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

Ivana Jokić,Predrag M Krstajić,Gradimir V Milovanović,Zoran Djurić,Miloš Frantlović,Katarina Radulović

doi:10.3390/bios11060194

Ivana Jokić, Predrag M Krstajić + Show 4 more

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https://doi.org/10.3390/bios11060194

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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.

Highlights

Microfluidic sensors are promising tools for chemical and biological detection [1,2,3,4].The operation of a large class of such devices, known as adsorption-based sensors, relies on the adsorption–desorption (AD) process of a target substance on the surface of a sensing element
We aim to investigate the temporal change in the statistical parameters of the biosensor stochastic response from the beginning of the adsorption process on the sensing surface until the steady state is reached, taking into account the mass transfer of analyte particles by both convection and diffusion, which corresponds to the realistic operating conditions in microfluidic biosensors
Where ka and kd are the adsorption and desorption rate constants, respectively, CS is the analyte concentration in the immediate vicinity of the binding sites on the sensing surface of area A, Nm is the number of binding sites on the surface, and km is the mass transfer coefficient, introduced in two-compartment model (TCM) as km = 1.467(D2 vm /(Ls hc ))1/3 [31] in order to characterize the transport of analyte particles by both convection and diffusion between the bulk solution and the surface binding sites (D is the diffusion coefficient of analyte particles, vm is the mean convection velocity, Ls is the adsorption zone length, and hc is the sensor chamber height)

Summary

Introduction

The operation of a large class of such devices, known as adsorption-based sensors, relies on the adsorption–desorption (AD) process of a target substance on the surface of a sensing element. These include SPR (Surface Plasmon Resonance), CNT (Carbon NanoTube) or NWFET (NanoWire Field Effect Transistor), resistive graphene-based, potentiometric, SAW (Surface Acoustic Wave), FBAR (thin Film Bulk Acoustic wave Resonator), microcantilever sensors, etc. A coupled effect of AD and MT processes determines the temporal change in the number of particles adsorbed on the sensing surface, N(t), which causes a change in a measurable sensor parameter, yielding the sensor response

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