Uebel, M.; Exbrayat, L.; Rabe, M.; Tran, T. H.; Crespy, D.; Rohwerder, M.: On the Role of Trigger Signal Spreading Velocity for Efficient Self-Healing Coatings for Corrosion Protection. Journal of the Electrochemical Society 165 (16), pp. C1017 - C1027 (2018)
Dandapani, V.; Tran, T. H.; Bashir, A.; Evers, S.; Rohwerder, M.: Hydrogen Permeation as a Tool for Quantitative Characterization of Oxygen Reduction Kinetics at Buried Metal-Coating Interfaces. Electrochimica Acta 189, pp. 111 - 117 (2016)
Tran, T. H.; Gerlitzky, C.; Rohwerder, M.; Groche, P.: Which properties must a surface have to be suitable for cold pressure welding? 22nd International Conference on Material Forming (ESAFORM 2019), Mondragon Unibrtsitatae, Spain, May 08, 2019 - May 10, 2019. AIP Conference Proceedings 2113, 050019, (2019)
Uebel, M.; Tran, T. H.; Altin, A.; Gerlitzky, C.; Erbe, A.; Groche, P.: Which Properties Must a Surface have to be Suitable for Cold Pressure Welding? 22nd International Conference on Material Forming (ESAFORM 2019), Mondragon Unibrtsitatae, Spain (2019)
Rohwerder, M.; Tran, T. H.: Novel zinc-nanocontainer composite coatings for intelligent corrosion protection. 11th Intrenational Conference on Zinc And Zinc Alloy Coated Steel Sheet- GALVATECH 2017, The University of Tokyo, Tokyo, Japan (2017)
Uebel, M.; Vimalanandan, A.; Tran, T. H.; Rohwerder, M.: Coatings for intelligent self-healing of macroscopic defects: first results and the major challenges. eMRS, Symposium „Self-Healing Materials", Warsaw, Poland (2015)
Uebel, M.; Exbrayat, L.; Rabe, M.; Tran, T. H.; Crespy, D.; Rohwerder, M.: Role of Trigger Signal Spreading Velocity on Self-healing Capability of Intelligent Coatings for Corrosion Protection. Scientific Advisory Board Meeting 2019, 6-years Evaluation of the Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany (2019)
Vimalanandan, A.; Altin, A.; Tran, T. H.; Rohwerder, M.: Conducting Polymers for Corrosion Protection - Raspberry like shaped ICP “pigments”. Gordon Research Conference Corrosion-Aqueous, New London, NH, USA (2012)
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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…