Towards Shape Optimization of a Quartz Crystal Microbalance Biosensor

Quartz Crystal Microbalance (QCM) biosensors are widely used for early-stage detection of pathogens in drinking water. However, in their standard geometric configuration, a substantial fraction of pathogens attach to regions of the crystal with low sensitivity, thereby reducing the overall detection efficiency. We believe that the situation can be improved and to this end, we have created a numerical model of the QCM biosensor and used it to simulate how changes in the sensor geometry affect its detection efficiency. The model works in two stages: first, the flow field is computed with a non-standard geometry encoded via the immersed boundary method; subsequently, the pathogen trajectories, and thus the sensor efficiency, are determined through the Lagrangian particle tracking. Although accurate, the particle-tracking stage is computationally prohibitive and renders direct optimization impractical. To address this limitation, we investigate whether biosensor efficiency can be reliably predicted using flow-field information alone, thereby bypassing the costly second stage. To further accelerate the optimization process, a non-intrusive reduced-order model of the flow is constructed. The resulting framework enables efficient multi-objective optimization aimed at maximizing detection efficiency while simultaneously minimizing pressure loss.