Kang, S. G.; Gainov, R.; Heußen, D.; Bieler, S.; Sun, Z.; Weinberg, K.; Dehm, G.; Ramachandramoorthy, R.: Green laser powder bed fusion based fabrication and rate-dependent mechanical properties of copper lattices. Materials and Design 231, 112023 (2023)
Bieler, S.; Kang, S. G.; Heußen, D.; Ramachandramoorthy, R.; Dehm, G.; Weinberg, K.: Investigation of copper lattice structures using a Split Hopkinson Pressure Bar. Proceedings of Applied Mathematics and Mechanics, Special Issue: 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM) 21 (1), e202100155, (2021)
Ramachandramoorthy, R.: High strain rate testing of copper based micropillars and microlattices. 206 Departmental Seminar Series, Empa, Thun, Switzerland (2021)
Ramachandramoorthy, R.: Pushing the limits of microscale manufacturing and mechanical testing. Department of Material Science and Engineering Seminar Series, Tel-Aviv University, online, Tel-Aviv, Israel (2021)
Ramachandramoorthy, R.: High strain rate testing from micro-to-meso scale. MRS Spring 2021 Conference - In Situ Mechanical Testing of Materials at Small Length Scales, Modeling and Data Analysis Symposium, online (2021)
Ramachandramoorthy, R.: High strain rate micromechanics: Instrumentation and implementation. DGM - Arbeitskreis Rasterkraftmikroskopie und nanomechanische Methoden, online (2020)
Bellón Lara, B.; Lu, W.; Fang, X.; Dehm, G.; Ramachandramoorthy, R.: Effect of Defects on the Dynamic Compression of Strontium Titanate Micropillars. ECI Nanomechanical Testing in Materials Research and Development IX, Sicily, Italy (2024)
Scientists at the Max Planck Institute for Sustainable Materials have developed a carbon-free, energy-saving method to extract nickel for batteries, magnets and stainless steel.
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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