Computational Modelling of the Behaviour of Potentiometric Membrane Biosensors

This paper presents a mathematical model of a potentiometric biosensor based on a potentiometric electrode covered with an enzyme membrane. The model is based on the reaction–diffusion equations containing a non-linear term related to theMichaelis–Menten kinetics of the enzymatic reaction. Using computer simulation the influence of the thickness of the enzyme membrane on the biosensor response was investigated. The digital simulation was performed using the finite difference technique. Results of the numerical simulation were compared with known analytical solutions.      

Modelling of Amperometric Biosensors with Rough Surface of the Enzyme Membrane

A two-dimensional-in-space mathematical model of amperometric biosensors has been developed. The model is based on the diffusion equations containing a nonlinear term related to the Michaelis–Menten kinetic of the enzymatic reaction. The model takes into consideration two types of roughness of the upper surface (bulk solution/membrane interface) of the enzyme membrane, immobilised onto an electrode. Using digital simulation, the influence of the geometry of the roughness on the biosensor response was investigated. Digital simulation was carried out using the finite-difference technique.     

Computational Modelling of Biosensors with Perforated and Selective Membranes

This paper presents a two-dimensional-in-space mathematical model of amperometric biosensors with perforated and selective membranes. The model is based on the diffusion equations containing a non-linear term of the Michaelis–Menten enzymatic reaction. Using numerical simulation of the biosensors action, the influence of the geometry of the perforated membrane on the biosensor response was investigated. The numerical simulation was carried out using finite-difference technique. The calculations demonstrated non-linear and non-monotonous change of the biosensor steady-state current at various degree of the surface of the perforated membrane covering. The non-monotonous behaviour of the biosensor response was also observed when changing the thickness of the perforated membrane.     

Computational modelling of amperometric biosensors in the case of substrate and product inhibition

In this paper the response of an amperometric biosensor at mixed enzyme kinetics and diffusion limitations is modelled in the case of the substrate and the product inhibition. The model is based on non-stationary reaction–diffusion equations containing a non-linear term related to non-Michaelis–Menten kinetics of an enzymatic reaction. A numerical simulation was carried out using a finite difference technique. The complex enzyme kinetics produced different calibration curves for the response at the transition and the steady-state. The biosensor operation is analysed with a special emphasis to the conditions at which the biosensor response change shows a maximal value. The dependence of the biosensor sensitivity on the biosensor configuration is also investigated. Results of the simulation are compared with known analytical results and with previously conducted researches on the biosensors.     

An Analysis of Mixtures Using Amperometric Biosensors and Artificial Neural Networks

This paper presents a sensor system based on a combination of an amperometric biosensor acting in batch as well as flow injection analysis with the chemometric data analysis of biosensor outputs. The multivariate calibration of the biosensor signal was performed using artificial neural networks. Large amounts of biosensor calibration as well as test data were synthesized using computer simulation. Mathematical and corresponding numerical models of amperometric biosensors have been built to simulate the biosensor response to mixtures of compounds. The mathematical model is based on diffusion equations containing a non-linear term related to Michaelis–Menten kinetics of the enzymatic reaction. The principal component analysis was applied for an optimization of calibration data. Artificial neural networks were used to discriminate compounds of mixtures and to estimate the concentration of each compound. The proposed approach showed prediction of each component with recoveries greater that 99% in flow injection as well as in batch analysis when the biosensor response is under diffusion control.     

Modelling synergistic action of laccase-based biosensor utilizing simultaneous substrates conversion

The response of a laccase-based amperometric biosensor that acts in a synergistic manner was modelled digitally. A mathematical model of the biosensor is based on a system of non-linear reaction diffusion equations. The modelling biosensor comprises three compartments, an enzyme layer, a dialysis membrane and an outer diffusion layer. By changing input parameters the biosensor action was analysed with a special emphasis to the influence of the species concentrations on the synergy of the simultaneous substrates conversion. The digital simulation was carried out using the finite difference technique.     

Mechanisms controlling the sensitivity of amperometric biosensors in flow injection analysis systems

This paper numerically investigates the sensitivity of an amperometric biosensor acting in the flow injection mode when the biosensor contacts an analyte for a short time. The analytical system is modelled by non-stationary reaction-diffusion equations containing a non-linear term related to the Michaelis-Menten kinetics of an enzymatic reaction. The mathematical model involves three regions: the enzyme layer where enzymatic reaction as well as the mass transport by diffusion takes place, a diffusion limiting region where only the diffusion takes place, and a convective region. The biosensor operation is analysed with a special emphasis to the conditions at which the biosensor sensitivity can be increased and the calibration curve can be prolonged by changing the injection duration, the permeability of the external diffusion layer, the thickness of the enzyme layer and the catalytic activity of the enzyme. The apparent Michaelis constant is used as a main characteristic of the sensitivity and the calibration curve of the biosensor. The numerical simulation was carried out using the finite difference technique.     

The Effect of Diffusion Limitations on the Response of Amperometric Biosensors with Substrate Cyclic Conversion

A mathematical model of amperometric biosensors in which chemical amplification by cyclic substrate conversion takes place in a single enzyme membrane has been developed. The model involves three regions: the enzyme layer where enzyme reaction as well as mass transport by diffusion takes place, a diffusion limiting region where only the diffusion takes place, and a convective region where the analyte concentration is maintained constant. Using computer simulation the influence of the thicknesses of the enzyme layer and the diffusion region on the biosensor response was investigated. This paper deals with conditions when the mass transport in the exterior region may be neglected to simulate the biosensor response in a well-stirred solution. The digital simulation was carried out using the finite difference technique.     

酶生物传感器中酶的固定化技术

综述了近年来国内外酶生物传感器的进展,介绍了制作酶生物传感器的关键技术——酶的固定化。固定化方法主要有吸附法、包埋法、共价键合法和交联法。固定化材料分为无机材料、有机聚合物材料、凝胶以及生物材料等。探讨了固定化方法和固定化材料对酶的固定化及酶生物传感器性能的影响,并结合自己的工作展望了酶生物传感器的发展方向和趋势。     

Mathematical Model and Numerical Simulation of Conductometric Biosensor of Urea

In this article, a mathematical model was developed to describe and optimize the configuration of the urea biosensor. The biosensor is based on interdigitated gold microelectrodes modified with a urease enzyme membrane. The model presented here focuses on the enzymatic reaction and/or diffusion phenomena that occur in the enzyme membrane and in the diffusion layer. Numerical resolution of differential equations was performed using the finite difference technique. The mathematical model was validated using experimental biosensor data. The responses of the biosensor to various conditions were simulated to guide experiments, improve analytical performance, and reduce development costs.     

Amperometric Determination of Hydrogen Peroxide and its Mathematical Simulation for Horseradish Peroxidase Immobilized on a Sonogel Carbon Electrode

A mathematical model of a horseradish peroxidase biosensor was applied to simulate the amperometric response for the detection of hydrogen peroxide. The development of the mathematical model was based on the Michaelis–Menten equation and Fick’s Second Law. The theoretical study is based on the determination of physico-chemical and geometric parameters of a horseradish peroxidase biosensor as well as the kinetic parameters of reaction mechanism such as diffusion coefficients of hydrogen peroxide, the thickness of enzymatic layer, and the Michaelis–Menten kinetic constant. The theoretical analysis provides an accurate estimate of parameters affecting the biosensor performance such as the diffusion coefficient of hydrogen peroxide in the biomembrane that was estimated to be 56?×?10^?12 m²/s. The thickness of diffusion layer was estimated to be 80–100?µm and the biomembrane 7.5?µm. The experimental and numerical values of kinetic parameters were 0.92 and 0.98?µM for the Michaelis–Menten constants and 0.010 and 0.012?µM/s for the catalytic activity rates. The model was validated for hydrogen peroxide detection and exhibited a good agreement with the experimental measurements.     

Structure simulation into a lamellar supramolecular network and calculation of the metal ions/ligands ratio

Background

Biosensors have attracted increasing attention as reliable analytical instruments in in situ monitoring of public health and environmental pollution. For enzyme-based biosensors, the stabilization of enzymatic activity on the biological recognition element is of great importance. It is generally acknowledged that an effective immobilization technique is a key step to achieve the construction quality of biosensors.

Results

A novel disposable biosensor was constructed by immobilizing laccase (Lac) with silica spheres on the surface of multi-walled carbon nanotubes (MWCNTs)-doped screen-printed electrode (SPE). Then, it was characterized in morphology and electrochemical properties by scanning electron microscopy (SEM) and cyclic voltammetry (CV). The characterization results indicated that a high loading of Lac and a good electrocatalytic activity could be obtained, attributing to the porous structure, large specific area and good biocompatibility of silica spheres and MWCNTs. Furthermore, the electrochemical sensing properties of the constructed biosensor were investigated by choosing dopamine (DA) as the typical model of phenolic compounds. It was shown that the biosensor displays a good linearity in the range from 1.3 to 85.5 ??M with a detection limit of 0.42 ??M (S/N = 3), and the Michaelis-Menten constant (K_m ^app) was calculated to be 3.78 ??M.

Conclusion

The immobilization of Lac was successfully achieved with silica spheres to construct a disposable biosensor on the MWCNTs-doped SPE (MWCNTs/SPE). This biosensor could determine DA based on a non-oxidative mechanism in a rapid, selective and sensitive way. Besides, the developed biosensor could retain high enzymatic activity and possess good stability without cross-linking reagents. The proposed immobilization approach and the constructed biosensor offer a great potential for the fabrication of the enzyme-based biosensors and the analysis of phenolic compounds.     

A microconductometric biosensor based on lipase extracted from Candida rugosa for direct and rapid detection of organophosphate pesticides

Organophosphate pesticides (OPs) have been intensively used as insecticides in agriculture; after entering the aquatic environment, they may affect a wide range of organisms. A conductometric enzymatic biosensor based on lipase extracted from Candida rugosa (CRL) has therefore been developed for the direct and rapid quantitative detection of organophosphate pesticides: diazinon, methyl parathion and methyl paraoxon in water. The biosensor signal and response time were obtained under optimum conditions, the enzyme being immobilised in the presence of gold nanoparticles. Under these conditions, the enzymatic biosensor was able to measure concentrations as low as 60 µg/L of diazinon, 26 µg/L of methyl parathion and 25 µg/L of methyl paraoxon very rapidly (response time: 3 min). Moreover, this CRL biosensor was not sensitive to interferences such as carbamates. It presented good storage stability for 21 days when kept at 4°C and it was successfully applied to real samples.     

Analytical expression of non-steady-state concentrations and current pertaining to compounds present in the enzyme membrane of biosensor

A mathematical model of trienzyme biosensor at an internal diffusion limitation for a non-steady-state condition has been developed. The model is based on diffusion equations containing a linear term related to Michaelis-Menten kinetics of the enzymatic reaction. Analytical expressions of concentrations and current of compounds in trienzyme membrane are derived. An excellent agreement with simulation data is noted. When time tends to infinity, the analytical expression of non-steady-state concentration and current approaches the steady-state value, thereby confirming the validity of the mathematical analysis. Furthermore, in this work we employ the complex inversion formula to solve the boundary value problem.     

Combined affinity and catalytic biosensor: in situ enzymatic activity monitoring of surface-bound enzymes

Surface Plasmon Resonance Spectroscopy (SPR) and miniature Fiber Optic Absorbance Spectroscopy (FOAS) were combined to monitor in situ and quantitatively an enzymatic model reaction catalyzed by beta-lactamase. The enzyme was covalently immobilized to the gold surface of a SPR chip, which was functionalized with NeutrAvidin through a biotinylated alkanethiol self-assembled monolayer, thus serving as a highly sensitive affinity biosensor. SPR was used to control the density of the surface-bound enzyme. Nitrocefin as the enzymatic substrate was allowed to react with the immobilized enzyme in the SPR flow cell, and its turnover was detected with the FOAS system acting as the catalytic biosensor. The coupling of the two techniques has a substantial potential for highly controlled on-line monitoring of surface-bound enzyme activity. The FOAS technique may also be easily employed as an add-on device to other types of affinity sensing instruments.     

Mathematical Modelling of a Non‐enzymatic Amperometric Electrochemical Biosensor for Cholesterol

Thickness of the electro‐polymerized layer grown on a substrate and used as the recognition element for the analyte is critical to measuring the response of a biosensor, with high sensitivity and accuracy. However, it is difficult to control the thickness during synthesis. A mathematical model is developed in this study that considers thickness of the electro‐polymerized layer in simulating the electrochemical response of a non‐enzymatic biosensor for cholesterol in blood. The model includes transient kinetics and one‐dimensional diffusion of the analyte in the poly‐methyl orange (PMO) recognition layer electrochemically grown on the electrode. The governing partial differential equations resulting from the species conservation balances in the PMO layer are numerically solved. Time and spatial concentration profiles of the analyte in the PMO layer are determined. Model predictions are calibrated with the experimental data for different PMO thicknesses. Interestingly, model predictions show a linear response over the calibrated concentration range of cholesterol for all PMO layer thicknesses. Based on the chronoamperometry measurements, the model predictions for the cholesterol concentrations measured in the laboratory samples were also found to be remarkably accurate. This is the first mathematical model developed to understand the transport and kinetics of an analyte in the electro‐polymerized layer used as the recognition element of a non‐enzymatic biosensor.     

Construction and Application of Flow Enzymatic Biosensor Based of Silver Solid Amalgam Electrode for Determination of Sarcosine

In this paper, the flow amperometric enzymatic biosensor based on polished silver solid amalgam electrode for determination of sarcosine in model sample under flow injection analysis conditions is presented. The biosensor works on principle of electrochemical detection of oxygen decrease during enzymatic reaction which is directly proportional to the concentration of sarcosine in sample. The whole preparation process takes about 3 h. The RSD of repeatability of 10 consecutive measurements is 1.6 % (c_sarcosine=1.0×10^?4 mol dm^?3). Under optimal conditions the calibration dependence was linear in the range 7.5×10^?6–5.0×10^?4 mol dm^?3 and limit of detection was 2.0×10^?6 mol dm^?3.     

Core-shell Hierarchical Enzymatic Biosensor Based on Hyaluronic Acid Capped Copper Ferrite Nanoparticles for Determination of Endocrine-disrupting Bisphenol A

A novel enzymatic electrochemical biosensor (mCuF/PANI-nf/HA/Lacc/GCE) was designed for detection of bisphenol A (BPA). The copper ferrite nanoparticles was obtained by co-precipitation and its surface was modified with -NH₂ functional organosilane. Polyaniline nanofibers were also synthesized by cyclic voltammetry and characterized by FTIR, XRD, TGA, SEM and TEM, respectively. Then, it was crosslinked with hyaluronic acid as an immobilization matrix for Laccase to adhere to surface of the modified copper ferrites. Cyclic and differential pulse voltammetries were used to evaluate the electrochemical performances of the biosensor, which has a LOD value of 5.40 nM and a LOQ value of 16.20 nM in the 0.01–7.50 μM linear working range. The biosensor was successfully applied for determination of BPA in seawater, canned water and milk samples with recoveries ranging from 96.0 % and 100.7 %. In addition, accuracy of the voltammetric determination method in the real samples was carried out by HPLC and spike/recovery test. The layer-by-layer surface modification strategy of the designed mCuF/PANI-nf/HA/Lacc/GCE biosensor opens a new perspective on both BPA determination and using biopolymer in the structure of enzymatic electrochemical biosensors.     

Glucose biosensor based on a platinum electrode modified with rhodium nanoparticles and with glucose oxidase immobilized on gold nanoparticles

We have developed an enzymatic glucose biosensor that is based on a flat platinum electrode which was covered with electrophoretically deposited rhodium (Rh) nanoparticles and then sintered to form a large surface area. The biosensor was obtained by depositing glucose oxidase (GOx), Nafion, and gold nanoparticles (AuNPs) on the Rh electrode. The electrical potential and the fractions of Nafion and GOx were optimized. The resulting biosensor has a very high sensitivity (68.1 μA mM^?1 cm^?2) and good linearity in the range from 0.05 to 15 mM (r?=?0.989). The limit of detection is as low as 0.03 mM (at an SNR of 3). The glucose biosensor also is quite selective and is not interfered by electroactive substances including ascorbic acid, uric acid and acetaminophen. The lifespan is up to 90 days. It was applied to the determination of glucose in blood serum, and the results compare very well with those obtained with a clinical analyzer.

Studies on simulated ageing of paper by photochemical degradation

The study of the ageing of two paper types was performed on monitoring it during a simulated process by means of the measure of the photocatalytic degradation of the paper cellulose. Such evaluation was possible due to the combined responses of a photosensor based on titanium dioxide in suspension, of an enzymatic biosensor based on superoxide dismutase (SOD), of a specific conductivity sensor and of an enzymatic biosensor based on glucose oxidase.