Sample volume as a key design parameter in affinity-based biosensors

Affinity-based biosensors have become indispensable in modern diagnostics and health monitoring. While considerable research has focused on optimizing analyte transport and binding kinetics, a fundamental parameter—sample volume—remains largely underexplored in biosensor design. This is critical because biosensor performance depends on the absolute number of target molecules present, not solely their concentration, making volume a key consideration where sample availability is limited. To address this gap, we developed a mathematical two-compartment model integrating simplified mass transport, Langmuir binding kinetics, and mass conservation under finite volume constraints. The model accurately simulates biosensor binding kinetics (root-mean-square deviation < 5%) and predicts equilibration time and required volume (mean relative error of 7.3%) compared to finite-element simulations, whilst achieving more than 100-fold reduction in computational time. From the framework, we derived analytical expressions for biosensor equilibration time and required volume as a function of the Damköhler number, ranging from reaction-limited to transport-limited systems. These analytical solutions predict equilibration time and required volume for a biosensors (mean relative error of 11.0% and 11.7% compared to numerical simulations), providing rapid estimates from first-order biosensor parameters without numerical simulation. We validated this framework experimentally by optimizing flow rate parameters for a quartz crystal microbalance (QCM) biosensor and retrospectively applied optimization guidelines on a published biosensor. The open-source model and analytical expressions allow researchers to gain mechanistic insights, optimize device performance, and make informed design decisions tailored to specific healthcare contexts, including point-of-care testing and resource-constrained environments. • Sample volume is a key parameter in biosensor design • Biosensors measure bound molecules and not only concentration • The open-source model provides rapid insight into biosensor kinetics • Biosensors can be optimized to minimize required volume and/or time • The Damköhler number determines the optimization regime