How Glucose Sensors Work: From Enzyme Reactions to Electrical Signals

Introduction

One of the most widely used biosensors is the glucose sensor, particularly in diabetes treatment, where continuous monitoring of glucose levels in the blood is essential for effective patient care. This sensor integrates a biological recognition element, typically the enzyme glucose oxidase (GOx) with a physicochemical transducer that converts an enzymatic reaction into an electrical signal.

The core principle of a glucose biosensor is the catalytic oxidation of glucose to gluconolactone by GOx, a process in which the flavin adenine dinucleotide (FAD) cofactor is reduced to FADH₂. To complete the catalytic cycle, FADH₂ is oxidized by oxygen at the electrode's surface, and the resulting current is directly proportional to the glucose concentration in the sample.

Although the enzymatic kinetics is usually described by a Michaelis-Menten mechanism, the enzyme is usually immobilized at the electrode surface, and the enzymatic reactions are coupled to interfacial electron transfer reaction and mass transport. These additional steps influence the kinetics and the resulting current. The reactions sequence is shown in the figure below.

Figure: Schematic of the enzymatic and electrochemical steps.

The former sequence can be translated into following set of reactions:

\[ \begin{align} \text{Glu} + \text{GOx(FAD)} &\underset{k_2}{\overset{k_1}{\rightleftharpoons}} [\text{Glu...GOx}] \quad &v_1 \\ [\text{Glu...GOx}] &\xrightarrow{k_3} \text{Glt} + \text{GOx(FADH}_2) \quad &v_2 \\ \text{GOx(FADH}_2) + \text{O}_2 &\xrightarrow{k_4} \text{GOx(FAD)} + \text{H}_2\text{O}_2 \quad &v_3 \\ \text{H}_2\text{O}_2 &\xrightarrow{k_5} \text{O}_2 + 2\text{H}^+ + 2e^- \quad &v_4 \end{align} \]

Here, k₁–k₅ represent the rate constants for glucose binding (k₁), dissociation (k₂), catalytic conversion (k₃), cofactor reoxidation (k₄), and H₂O₂ electrooxidation (k₅), respectively; v₁–v₄ denote the corresponding reaction rates. Glu denotes glucose, GOx(FAD) is the oxidized form of the enzyme, [Glu...GOx] represents the enzyme–substrate complex, Glt is gluconolactone, and GOx(FADH₂) corresponds to the reduced form of the enzyme.

In many simplified models, it is assumed that the enzyme-substrate complex reaches a steady state instantly. Hence, \( \frac{d[\mathrm{Glu\!...\!GOx}]}{dt} \) is approximated as zero, leading to the classical Michaelis–Menten approximation. However, this assumption does not accurately describe the reaction kinetics for the following reasons:

1. Mass-transport limitations: Diffusion of glucose and oxygen to the immobilised enzyme can limit reaction rates.
2. Neglect of transient response: The steady-state approximation ignores the transient formation and decay of the enzyme-substrate.
3. Limited concentration range: Michaelis-Mentel equation assumes a substrate concentration much greater than enzyme concentration.[1,2]

For an accurate sensor modelling, it is paramount to solve the full time-dependent system rather than imposing \( \frac{d[\mathrm{Glu\!...\!GOx}]}{dt}=0 \).

Modelling the sensor

Species like \([{\rm Glu}]\), \([{\rm Glt}]\), \([{\rm H_2O_2}]\) and \([{\rm O_2}]\), can diffuse from the bulk towards the immobilized enzyme or vice versa. In this case, we use Fick's second law:

\[ \frac{\partial [C_i](x,t)}{\partial t} \;=\; D_i\,\frac{\partial^2 [C_i](x,t)}{\partial x^2}, \qquad x>0,\; t>0, \]

At the electrode surface (x = 0), the dynamics are described by flux boundary conditions. Here we use the convention that the flux is positive when it flows into the electrode. The surface fluxes are:

\[ \begin{aligned} -\,D_{\mathrm{Glu}}\left.\frac{\partial [\mathrm{Glu}]}{\partial x}\right|_{x=0} &= \nu_1\\[6pt] \frac{d[\mathrm{GOx(FAD)}]}{dt} &= \nu_1 + \nu_3\\[6pt] -\,D_{\mathrm{Glt}}\left.\frac{\partial [\mathrm{Glt}]}{\partial x}\right|_{x=0} &= -\nu_1\\[6pt] -\,D_{\mathrm{H_{2}O_{2}}}\left.\frac{\partial [\mathrm{H_{2}O_{2}}]}{\partial x}\right|_{x=0} &= \nu_4 - \nu_3\\[6pt] -\,D_{\mathrm{O_{2}}}\left.\frac{\partial [\mathrm{O_{2}}]}{\partial x}\right|_{x=0} &= \nu_3 - \nu_4 \end{aligned} \]

Here \(\nu_1\)–\(\nu_4\) are the surface reaction rates defined in the reaction scheme. Whereas the flux very far from the electrode is set to zero. In contrast the immobilized enzyme and related species, are not affected by diffusion, and their dynamic are:

\[ \begin{aligned} \frac{d[\mathrm{GOx(FAD)}]}{dt} &= -\nu_1 + \nu_3\\[6pt] \frac{d[\mathrm{Glu\!...\!GOx}]}{dt} &= \nu_1 - \nu_2\\[6pt] \frac{d[\mathrm{GOx(FADH_2)}]}{dt} &= \nu_2 - \nu_3\\[6pt] \end{aligned} \]

In an amperometric mode, the electrode potential is held constant at a value where H₂O₂ is effectively fully oxidised. Consequently, the oxidation kinetics can be represented by a constant rate constant \(k_5\), so \(\nu_4(t)\) is mainly controlled by mass transport. The measured current is related to the H₂O₂ flux at the electrode interface:

\[ i(t) \;=\; -\,n F D_{\mathrm{H_2O_2}}\left.\frac{\partial [\mathrm{H_2O_2}]}{\partial x}\right|_{x=0}, \]

where \(n\) is the number of electrons transferred per H₂O₂ molecule and \(F\) is the Faraday constant.

Breaking Down Chronoamperometry: Glucose and Oxygen effect

As mentioned above, the current is proportionally to the concentration. In chronoamperometry, the steady-state current (the value reached after the transient decay) is usually taken as the analytical signal. However, this linearity fails under specific conditions. Two cases are discussed below: glucose concentration and oxygen concentration.

Glucose concentration.

The effect of glucose concentration is shown in the animation below. The left panel shows how the amperometric signal increases as the glucose concentration rises, while the right panel displays the interfacial concentrations of the enzyme in its reduced and oxidised states. As the animation indicates, the steady-state current initially increases proportionally with glucose concentration; however, once the enzyme becomes satured, the reduce form (GOx(FADH₂)) dominates. Consequently, the current does not reach steady state conditions, and the linear relationship with glucose concentration fails.

Animation: Effect of glucose concentration on amperometric current (left) and enzyme states (right).

The steady state current is achieved when the rate of H₂O₂ oxidation matches the rate at which glucose diffuses from the bulk towards the electrode surface. When the enzyme becomes saturated, glucose starts to accumulate close to the electrode’s surface because the enzymatic process becomes the rate-limiting step. The animation below visualizes the formation of the diffusion layer and the subsequent accumulation once its concentration is high enough to exceed the enzyme’s catalytic capacity.

Animation: Formation of the glucose diffusion layer as a function of glucose concentration.

Oxygen concentration

Oxygen plays a key role in the glucose biosensor, as it completes the catalytic cycle of the glucose oxidase (GOx). Its influence, however, depends on the enzyme’s interfacial concentration, and the rate at which GOx(FADH₂) is produced. A decrease in oxygen concentration leads to a reduction in the steady-state current, as illustrated in the animation below.

Animation: Effect of oxygen concentration on the amperometric current of a glucose biosensor.

Glucose biosensor simulator

The following interactive app simulates the amperometric response of the glucose biosensor and the interfacial concentration of the oxidised and reduced form the glucose oxidase (GOx). You can explore the effect of various parameters on the amperometric response and catalytic rate:

• Glucose binding rate (k₁)
• Complex dissociation rate (k₂)
• Catalytic rate constant (k₃)
• Enzyme oxidation rate (k₄)
• H₂O₂ oxidation rate (k₅)
• Glucose concentration
• Enzyme interfacial concentration
• Oxygen concentration

By adjusting these parameters, you can gain deeper understanding of how adsorption affect a cyclic voltammogram. In addition, the plots can be exported as CSV files.

References

1. Kim, J.K., Tyson, J.J. (2020). Misuse of the Michaelis–Menten rate law for protein interaction networks and its remedy. PLOS Computational Biology. 16(10): e1008258. https://doi.org/10.1371/journal.pcbi.1008258.
2. Choi, B., Rempala, G.A., Kim, J.K. (2017). Beyond the Michaelis–Menten equation: Accurate and efficient estimation of enzyme kinetic parameters. Scientific Reports. 7(1): 17018. https://doi.org/10.1038/s41598-017-17072-z.

If you want to cite this blog post, use:
Robayo, I. (2025). How Glucose Sensors Work: From Enzyme Reactions to Electrical Signals. Available at: https://electrochemeisbasics.blogspot.com/2025/04/understanding-surface-adsorption-in.html [Accessed Date Accessed].