Label-Free Characterization and Separation of Liposomes using Dielectrophoresis (DEP)

Liposomes are currently the gold standard in drug delivery systems, widely used to transport chemotherapeutics and mRNA vaccines [1]. However, a critical bottleneck in their manufacturing is heterogeneity: synthesized batches often contain a mixture of empty vesicles, varying sizes, and inconsistent drug encapsulation efficiencies. Traditional purification methods (like centrifugation or chromatography) are often slow, require large sample volumes, or lack the sensitivity to distinguish between a liposome that is "full" and one that is "empty." To advance nanomedicine, we need a high-precision, non-invasive method to characterize and sort these carriers based on their internal payload. This project addresses that gap by leveraging Dielectrophoresis (DEP), a microfluidic technique that manipulates particles using non-uniform electric fields [2], to develop a label-free quality control and separation platform.

In this project, you will work at the intersection of microfluidics, physics, and physical chemistry. The core hypothesis is that the internal chemical loading of a liposome fundamentally alters its dielectric permittivity and conductivity, thereby changing its response to DEP forces [3, 4]. Your goal is to experimentally determine the "dielectric fingerprint" of liposomes with varying cargo loads. Specific tasks include:

• DEP Investigation: Systematically testing the behaviour of Liposomes against electric field to identify the threshold required to separate loaded vs. unloaded vesicles [5].
• Optimization: Determining the interplay between the applied voltage, sample condition, and the liposome's properties to maximize separation efficiency.
• Data analysis: Analysing the output of the experiment and checking with the hypothesis.
• Synthesis & Characterization: Preparing liposome samples with controlled variations in chemical loading (e.g., different drug mimics or salt concentrations).

This position is ideal for a student looking to gain a multidisciplinary skillset highly valued in both academia and the biotech industry. You will move beyond simple observation to rigorous quantitative analysis. Skills you will learn:

• Lab Skills: Liposome extrusion, dynamic light scattering (DLS), and fluorescence microscopy.
• Data Analysis: Image processing (e.g., ImageJ/Python) to investigate and analyzing the region of interest (ROI) and making sense of the experimental data.
• Academic Output: This is a research-intensive project designed to generate novel results. High-quality data produced during this thesis will contribute directly to ongoing PhD research, with a strong possibility of co-authorship in a peer-reviewed journal article.