Genchev, G.; Cox, K.; Sarfraz, A.; Bosch, C.; Spiegel, M.; Erbe, A.: Sour corrosion – Investigation of iron sulfide layer growth in saturated H2S solutions. In: Proceedings of the European Corrosion Congress EUROCORR. European Corrosion Congress EUROCORR 2014, Pisa, Italy, September 08, 2014 - September 12, 2014. (2014)
Genchev, G.; Cox, K.; Sarfraz, A.; Bosch, C.; Spiegel, M.; Erbe, A.: Sour corrosion – Investigation of anodic iron sulfide layer growth in saturated H2S saline solutions. Gordon Research Conference-Aqueous Corrosion, New London, NH, USA (2014)
Genchev, G.; Cox, K.; Sarfraz, A.; Bosch, C.; Spiegel, M.; Erbe, A.: Sour corrosion – Investigation of anodic iron sulfide layer growth in saturated H2S saline solutions. Gordon Research Seminar-Aqueous Corrosion, New London, NH, USA (2014)
Cox, K.: Elektrochemische Untersuchung von Eisen im Schwefelwasserstoff gesättigten Elektrolyten. Bachelor, Faculty of Chemistry, Niederrhein University of Applied Sciences (Hochschule Niederrhein), Krefeld, Germany (2013)
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
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…