to retrieve information to thoroughly comprehend the metadata of each experiment. Therefore, Electronic lab-notebooks (ELNs) are developed that aim to eliminate these short-comings and allow for a sustainable science that prevents repeating measurements because some measurement details are unknown. On the other hand, these ELNs have to be efficient when it comes to data accumulation as the manual data entry is not sustainable.
We develop PASTA-ELN that combines raw-data with rich metadata to allow advanced data science for experimental scientics. In this software framework, the scientist can fully adapt and improve the metadata definitions to generate novel workflows. PASTA uses a local-first approach: store all data and metadata locally (always accessible to user) and synchronize with a server upon the user request. Most importantly, PASTA heavily relies on extractors that extract valuable metadata from the measurement files and supplements the database.
We are aware that electronic lab-notebooks and the workflows around them will change significantly in the next years as scientists use them and new scientific workflows develop. Therefore, we don’t only employ agile software development methods for PASTA-ELN, but the software developers are also experimental scientists that use the tool for their research and improve it.

Proprietary binary file formats

The workflow of deciphering proprietary binary files produces a python converter that can batch process vendor files into open files.

Raw data and metadata from scientific instruments are generally stored in proprietary binary files that are designed by the instrument vendor. These files differ depending not only on the particular vendor but also on the instrument generation. Typically, export functions in the proprietary software allow outputting raw data of low accuracy and omitting potentially valuable metadata. As such metadata remains hidden in the binary file and is not exported while primary data has a low quality.
The MARBLE project develops a software tool with graphical user interface that allows scientists to decipher metadata and raw data from proprietary binary files. Additionally, the software supports the scientists in creating converters that store data alongside metadata and thereby improve documentation and data provenance. Once these converters are created, they can be shared and used for batch processing proprietary files into open formats.
This project is executed in partnership with the IAS-9 of the FZJ and the HMC-Hub Information. This project includes also an educational aspects that teaches scientists about their rights when it comes to the data that they created.

Letzte Änderung: 09.11.2022