معرفی
Univ.-Prof. Dr.-Ing. Michael Kersten is a Professor of Hydrogeochemistry at the Institute of Geosciences, Johannes Gutenberg University Mainz since 1997. His academic career includes a Dipl.-Ing. in Mineralogy and Materials Science from TU Darmstadt (1982), followed by a Dr.-Ing. from TUHH in 1988 and habilitation in 1996. Kersten's research focuses on hydrogeochemistry, environmental geochemistry, and mineralogical processes, particularly examining contaminant transport, mineral solubility, and pore-scale modeling in geological systems. He leads the Hydrogeochemistry group and serves as Editor-in-Chief of Applied Geochemistry.
Education and Career Highlights:
- 1982: Dipl.-Ing. (Mineralogy and Materials Science, TU Darmstadt)
- 1983–1992: Research Assistant at TUHH under Prof. Förstner
- 1988: Dr.-Ing. from TUHH
- 1989–1992: Postdoc, TUHH
- 1993–1994: Postdoc at EAWAG/ETH Zurich
- 1995–1997: Group Leader at Baltic Sea Research Institute
- 1996: Habilitation (Dr.-Ing. habil.) in Geochemistry, TUHH
- 1997–present: Full Professor (C3) at JGU Mainz
Research Interests: Professor Kersten’s work spans hydrogeochemistry, environmental geochemistry, and pore-scale modeling. His studies address mineral solubility, contaminant transport mechanisms, and the impacts of human activities on groundwater systems. Key areas include:
- Geochemical modeling of mineral-water interactions
- Adsorption processes on mineral surfaces (e.g., goethite, apatite)
- Pore-scale imaging and digital rock physics
- Environmental impacts of mining and industrial activities
Advising & Collaborations: Leading the Hydrogeochemistry team, Kersten collaborates on projects involving digital rock physics and environmental geochemistry. His work often integrates experimental and computational approaches to address real-world challenges in groundwater management and resource exploration.
Labs & Facilities: Active in the Institute of Geosciences’ laboratories, particularly in advanced microtomography and geochemical analysis facilities. His team utilizes synchrotron-based imaging and machine learning techniques for rock property characterization.
