Thomas Johann Tewes, M.Sc.

As a research associate at the Faculty of Life Sciences, I am involved in teaching and research in the fields of chemical process engineering and analytical science. A particular focus of my work is Raman spectroscopy and the data-driven analysis of spectroscopic measurements, especially using multivariate methods and machine learning approaches.

In teaching, I am primarily involved in the supervision and organization of practical courses and exercises related to chemical process engineering, analytical methods, and biological resources. My research interests also include the spectroscopic characterization of biological systems and the development and application of analytical methods at the interface of chemistry, microbiology, and data analysis.

• Tewes, T. J. (2026). Mean Raman Spectra of Polyethylene Glycol (PEG) Samples with Different Molecular Weights [Dataset]. https://doi.org/10.5281/zenodo.18298724.
• Tewes, T. J., Duismann, C. N., Singh, U., Simon, P. F. W., & Bockmühl, D. P. (2026). Raman Spectroscopic Classification of Polyethylene Glycol Samples of Varying Molecular Weights Using Machine Learning. Molecules, 31(5), 778. https://doi.org/10.3390/molecules31050778.
• van Leuven, N., Lucassen, R., Lipski, A., Rheinschmidt, R., Kleinke, M., Tewes, T. J., & Bockmühl, D. P. (2026). The Microbiota of Evaporative Cooling Systems and the Impact of Rainwater as an Alternative Water Source. Water environment research, 98(7), 1–12. https://doi.org/10.1002/wer.70496.
• Bockmühl, D. P., & Tewes, T. J. (2025). With a Little Help from My Friends: The Role of Detergents for Energy, Efficiency, and Hygiene in Domestic Dishwashing. ChemPlusChem, 90(2), 1–5. https://doi.org/10.1002/cplu.202400657.
• Kudla, M., Tewes, T., Gibbin-Lameira, E., Harcq, L., & Bockmühl, D. (2025). Unlocking the Power of Short Cycles: Enhancing Hygiene in Domestic Dishwashers [Dataset].
• Tewes, T. (2025). Luminescence and Raman spectroscopic data of bacteria with and without exposure to water-soluble polymers (WSPs) [Dataset]. https://doi.org/10.5281/zenodo.17018211.
• Tewes, T. J., Brands, B., Schacher, F. H., & Bockmühl, D. P. (2025). Exploring interactions of Aliivibrio fischeri with water-soluble polymers using bioluminescence and Raman microspectroscopy. PLOS One, 20(9), e0330775. https://doi.org/10.1371/journal.pone.0330775.
• Tewes, T. J., Bockmühl, D. P., Kudla, M., Gibbin-Lameira, E., & Harcq, L. (2025). Hygiene Efficacy of Short Cycles in Domestic Dishwashers. Microorganisms, 13(7), 1542. https://doi.org/10.3390/microorganisms13071542.
• Tewes, T. J., Harcq, L., Minot, P., & Bockmühl, D. P. (2024). Brevity is the soul of wit – how time, temperature and detergent choice impact the cleaning performance in domestic dishwashers. Tenside Surfactants Detergents, 61(1), 10–23. https://doi.org/10.1515/tsd-2023-2563.
• Tewes, T., Kerst, M., Pavlov, S., Huth, M. A., Hansen, U., & Bockmühl, D. P. (2024). Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms. Heliyon, 1(6), e27824. https://doi.org/10.1016/j.heliyon.2024.e27824.
• Tewes, T. J., Welle, M. C., Hetjens, B., Tipatet, K. S., Pavlov, S., Platte, F., & Bockmühl, D. (2023). Understanding Raman Spectral Based Classifications with Convolutional Neural Networks Using Practical Examples of Fungal Spores and Carotenoid-Pigmented Microorganisms. AI, 4(1), 114–127. https://doi.org/10.3390/ai4010006.
• Tewes, T. J., Harcq, L., & Bockmühl, D. (2023). Use of Automatic Dishwashers and Their Programs in Europe with a Special Focus on Energy Consumption. Clean Technologies, 5(3), 1067–1079. https://doi.org/10.3390/cleantechnol5030054.
• Hetjens, B. T., Tewes, T., Platte, F., & Wichern, F. (2022). The application of Raman spectroscopy in identifying Metarhizium brunneum, Metarhizium pemphigi and Beauveria bassiana. Biocontrol science and technology, 32(3), 329–340. https://doi.org/10.1080/09583157.2021.2007851.
• Tipatet, K. S., Davison-Gates, L., Tewes, T. J., Fiagbedzi, E. K., Elfick, A., Neu, B., & Downes, A. (2021). Detection of acquired radioresistance in breast cancer cell lines using Raman spectroscopy and machine learning. Analyst, 146(11), 3709–3716. https://doi.org/10.1039/d1an00387a.