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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…