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Mathematical Modeling of Biosensors
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Mathematical Modeling of Biosensors

Second Edition 2021

Book Details

Format Hardback or Cased Book
ISBN-10 3030655040
ISBN-13 9783030655044
Edition Second Edition 2021
Publisher Springer Nature Switzerland AG
Imprint Springer Nature Switzerland AG
Country of Manufacture GB
Country of Publication GB
Publication Date Feb 16th, 2021
Print length 456 Pages
Ksh 28,800.00
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This newly designed and enlarged edition offers an up-to-date presentation of biosensor development and modeling from both a chemical and a mathematical point of view. The action of single enzyme as well as polyenzyme biosensors and biosensors based on chemically modified electrodes is studied.

This newly designed and enlarged edition offers an up-to-date presentation of biosensor development and modeling from both a chemical and a mathematical point of view. An entire new chapter in particular is dedicated to optimal design of biosensors. Two more new chapters discuss biosensors which utilize microbial cells and are based on carbon nanotubes respectively. All the other chapters have been revised and updated. 

 The book contains unique modeling methods for amperometric, potentiometric and optical  biosensors based mainly on biocatalysts . It examines processes that occur in the sensors'' layers and at their interface, and it provides analytical and numerical methods to solve equations of conjugated enzymatic (chemical) and diffusion processes. The action of single enzyme as well as polyenzyme biosensors and biosensors based on chemically modified electrodes is studied. 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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