Mathematical modeling and experimental results of a sandwich-type amperometric biosensor

Romero, Marcelo Ricardo; Baruzzi, Ana María; Garay, Fernando

doi:10.1016/j.snb.2011.12.079

Cited by 33 publications

(36 citation statements)

References 35 publications

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“…According to the optimal conditions found in the previous experiments, a calibration curve was measured to determine the range in which the analytical The analytical performance of the bioelectrode was compared with other glucose biosensors prepared with GOx, Table 1. The LOD obtained from Pt-CNT-muc 50% was similar that of some recently reported amperometric biosensors based on the immobilization of GOx [12,[22][23][24][25][26][27][28]. It is necessary to emphasize that the biosensor reported here has excellent interval of linear behavior since the linear dependence involves more than 3 orders of magnitude.…”

Section: Detection Limit and Linear Interval Of The Proposed Biosensorsupporting

confidence: 86%

Mucin and carbon nanotube-based biosensor for detection of glucose in human plasma

Comba

Romero

Garay

et al. 2018

Analytical Biochemistry

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This work reports an amperometric enzyme-electrode prepared with glucose oxidase, which have been immobilized by a cross-linking step with glutaraldehyde in a mixture containing albumin and a novel carbon nanotubes-mucin composite (CNT-muc). The obtained hydrogel matrix was trapped between two polycarbonate membranes and then fixed at the surface of a Pt working electrode. The developed biosensor was optimized by evaluating different compositions and the analytical properties of an enzymatic matrix with CNT-muc. Then, the performance of the resulting enzymatic matrix was evaluated for direct glucose quantification in human blood plasma. The novel CNT-muc composite provided a sensitivity of 0.44 ± 0.01 mA M and a response time of 28 ± 2 s. These values were respectively 20% higher and 40% shorter than those obtained with a sandwich-type biosensor prepared without CNT. Additionally, CNT-muc based biosensor exhibited more than 3 orders of magnitude of linear dynamic calibration range and a detection limit of 3 μM. The short-term and long-term stabilities of the biosensors were also examined and excellent results were obtained through successive experiments performed within the first 60 days from their preparation. Finally, the storage stability was remarkable during the first 300 days.

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Section: Detection Limit and Linear Interval Of The Proposed Biosensorsupporting

confidence: 86%

Mucin and carbon nanotube-based biosensor for detection of glucose in human plasma

Comba

Romero

Garay

et al. 2018

Analytical Biochemistry

Self Cite

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“…Recent developments in numerical techniques and increasing computational powers, on the other hand, imply that numerical solutions can be obtained with a high degree of precision, making the numerical techniques convenient to solve complex problems, regardless of the existence of the corresponding analytical solutions . Several numerical models have been developed for different configurations of biosensors, for instance, simple diffusion , sandwich , and multi‐layers . These models have helped to study the effects of various conditions such as electrode film thickness and enzyme loading, on the sensor responses .…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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“…Hence, a mathematical model based on the use of reaction-diffusion equations was developed. This type of mathematical model is widely exploited in biosensor design (Štikonien et al, 2010;Romero et al, 2012;Meškauskas et al, 2013). Finally, possible modifications to the sensing layer in the light of the theoretical results are discussed.…”

Section: Introductionmentioning

confidence: 99%

Enzymatic sensor of putrescine with optical oxygen transducer – mathematical model of responses of sensitive layer

et al. 2015

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A biosensor for putrescine containing a sensing layer with an optical oxygen probe based on ruthenium complex and the enzyme diamine oxidase from pea is described. The diamine oxidase was pre-immobilised on broken micro-beads modified with a ferrofluid. The pre-immobilised enzyme and ruthenium complex were both incorporated into the UV-cured inorganic-organic hybrid polymer ORMOCER and deposited on a lens to form a sensitive layer of 210 µm in thickness. The sensitivity to the putrescine concentration determined under air saturation was between 3.50 µs L mmol −1 and 4.50 µs L mmol −1 in a hundred experiments conducted intermittently over a one year period. With the oxygen concentration increasing from 10 % to 100 % of DO (dissolved oxygen), the biosensor sensitivity decreased from 6.87 µs L mmol −1 to 0.70 µs L mmol −1 and its dynamic range increased from 0.10 mmol L −1 to 1.75 mmol L −1 . To estimate the behaviour of the putrescine sensor in parametric space, a mathematical model of the reaction-transport processes inside the sensing layer was developed. The model revealed the qualitative relations between the sensor analytical features, the characteristics of the sensitive layer and concentrations of substrates. The results of the mathematical modelling may serve as guidelines in the design of optodes for specific applications.

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Mathematical modeling and experimental results of a sandwich-type amperometric biosensor

Cited by 33 publications

References 35 publications

Mucin and carbon nanotube-based biosensor for detection of glucose in human plasma

Mucin and carbon nanotube-based biosensor for detection of glucose in human plasma

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

Enzymatic sensor of putrescine with optical oxygen transducer – mathematical model of responses of sensitive layer

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