Steady-state analysis of stochastic time response of chemical and biological microfluidic sensors

Abstract

In this paper we first give a short review of two stochastic models describing both the expected value and variance of the random number of adsorbed particles in microfluidic adsorption-based chemical and biological sensors. One model takes into account the influence of coupling of stochastic adsorptiondesorption processes and mass transfer on the change of the number of adsorbed particles, while the other neglects the influence of mass transfer. Subsequently, by using the two models, we perform the analysis of the expected value and variance, as well as the sensor's signal-to-noise ratio, after reaching the steady state of all transient processes. We compare the results obtained by using the different models, and determine conditions for their application. We estimate the influences of the sensing surface area and the concentration of target particles on statistical parameters of sensor response and signal-to-noise ratio, considering the cases where mass transfer is significant, and those where it is not. We particularly analyze the mass transfer influence on the expected value, variance and signal-to-noise ratio. Such analysis does not exist in the available literature. The presented analysis yields new knowledge about the stochastic response of adsorption-based sensors, and it is significant for their optimization in order to achieve reliable analyte detection and improved sensing performance.