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Modeling of hysteresis in immobilized enzymatic systems with applications in electrochemical sensing

P. Ashokan et al · Elsevier · 2026

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Understanding hysteresis behavior in immobilized enzymatic systems is crucial for improving the performance and reliability of electrochemical biosensors. This study aims to develop a mathematical model describing hysteresis in enzyme flow calorimetry systems used for electrochemical sensing applications. The model is based on the convection–diffusion equation with nonlinear substrate inhibition kinetics, assuming steady-state operation, uniform enzyme immobilization, and idealized planar, cylindrical, and spherical geometries. Nonlinear governing equations are solved using a combined analytical framework involving the Akbari–Ganji Method (AGM), Taylor Series Method (TSM), and Adomian Decomposition Method (ADM). Closed-form expressions for substrate concentration and effectiveness factors are obtained. The analytical solutions closely match numerical simulation results and offer an efficient framework for evaluating inhibition effects and improving the performance of immobilized enzymatic systems.

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APA 7

al, P. A. E. (2026). Modeling of hysteresis in immobilized enzymatic systems with applications in electrochemical sensing. https://doi.org/10.1016/j.ijoes.2026.101364

MLA

al, P. Ashokan et. "Modeling of hysteresis in immobilized enzymatic systems with applications in electrochemical sensing." 2026. https://doi.org/10.1016/j.ijoes.2026.101364.

Chicago

al, P. Ashokan et. 2026. "Modeling of hysteresis in immobilized enzymatic systems with applications in electrochemical sensing.". https://doi.org/10.1016/j.ijoes.2026.101364.

Harvard

al, P. A. E. 2026, Modeling of hysteresis in immobilized enzymatic systems with applications in electrochemical sensing, Elsevier, available at: https://doi.org/10.1016/j.ijoes.2026.101364 [Accessed 9 Aug. 2026].

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Title
Modeling of hysteresis in immobilized enzymatic systems with applications in electrochemical sensing
Author / contributors
P. Ashokan et al
Publisher
Elsevier
Publication year
2026
ISSN
1452-3981
ISSN
1452-3981
Language
English

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