Dörte Solle

Dr. Dörte Solle is a Researcher at the Institute of Technical Chemistry, Leibniz University Hannover, within the Faculty of Natural Sciences. She leads the Bioprocess Analytics working group and serves as Deputy Representative for Research Staff on the Faculty Council. Her work bridges research, teaching, and technological innovation in bioprocessing.

Research Interests:

• Bioprocess Analytics
• Cell Cultivation (especially CHO cells for monoclonal antibody production)
• Chemometrics and multivariate data analysis
• Bioprocess Automation and control
• Development of spectroscopic and optical sensors for online monitoring
• Disposable bioreactor systems and process intensification

Her recent publications (2022–2025) highlight a strong trend toward integrating machine learning, digital twins, and advanced sensor systems into bioprocess engineering. There is a consistent focus on improving monoclonal antibody production through quality-by-design, perfusion systems, and real-time analytics. The work combines experimental innovation with modeling for intelligent process control.

Scientific Awards:

• No awards explicitly mentioned in the provided text.

Advising and Grants:
While specific student names and grant details are not listed, Dr. Solle leads a research group (Solle AG) and co-authors multiple publications with junior researchers, indicating active advising in PhD and postdoctoral training. Her involvement in projects involving 3D-printed devices, sensor development, and process optimization suggests participation in funded research initiatives, likely supported by national or institutional grants.

Labs and Teams:
Dr. Solle heads the Bioprocess Analytics group at the Institute of Technical Chemistry. The team focuses on developing and applying advanced analytical methods for bioprocess monitoring, including fluorescence spectroscopy, NIR/MIR, in situ microscopy, and novel disposable sensor systems. The lab is equipped for cell cultivation from 15 mL to 50 L scales and emphasizes automation and data-driven process control.