About
Bezhan Chankvetadze is a Georgian chemist serving as Full Professor and Head of the Department of Physical and Analytical Chemistry at Tbilisi State University, where he also directs the Institute of Physical and Analytical Chemistry. His career spans multiple roles including academic leadership, industrial consulting at Phenomenex, and entrepreneurial ventures through Enantiosep GmbH. He serves as editor for the Journal of Pharmaceutical and Biomedical Sciences and maintains active collaborations with international research groups.
Education:
- Bachelor in Chemistry (1979), Tbilisi State University
- Master in Physical Chemistry (1979) with distinction
- PhD in Physical Chemistry (1985), Soviet Academy of Sciences
- Habilitation in Physical Chemistry (1998) on capillary electrophoresis in chiral analysis
Research Focus: Specializing in chiral separation science, his work addresses enantioselective pharmaceutical analysis, isotope effects in chromatography, and cyclodextrin-based recognition mechanisms. His methodological innovations span supercritical fluid chromatography, nano-LC, and capillary electrochromatography, with applications in forensic toxicology and environmental monitoring.
Publication Trends: Recent works demonstrate advancements in separating isotopomers and enantiomers of amphetamine derivatives, chiral triazoles, and bioactive ferrocenes. Methodologies emphasize chiral column comparison, molecular modeling integration, and mechanistic studies involving halogen bonding and dispersion forces.
Scientific Recognition:
- Three-time recipient of Journal of Chromatography Top Cited Article Award
- Georgian National Academy of Sciences member (2013)
- Shota Rustaveli National Science Foundation's Scientist of the Year (2016)
- Csaba Horvath Award (2017)
- Honorary Professor at Xi’an Jiaotong University (2019)
Technical Contributions: Developed chiral separation protocols for new psychoactive substances, antiviral agents, and environmental pollutants. His work bridges instrumental development with computational modeling to enhance chiral discrimination mechanisms.
