PhD - Leuven | Just now
Nanoparticles are ubiquitous, and can be both a nuisance and an asset. In advanced CMOS processing the critical feature size has reached 10 nm and control of particle contamination on this length scale is challenging [1]. At the same time nanoparticles are a key building block in a novel colloidal memory concept [2]. In life sciences the importance of characterization and manipulation of proteins, DNA, drug delivery vesicles, mRNA vaccines etc. cannot be overestimated. Hence understanding, control, and manipulation of nanoparticles is important.
Dielectrophoresis (DEP) is a technique where an inhomogeneous electric field results in a force on a polarizable object. This phenomenon is well understood for large objects such as water-in-oil droplets or biological cells. Here bulk properties and classical Maxwell electrostatics are sufficient to describe the problem. However, for nanoparticles (and this thesis aims at a size range of ~10 nm) the problem is much more complex. Mobile ions in the electric double layer surrounding any charged surface [3] result in a variety of electrokinetic forces that can no longer be neglected [4]. The behaviour of water dipoles in the hydration shell around charged surface groups is restrained, resulting in modified dielectric behaviour of the interface. Furthermore an imbalance in the surface charges may result in the presence of a permanent dipole. Both of these have a strong impact on the DEP response of proteins [5] [6] [7].
In addition, much larger electric field gradients are required to manipulate nm-sized particles. These can only be generated close to nanoscale electrodes (electrode-based DEP, eDEP) or nanoscale constrictions (insulator-based DEP, iDEP) and do not extend very far into the flow channel. Hence a second problem is to design structures and devices that can handle large volumes of liquid. Also here the effect of the electrokinetic forces close to the device surface cannot be neglected.
This PhD will advance the state of the art in understanding and manipulating nanoparticles in the 10 nm size range using a combination of modelling and experimental validation. The existing Maxwell stress tensor approach [4] will be generalized and combined with finite element methods to handle complex device geometries. Dielectric spectroscopy will be used to characterize the response of dilute particle solutions and validate the modeling results [7]. Nanopore-based devices will allow the study and manipulation of individual nanoparticles [8].
[1] D. Lee et al., “Insulator-Based Dielectrophoresis for Purifying Semiconductor Industry-Compatible Chemicals with Trace Nanoparticles,” JACS Au 5, 2342–2349 (2025), doi: 10.1021/jacsau.5c00307.
[2] M. Rosmeulen et al., “Liquid Memory and the Future of Data Storage,” in 2022 IEEE International Memory Workshop (IMW), Dresden, Germany: IEEE, May 2022. doi: 10.1109/IMW52921.2022.9779295.
[3] M. Z. Bazant, M. S. Kilic, B. D. Storey, and A. Ajdari, “Towards an understanding of induced-charge electrokinetics at large applied voltages in concentrated solutions,” Advances in Colloid and Interface Science 152, 48–88 (2009_, doi: 10.1016/j.cis.2009.10.001.
[4] B.-Y. Shih et al., “Not only dielectrophoresis: The crucial role of electrokinetic phenomena in a dielectric particle’s response to an oscillating electric field,” Journal of Electrostatics 133, 104009 (2025), doi: 10.1016/j.elstat.2024.104009.
[5] S. S. Seyedi and D. V. Matyushov, “Protein Dielectrophoresis in Solution,” J. Phys. Chem. B 122, 9119–9127 (2018), doi: 10.1021/acs.jpcb.8b06864.
[6] D. V. Matyushov, “Dipolar response of hydrated proteins,” The Journal of Chemical Physics 136, 085102 (2012), doi: 10.1063/1.3688229.
[7] R. Pethig, “Protein Dielectrophoresis: A Tale of Two Clausius-Mossottis - Or Something Else?,” Micromachines 13, 261 (2022), doi: 10.3390/mi13020261.
[8] T. Colburn and D. V. Matyushov, “Trapping proteins on nanopores by dielectrophoresis,” Journal of Applied Physics 133, 164701, (2023), doi: 10.1063/5.0144564.
Required background: Physics, Electrical Engineering, Computer Science (computational modelling) or equivalent
Type of work: 50% modeling & simulation, 40% experimental & data analysis, 10% literature
Supervisor: Pol Van Dorpe
Co-supervisor: Wim Van Roy
Daily advisor: Radin Tahvildari
The reference code for this position is 2026-111. Mention this reference code on your application form.