Ramana, E. V.; Durairajan, A.; Kavitha, D.; Tobaldi, D. M.; Zavašnik, J.; Bdikin, I.; Valente, M. A.: Enhanced magnetoelectric and energy storage performance of strain-modified PVDF-Ba0.7Ca0.3TiO3-Co0.6Zn0.4Fe2O4nanocomposites. Journal of energy storage 87, 111454 (2024)
Öcal, E. B.; Sajadifa, S. V.; Sellner, E. P. K.; Vollmer, M.; Heidarzadeh, A.; Zavašnik, J.; Niendorf, T.; Groche, P.: Functionally Graded AA7075 Components Produced via Hot Stamping: A Novel Process Design Inspired from Analysis of Microstructure and Mechanical Properties. Advanced Engineering Materials - Special Issue: Structural Materials 25 (15), 2201879 (2023)
Sajadifar, S. V.; Suckow, T.; Chandra, C. K.; Heider, B.; Heidarzadeh, A.; Zavašnik, J.; Reitz, R.; Oechsner, M.; Groche, P.; Niendorf, T.: Assessment of the impact of process parameters on the final material properties in forming of EN AW 7075 employing a simulated forming process. Journal of Manufacturing Processes 86, pp. 336 - 353 (2023)
Entezari, H.; Kashi, M. A.; Alikhanzadeh-Arani, S.; Montazer, A.H.; Zavašnik, J.: In situ precipitation synthesis of FeNi/ZnO nanocomposites with high microwave absorption properties. Materials Chemistry and Physics 266, 124508 (2021)
Žerjav, G.; Teržan, J.; Djinović, P.; Barbieriková, Z.; Hajdu, T.; Brezová, V.; Zavašnik, J.; Kovač, J.; Pintar, A.: TiO2–β–Bi2O3 junction as a leverage for the visible-light activity of TiO2 based catalyst used for environmental applications. Catalysis Today 361, pp. 165 - 175 (2021)
Djinović, P.; Zavašnik, J.; Teržan, J.; Jerman, I.: Role of CO2 During Oxidative Dehydrogenation of Propane Over Bulk and Activated-Carbon Supported Cerium and Vanadium Based Catalysts. Catalysis Letters 151 (10), pp. 2816 - 2832 (2021)
Taherzadeh Mousavian, R.; Zavašnik, J.; Heidarzadeh, A.; Bahramyan, M.; Vijayaraghavan, R. K.; McCarthy, É.; Clarkin, O. M.; McNally, P. J.; Brabazon, D.: Development of BMG-B2 nanocomposite structure in HAZ during laser surface processing of ZrCuNiAlTi bulk metallic glasses. Applied Surface Science 505, 144535 (2020)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.