Adsorption-based microfluidic sensors are promising tools for biosensing. Advanced mathematical models of time response and noise of such devices are needed in order to improve the interpretation of measurement results, and to achieve the optimal sensor performance. Here the mathematical models are presented that take into account the coupling of processes that generate the sensor signal: adsorption–desorption (AD) of the target analyte particles on the heterogeneous sensing surface, and mass transfer (MT) in a microfluidic chamber. The response kinetics and AD noise (which determines the ultimate sensing performance) of protein biosensors are analyzed, assuming practically relevant analyte concentrations, sensing surface areas and MT parameters. The condition is determined under which MT significantly influences the sensor characteristics relevant for reliable analyte detection and quantification. It is shown that the development of improved mathematical models of sensor temporal response and noise can be used as one of strategies for achieving better sensing performance.
- microfluidic biosensor
- surface heterogeneity
- mass transfer
- adsorption–desorption noise
- mathematical model
Microfluidic adsorption-based biosensors are promising devices for real-time, in-situ and low-cost analysis of samples taken from the environment, food or living organisms, enabling detection of the presence and measurement of the amount of target biological specimens: biomolecules (such as proteins or DNA fragments), microorganisms, or other biological structures [1, 2, 3]. Such sensors are highly sensitive, capable of operation with small sample quantities, and also small, lightweight and energy efficient, thus being especially suitable for autonomuous, portable and distributed sensing applications [4, 5, 6]. Due to such characteristics, development of microfluidic sensors is very significant for environmental protection, medicine, agriculture, food inspection, public healthcare and security, and other fields, where they can substitute large and expensive laboratory equipment, typically located far from the place where the samples are taken for the analysis. They also enable the development of new fields of biosensor applications, such as the personalized medical point-of-care diagnostics, telemedicine, wearable sensors etc. [7, 8, 9].
In order to utilize the great potential of adsorption-based microfluidic biosensors for practical applications, research is performed aiming to enable optimization of their performance. The increase of sensitivity and response rate of the sensors, better selectivity, lowering of the minimal detectable concentration, higher reliability of measurement results ant their more accurate interpretation, as well as the research and development of new measurement methods, which enable obtaining of more information about one or multiple adsorbed substances at the same time, are of great practical significance. In that sense, of particular interest is to know the dependence of the temporal response and the sensor noise on the parameters of the sensing element, the measurement system, and the experimental conditions, which requires the development and application of mathematical models that take into account physical processes and phenomena relevant for generating the response and its fluctuations. The adsorption and desorption (AD) processes are inevitably taken into account in modeling of the response and noise, since they are fundamental for sensor operation, and are also the source of adsorption–desorption noise, which sets the fundamental limits of detection and quantification of the analyte. In structures of micrometer and nanometer dimensions, AD noise can dominantly determine the values of minimal detectable and quantifiable signal, as well as other limiting sensor performances, especially in the case of low analyte concentration [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. In various cases, apart from the AD process of the target analyte, different additional processes influence the sensor response kinetics and noise, so it is necessary to take them into account in mathematical models.
The objective of this work is to present mathematical models of the temporal response and adsorption–desorption noise of microfluidic adsorption-based biosensors, that take into account the processes responsible for the generation of the sensor signal: adsorption–desorption of the target analyte particles on the heterogeneous sensing surface, and mass transfer (MT) in a microfluidic chamber. Section 2 presents the mathematical models of the sensor temporal response, while Section 3 describes the AD noise models, developed for the cases considered in Section 2. In Section 4, the results will be presented of the temporal response analysis, and of the analysis of AD noise of protein biosensors, both performed by using of the models presented in Sections 2 and 3, assuming practically relevant analyte concentrations, sensing surface areas and MT parameters. The conclusions will be summarized in Section 5.
2. Mathematical models of sensor temporal response
In adsorption-based biosensors (e.g. SPR (
Here, the term “temporal response of adsorption-based sensors” will be used to denote the temporal change of a certain physical parameter of the sensing element, which is induced by analyte adsorption. At any given time, that change is determined by the number of analyte particles bound to the sensing surface, and that number depends on the target analyte concentration in the sample. This enables the measurement of the concentration of the target substance to be performed by measuring the change of the physical parameter. Therefore, the mathematical model of the sensor temporal response is based on the model of the time evolution of the number of adsorbed particles as a function of analyte concentration, assuming that there is a known (preferably linear) relation between the two quantities.
In the analyses of microfluidic biosensors time response, which have been published in the literature, it is often assumed that the change of the number of adsorbed particles occurs only due to the AD process of the target substance, and the interpretation of experimental results is performed according to that [29, 30]. This simplified interpretation of the events occurring on the sensing surface is justified under the conditions that ensure a negligible influence of other processes, and assume a homogeneous adsorption surface. Analyses that include some of the additional effects that also influence the current number of adsorbed target particles are less abundant in the existing literature. For example, the mass transfer process of target adsorbate particles in a reaction chamber, toward or away from adsorption sites on the sensing surface, coupled with the AD process, is considered in [31, 32, 33]. Competitive AD processes of target and competitor substances are analyzed in [34, 35]. An example of the analysis encompassing multiple phenomena is the research of adsorption processes of two or three analytes on the surface of the same sensor, coupled with mass transfer processes of corresponding particles [36, 37, 38]. Sensing surface heterogeneity and mass transfer processes in biosensors are considered within the analysis of sensor models response in [30, 36, 39], where the need is emphasized for taking into account both of these factors simultaneously while creating experiments and interpreting the measurement results.
In this section, first the starting system of equations will be presented that model the change of the number of adsorbed particles on a sensing surface with an arbitrary number of different adsorption sites, and take into account mass transfer processes of analyte particles in the sensor chamber. Subsequently, simplified physical models will be defined and presented by suitable equations for certain cases of practical significance, in a similar way as in . Also, conditions will be defined under which the application of the approximate models is justified. The case of adsorbing surface with two types of adsorption sites will be particularly considered.
2.1 Starting equations
It is assumed that the sensing element of a sensor is in a microfluidic reaction chamber of a rectangular cross-section, with the sample to be analyzed flowing through it. In Figure 1 (left) a schematic representation is given of the chamber and the sensing element, with the designations of dimensions and coordinate axes. During the laminar fluid flow, a parabolic velocity profile is formed in the chamber (the profile belongs to the
On the sensor’s active surface the recognition and binding of the target analyte occurs. Adsorption–desorption and mass transfer are the key processes for binding of particles to the sensing surface. The former is the process of binding of the analyte particles to surface adsorption sites due to a certain affinity, and unbinding from them. The latter encompasses the processes (convection and diffusion) by which the analyte particles are transported through the microfluidic chamber to or from the binding sites. The coupling of these processes determines the spatial and temporal dependence of the analyte concentration in the chamber.
In the analysis, it is assumed that the adsorption occurs in a single layer only, that only one analyte particle can be adsorbed on an adsorption site, and that the probability of adsorption on any given site (or desorption from it), does not depend on the occupancy of adjacent sites. In the case of a homogeneous sensing surface (in the sense of the affinity toward the target analyte particles), the AD process is characterized by a single pair of adsorption and desorption rate constants,
The active surface of affinity-based sensors is often not comprised of uniform adsorption sites to which the particles of the target analyte bind [30, 40]. The reason for this can be a non-uniform surface morphology or chemical composition, or the existence of multiple possible binding sites for analyte particles due to the material structure on the molecular scale. In more detail, the presence of various defects, irregularities, cavities, pores, impurities, contaminants, different functional groups on the surface, etc. are some of numerous features that constitute the surface heterogeneity. Materials in the form of flakes with specific binding sites on edges and basal planes, such as liquid-phase exfoliated graphene , are also an example of a heterogeneous sensing surface. When a surface is functionalized with specific capturing probes for the target analyte, adsorption sites heterogeneity can be manifested through nonspecific binding of analyte particles to the surface. An adsorption surface can also be heterogeneous due to uneven binding of functionalizing entities (e.g. non-uniform orientation of capturing proteins when attached to the surface), which influences the affinity toward the analyte, and the efficiency of its binding. Whatever the reason, a heterogeneous sensing surface can be characterized by different affinities of different surface sites for the analyte particles binding. The analyte’s AD process is then characterized by a certain distribution of adsorption and desorption rate constants across the surface, and it is described by a model that takes into account the surface non-uniformity.
If we assume that there is
The boundary and initial conditions are as follows: 1. at the entrance of the chamber (
For the determination of
When the total sensor response is the sum of contributions of
The weight factor
For instance, in the case of resonant micro/nanocantilevers and acoustic wave mechanical sensors, particle adsorption changes the mass of the mechanical sensing structure, so that
Numerical solving of Eqs. (1) and (2) requires the surface distributions of different adsorption sites to be known, which is rarely the case. Instead of that, approximations can be introduced in order to simplify the equations, which can even enable obtaining of the analytical solution valid for the transient regime and/or the steady state. The approximations are introduced based on the comparison of adsorption, convection and diffusion time scales.
2.2 Adsorption limited response kinetics
A significant reduction of mathematical complexity of the problem is possible when it is justified to assume that the concentration of the analyte in the reaction chamber is spatially uniform, constant in time, and equal to the concentration in the sample injected in the chamber. Eq. (3) is then reduced to
The analytical solution of each of Eqs. (6), with the initial condition
This model is applicable in the cases where the transfer flux toward the sites of each type is sufficiently greater than the adsorption flux, so that the binding kinetics is adsorption limited [31, 32, 37]. This means that the number of adsorbed particles on the given surface and at the given analyte concentration is determined only by the AD process parameters at any given time. This is the ideal case for obtaining the data about the AD process and its kinetics from the measured time response of the sensor. The transport-adsorption regime of this kind is typical for fast diffusing particles (such as gas molecules and certain biomolecules of small mass).
2.3 Mass transfer influenced response kinetics
When the transport flux of analyte particles is lower than the adsorption flux, or comparable with it, the time evolution of the numbers of adsorbed particles is affected by mass transfer processes [31, 32, 37]. The temporal and spatial change of the analyte concentration in the sensor chamber depends on the parameters of AD and mass transfer processes in the system of a given geometry. As the adsorption flux becomes more dominant, the change of the analyte concentration in the chamber becomes more pronounced due to the slow compensating influx of particles carried by transport processes in the space that is being depleted of particles due to their fast binding to the adsorbing surface. Here, the analysis will be focused on the situation in which a thin depleted layer is formed adjacent to the sensing surface, because it is common in many types of microfluidic sensors (especially those whose reaction chamber height is of the order of ≥10 μm ). It also enables approximations to be used, which lead to the simplified time response mathematical model, and also yield the analytical expression for the AD noise spectral density.
When the diffusion time scale is greater than the convection time scale (expressed by
where the mass transfer coefficient is given as
and Eqs. (2) after averaging of the quantities over the surface yield
The time evolution of the numbers of adsorbed particles on different types of sites is determined by Eqs. (10) and (11), i.e. by
The presented mathematical model is applicable for the thin depleted zone adjacent to the sensing surface, which is more likely to exist at lower
The simplest heterogeneous sensing surface contains two types of adsorption sites. The corresponding equations are
3. Mathematical models of adsorption-desorption noise
Due to the inherently stochastic nature of processes involved in the analyte particle binding-unbinding events on the sensing surface, the number of adsorbed particles randomly fluctuates, even after reaching the steady state, so it can be expressed as
Here, the goal is to obtain the analytical expression for the spectral density of sensor AD noise, when all the transient processes are finished, i.e. when an steady state is established. According to Eq. (16), the basis of this analysis is the analysis of fluctuations of the number of adsorbed particles around the steady-state values. Before presenting the theoretical models of AD noise of sensors with heterogeneous sensing surface, a short overview will be given of the already published results that include mathematical modeling of AD noise of sensors and other micro/nanodevices.
An insight into the existing literature shows that AD noise analyses have been usually limited to the consideration of individual phenomena pertinent to fluctuations of the number of adsorbed particles – e.g. fluctuations originating from a stochastic single-analyte AD process  or fluctuations due to surface diffusion of adsorbed particles .
First papers on AD noise in micro/nanodevices were focused on resonant mechanical structures [12, 48, 49]. Dating from the same period is the first paper on AD fluctuations in micro-biosensors . In subsequent publications, the analysis of AD fluctuations was performed for various types of sensors (mass sensors with micro/nanocantilevers [15, 16, 50], semiconductor resistive gas sensors , plasmonic sensors , quartz crystal microbalance gas sensors ), which operate in a single-gas environment, assuming Langmuir adsorption. In  a theoretical AD noise model is presented for the Wolkenstein adsorption of particles of a single gas, applicable in the case of chemical adsorption in semiconductor resistive sensors.
In real situations, certain processes (e.g. various cases of non-specific adsorption, or mass transfer processes), which are coupled with the AD process of target particles, affect the sensor response, and thus influence the response fluctuations. Therefore, it is necessary for the AD noise analysis to include the coupling of multiple processes, depending on the considered practical case. In  the expression is derived for the PSD of adsorbed mass fluctuations due to the coupled AD processes of an arbitrary number of gases, by using the Langevin approach. The AD noise model in the case of multilayer adsorption according to the BET model is presented in [55, 56]. Analyte diffusion within the sensor chamber is considered together with the binding of particles to the surface sites, in order to analyze the fluctuations and noise figures of merit of biosensors in [14, 57] assuming the adsorption surface of infinite capacity. The coupling of mass transfer (convection and diffusion) and AD processes of one or multiple substances on the sensing surface is taken into account in the development of the AD noise model of microfluidic sensors, which is presented in [58, 59, 60]. The combined effect of the AD process, the mass transfer in the sensor chamber and the surface diffusion on the fluctuations of the number of adsorbed particles is analyzed in , and a good match is shown between the derived PSD of AD noise and the experimental results obtained by using a graphene gas sensor . The influence of the analyte depletion from the sample on the AD noise is modeled and analyzed in . In  the analysis is presented of the signal-to-noise ratio of a nanowire biosensor, based on stochastic simulations of the AD process coupled with diffusion.
According to the current trends in micro- and nanosensor development (the decrease of the sensing surface area, and the decrease of detectable concentrations), the analysis of AD fluctuations becomes increasingly significant for the estimation of limiting performances of such devices, and for optimization of sensor design and experimental methods. In spite of that, the topic of AD noise is scarce in the literature, compared to the total number of papers on chemical and biological sensors. Also, the published experimental results pertinent to AD noise are very scarce, and can be found for gas sensors [17, 18, 19, 20].
In the following part of this Section, mathematical models will be presented that take into account the existence of different types of adsorption sites on the sensing surface.
3.1 Langevin method for multicomponent stochastic processes
There are two approaches that are commonly used for the analysis of the fluctuations of the number of adsorbed particles: the first is based on the master equation, and the second on the Langevin equation, with the use of Wiener-Khinchin theorem. Both the approaches are described in detail in Supplementary data of Ref.  for a competitive AD process of two analytes, and all the given expressions can be generalized in a simple manner in order to be valid for
When adsorption and desorption processes occur on the heterogeneous active surface of a sensor, the number of particles adsorbed on each type of adsorption sites stochastically fluctuates in time. The fluctuations of the number of particles adsorbed on sites belonging to the
The Langevin equation in the complex domain (
The expressions for the elements of the matrix Sξ2 can be derived by using the formal statistical approach, as presented in . Here only the final result is shown (
where the row vector of weight factors
3.2 Adsorption limited binding
In the case of the transport-adsorption regime, known as the rapid mixing regime, the transfer flux toward the adsorption sites dominates over the adsorption flux, so the dependence of the concentration on the spatial coordinates is negligible, and it results in adsorption-limited response kinetics, determined by the kinetic Eqs. (6). These equations are mutually independent, and show the change of the number of adsorbed particles on any of
after a random source function is added on the right side of each equation.
When this system of equations is written in the matrix form, as shown in SubSection 3.1, the quadratic diagonal matrix
The elements of the matrix
Since all the quadratic matrices on the right side of Eq. (19) are diagonal, the matrix
with the characteristic frequency
3.3 Mass transfer influenced binding
The use of TCM for approximation of spatially and temporally dependent analyte concentration in a sensor chamber (see SubSection 2.3) has enabled the presentation of the macroscopic kinetic equations (Eqs. (10), (11) and (14)) in the form in which the change of the number of adsorbed particles in unit time equals the difference between the instantaneous effective rates of adsorption and desorption,
All the derivatives are calculated for
and after differentiation
Based on Eq. (20)
Now, all the quantities are known that enable the determination of the spectral and cross-spectral densities of fluctuations of the numbers of adsorbed particles on different types of adsorption sites, according to Eq. (19), as well as the spectral density of the sensor response fluctuations, based on Eq. (21)
so the PSD of the AD noise, calculated based on Eq. (21), is
The characteristic frequencies of the AD noise spectrum are
The expressions for
4. Results and discussion
In order to analyze the effects of mass transfer and surface heterogeneity on the sensor temporal response and AD noise, numerical calculations are performed for the case of protein biosensor, on whose active surface two types of adsorption sites exist with different affinities toward the target analyte. The results are presented in terms of the adsorbed mass on the sensing surface, assuming the values of adsorption and desorption rate constants from the ranges corresponding to biomolecules and biosensors :
Figure 2 shows the temporal change of adsorbed mass, representing the temporal response of the sensor whose measured parameter is a function of the total bound mass of the target protein. The total mass adsorbed on the sensing surface is shown (black lines), as well as the adsorbed amounts on each of the two types of adsorption sites (red lines for the type 1 sites, blue lines for the type 2 sites). The five diagrams correspond to different shares of the types of adsorption sites in the overall number of sites, which is expressed by different values of the parameter
In the absence of the mass transfer influence (dashed-line curves shown in the diagram), the existence of two types of adsorption sites is clearly noticeable based on the sharp transition from fast to slow transient regime (for
When the mass transfer is characterized by
Figure 3 shows the spectral density (SD) of adsorbed mass fluctuations, (
It can be noticed that at all the values of
By comparing the diagrams for the case of a surface containing a single type of adsorption sites (i.e. for
The diagrams also show that when the mass transfer influence is negligible, the two characteristic frequencies of the AD spectrum do not change at different shares of adsorption site types on the sensing surface. They are determined by the characteristic frequencies that correspond to the fluctuation spectra of adsorbed masses on each of site types, which are given by expressions
In this chapter, the mathematical modeling has been performed of time response and adsorption–desorption (AD) noise in microfluidic adsorption-based biosensors whose active surface is heterogeneous in the sense that it contains adsorption sites of different affinity toward the target analyte. The adsorption–desorption processes of analyte particles on different types of adsorption sites, as well as mass transfer in a microfluidic chamber, have been taken into account as the coupled processes that generate the sensor signal. The devised model of AD noise is the first that simultaneously takes into account surface heterogeneity and mass transfer through both the convection and diffusion of analyte particles. The analytical expression of the spectral density of AD noise has been derived. The models of the time response and AD noise of a sensor with heterogeneous adsorbing surface in the case of negligible mass transfer influence have also been presented. The criterion is given based on which it can be discerned whether the mass transfer influence is significant or negligible, so that the appropriate mathematical model of the response and noise can be chosen.
The derived mathematical models have been used for the analysis of the response and AD noise of protein biosensors that have two types of adsorption sites on the sensing surface. The comparison of the results obtained by the use of the models that take into account mass transfer effects, and those that neglect them, for different shares of two types of binding sites, has enabled drawing conclusions on both separate and coupled influences of surface heterogeneity and transport processes on the sensor response and noise.
While a slow mass transfer increases the sensor response time, the existence of different adsorption site types affects both the transient regime and the sensor response magnitude in the steady state. The minimal mass transfer coefficient value at which the response kinetics can be considered as adsorption limited increases with the increase of the share of high affinity binding sites. The influence of mass transfer (of given parameters) on the time evolution of the number of adsorbed particles is more pronounced on sites with a higher adsorption rate constant.
At any share of the two types of binding sites on the sensing surface, mass transfer causes the increase of the AD noise, and thus increases the sensor’s fundamental limits of analyte detection and quantification. For a given ratio of the numbers of adsorption sites of the two types, mass transfer causes a shift of the characteristic frequencies in the AD noise spectrum. When the mass transfer influence is pronounced, the characteristic frequencies shift with the change of the ratio of the numbers of sites of different types. Therefore, the AD noise spectrum analysis can yield information not only on adsorption and desorption rate constants and analyte concentration, but also on mass transfer parameters and on the share of a certain type of binding sites on the sensing surface. It can be used as an additional source of data in biosensing.
As the results of the performed analysis have shown a potentially significant influence of sensing surface heterogeneity and mass transfer processes on the sensor temporal response, as well as on AD noise, which is inevitable in adsorption-based sensors and determines their ultimate sensing performance, the presented mathematical models can enable better interpretation of measurement results, and give guidelines for ensuring lower noise levels and improved detection limits in microfluidic biosensors.
This research was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, grant number 451-03-9/2021-2114/200026. It was also supported by the Science Fund of the Republic of Serbia, PROMIS, #6057070, Gramulsen.