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Master Thesis Project

Optimizing Fluid Transport and Saturation Kinetics in Lateral Flow Assays

Photo by Solen Feyissa


Paper-based lateral flow assays (LFA) are widely employed as diagnostic tools within a broad range of fields, including biomedical research, healthcare, and environmental science. Due to their simplicity and affordability, LFAs are especially useful for Point-of-care (POC) diagnosis. Paper-based LFAs are attractive as tools to evaluate biological markers in bodily fluids (saliva, urine, etc), and are perhaps most well known for their role in affordable disease diagnostics. Paper-based devices are especially simple because of the power-free fluid transport by capillary action. The flow characteristics of these assays determine the diagnostic performance (i.e. sensitivity and specificity) of the tests, so improving our fundamental understanding of flow in paper-based LFAs is of paramount importance. Several questions remain regarding how fluid flow impacts target binding and signal generation in these assays, and we believe recent advances in computational simulation can help us to find the necessary answers.


Herein we propose to develop an in-silico model to reproduce with high-fidelity real-life LFA test performance. This model, once calibrated and validated with real LFA tests, will be used to answer important questions regarding how LFAs operate. We will subsequently use the knowledge we have gained to improve the performance of this important class of diagnostic tool.


To achieve this goal, we will need to:


  • Derive the parameters to define the computational model;

  • Verify the simulated binding and transport phenomena with the experimental data – through real-time image acquisition;

  • Optimize the real materials (with possibility of introducing novel components) to improve efficacious transport and binding in the LFA.

Prior knowledge of computational fluid dynamics and/or pharmaco-kinetics simulations is preferential.


Contact person:

Dr. Monika Colombo, monika.colombo@chem.ethz.ch

Co-supervisors: Leonard Bezinge, Dr. Daniel Richards

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