Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors

Abstract

In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.

Modeling of Nitrification Kinetics in a Respirometric Biosensor under Suboptimal Conditions

“…The signal-to-noise ratio (SNR) was estimated as ν/σ, where ν is the expected (measured) value of oxygen consumption rate, and σ is a measure of fluctuations resulting from the stochastic nature of the processes [8]. The signal-to-noise ratio (SNR) is estimated as ν/σ, where ν is the expected (measured) value of oxygen consumption rate as σ is a measure of fluctuations resulting from the stochastic nature of the processes [22].…”