Due to the current situation (COVID-19) access to the laboratories, and in fact the entire David de Wied & Vening Meinesz buildings, is strictly limited until further notice.
We can therefore only accept a limited number of new MSc or BSc projects; applications are handled on a first-come-first-serve basis. Please make sure to fill in and submit your request via the online form linked to here.
UPDATE January 29, 2021:
Unfortunately, at the moment, our group can no longer accept additional requests for BSc and MSc projects for periods 3 and 4 2021. The reason is that we have restricted lab access due to the COVID-19 measures and we have no more capacity for additional projects.
However, some of the calls below might still be open if they contain parts that do not require immediate lab access. Please contact the supervisors directly for more information about the options.
Supervisor: Erik Maris
Title: Machine Learning for Single-Molecule Microscopy
Diffusion of molecules through nanoporous materials is often the limiting factor in their application in catalysis and separation. The molecular motion is usually heterogeneous and complex, due to strong interactions between the diffusing molecules and nanoporous material . Single-molecule localization microscopy allows us to study these interactions via the trajectories of individual molecules . Due to diffraction of light, the individual molecules appear as bright spots on the camera, which are much larger than the actual size of the molecules. To find their location with nanometer precision, we fit the center of the spot in an analysis step called “localization”. This is easy when the molecules have a fixed position; however, when the molecules move, the spot on the camera is blurred and the fit is poor. Machine learning, and particularly neural networks, have proven to be a great tool to localize molecules efficiently . In this project, you will train neural networks to localize molecules with motion blur and benchmark its performance against established techniques.
 Hendriks, F.C., et al. J. Am. Chem. Soc. 139, 13632–13635 (2017)
 Manzo, C., et al. Rep. Prog. Phys. 78, 124601 (2015)
 Möckl, L., et al. Biomed. Opt. Express 11, 1633–1661 (2020)
Supervisor: Rafael Mayorga González, Yadolah Ganjkhanlou
Title: Mapping heterogeneity within catalyst particles using carbon quantum dots
Carbon dots (CQDs) are new organic luminescent compounds that were accidentally discovered by Xu et al. in 2004 . CQDs have been vastly investigated as a local sensor for different applications due to the sensitivity of their luminescence to different parameters (e.g., thier solvent, temperature, the presence of specific metals, and/or the pH of their environment). For instance, they have been applied as chemosensors of different metal ions in solvents  and as intracellular pH detectors in biological systems . The goal of the proposed project is to develop a simple method based on CQDs in order to get a submicron 3D map of specific properties within catalyst particles. To perform this project, the student will synthesize CQDs, stain different solid materials with varying properties (e.g., zeolites with different Si/Al ratios and porosities) with the prepared CQDs, and image the stained solid samples with a confocal fluorescence microscope. The obtained images will be used to correlates acidity (or other properties) with emission spectra of CQDs. The student will have opportunities to learn about the synthesis of CQDs, using the confocal microscope, and processing the obtained images. He or she will also become familiar with a few other advanced characterization techniques of catalyst materials.
 Liu, Y., Duan, W., Song, W., Liu, J., Ren, C., Wu, J., … & Chen, H. Red emission B, N, S-co-doped carbon dots for colorimetric and fluorescent dual mode detection of Fe3+ ions in complex biological fluids and living cells. ACS applied materials & interfaces 2017 9(14), 12663-12672.
 Ye, X., Xiang, Y., Wang, Q., Li, Z., & Liu, Z. A Red Emissive Two‐Photon Fluorescence Probe Based on Carbon Dots for Intracellular pH Detection. Small 2019 15(48), 1901673.