Yin, Y.; Zhao, H.; Prabhakar, J. M.; Rohwerder, M.: Organic composite coatings containing mesoporous silica particles: Degradation of the SiO2 leading to self-healing of the delaminated interface. Corrosion Science 200, 110252 (2022)
Yin, Y.; Schulz, M.; Rohwerder, M.: Optimizing smart self-healing coatings: Investigating the transport of active agents from the coating towards the defect. Corrosion Science 190, 109661 (2021)
Yin, Y.: Self-heating coatings based on conducting polymer for intelligent corrosion protection. Dissertation, Ruhr-Universität Bochum, Fakultät für Maschinenbau (2022)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
Electron microscopes offer unique capabilities to probe materials with extremely high spatial resolution. Recent advancements in in situ platforms and electron detectors have opened novel pathways to explore local properties and the dynamic behaviour of materials.
In this ongoing project, we investigate spinodal fluctuations at crystal defects such as grain boundaries and dislocations in Fe-Mn alloys using atom probe tomography, electron microscopy and thermodynamic modeling [1,2].
Here, we aim to develop machine-learning enhanced atom probe tomography approaches to reveal chemical short/long-range order (S/LRO) in a series of metallic materials.