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Mathematical Modeling of Biosensors [electronic resource] :An Introduction for Chemists and Mathematicians / by Romas Baronas, Feliksas Ivanauskas, Juozas Kulys.

by Baronas, Romas [author.]; Ivanauskas, Feliksas [author.]; Kulys, Juozas [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Springer Series on Chemical Sensors and Biosensors, Methods and Applications: 9Publisher: Dordrecht : Springer Netherlands, 2010.Description: XIX, 334p. online resource.ISBN: 9789048132430.Subject(s): Chemistry | Biochemical engineering | Chemistry -- Mathematics | Computer simulation | Computer science -- Mathematics | Mathematical physics | Chemistry | Math. Applications in Chemistry | Computational Mathematics and Numerical Analysis | Computer Applications in Chemistry | Mathematical Methods in Physics | Biochemical Engineering | Simulation and ModelingDDC classification: 541.2 Online resources: Click here to access online
Contents:
Analytical Modeling of Biosensors -- Biosensor Action -- Modeling Biosensors at Steady State and Internal Diffusion Limitations -- Modeling Biosensors at Steady State and External Diffusion Limitations -- Modeling Biosensors Utilizing Microbial Cells -- Modeling Nonstationary State of Biosensors -- Numerical Modeling of Biosensors -- Mono-Layer Mono-Enzyme Models of Biosensors -- One-Layer Multi-Enzyme Models of Biosensors -- Multi-Layer Models of Biosensors -- Modeling Biosensors of Complex Geometry -- Numerical Methods for Reaction-Diffusion Equations -- The Difference Schemes for the Diffusion Equation -- The Difference Schemes for the Reaction–Diffusion Equations.
In: Springer eBooksSummary: This book presents biosensor development and modeling from both a chemical and a mathematical point of view. It contains unique modeling methods for catalytical (amperometric, potentiometer and optical) biosensors. It examines processes that occur in the sensors' layers and at their interface, and it provides analytical and numerical methods to solve enzymatic kinetic and diffusion equations. The action of single enzyme as well as polyenzyme biosensors is studied, and the modeling of biosensors that contain perforated membranes and multipart mass transport profiles is critically investigated. Furthermore, it is fully described how signals can be biochemically amplified, how cascades of enzymatic substrate conversion are triggered, and how signals are processed via a chemometric approach and artificial neuronal networks. The results of digital modeling are compared with both proximal analytical solutions and experimental data.
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Analytical Modeling of Biosensors -- Biosensor Action -- Modeling Biosensors at Steady State and Internal Diffusion Limitations -- Modeling Biosensors at Steady State and External Diffusion Limitations -- Modeling Biosensors Utilizing Microbial Cells -- Modeling Nonstationary State of Biosensors -- Numerical Modeling of Biosensors -- Mono-Layer Mono-Enzyme Models of Biosensors -- One-Layer Multi-Enzyme Models of Biosensors -- Multi-Layer Models of Biosensors -- Modeling Biosensors of Complex Geometry -- Numerical Methods for Reaction-Diffusion Equations -- The Difference Schemes for the Diffusion Equation -- The Difference Schemes for the Reaction–Diffusion Equations.

This book presents biosensor development and modeling from both a chemical and a mathematical point of view. It contains unique modeling methods for catalytical (amperometric, potentiometer and optical) biosensors. It examines processes that occur in the sensors' layers and at their interface, and it provides analytical and numerical methods to solve enzymatic kinetic and diffusion equations. The action of single enzyme as well as polyenzyme biosensors is studied, and the modeling of biosensors that contain perforated membranes and multipart mass transport profiles is critically investigated. Furthermore, it is fully described how signals can be biochemically amplified, how cascades of enzymatic substrate conversion are triggered, and how signals are processed via a chemometric approach and artificial neuronal networks. The results of digital modeling are compared with both proximal analytical solutions and experimental data.

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