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Generalized Analytical Model for Enzymatic BioFET Transistors

Software tools that are able to simulate the functionality or interactions of an enzyme biosensor with Metal Oxide Semiconductor (MOS), or any Field Effect Transistor (FET) as transducer, represent a gap in the market. Bio-devices, or Enzyme-FET, cannot be simulated by Atlas or equivalent software....

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Detalles Bibliográficos
Autores principales: Ravariu, Cristian, Srinivasulu, Avireni, Mihaiescu, Dan Eduard, Musala, Sarada
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313200/
https://www.ncbi.nlm.nih.gov/pubmed/35884278
http://dx.doi.org/10.3390/bios12070474
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author Ravariu, Cristian
Srinivasulu, Avireni
Mihaiescu, Dan Eduard
Musala, Sarada
author_facet Ravariu, Cristian
Srinivasulu, Avireni
Mihaiescu, Dan Eduard
Musala, Sarada
author_sort Ravariu, Cristian
collection PubMed
description Software tools that are able to simulate the functionality or interactions of an enzyme biosensor with Metal Oxide Semiconductor (MOS), or any Field Effect Transistor (FET) as transducer, represent a gap in the market. Bio-devices, or Enzyme-FET, cannot be simulated by Atlas or equivalent software. This paper resolves this issue for the enzymatic block coupled with FETs’ role within biosensors. The first block has the concentration of biological analyte as the input signal and concentration of ions from the enzymatic reaction as the output signal. The modeling begins from the Michaelis–Menten formalism and analyzes the time dependence of the product concentrations that become the input signal for the next FET block. Comparisons within experimental data are provided. The analytical model proposed in this paper represents a general analytical tool in the design stage for enzymatic transistors used in clinical practices.
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spelling pubmed-93132002022-07-26 Generalized Analytical Model for Enzymatic BioFET Transistors Ravariu, Cristian Srinivasulu, Avireni Mihaiescu, Dan Eduard Musala, Sarada Biosensors (Basel) Communication Software tools that are able to simulate the functionality or interactions of an enzyme biosensor with Metal Oxide Semiconductor (MOS), or any Field Effect Transistor (FET) as transducer, represent a gap in the market. Bio-devices, or Enzyme-FET, cannot be simulated by Atlas or equivalent software. This paper resolves this issue for the enzymatic block coupled with FETs’ role within biosensors. The first block has the concentration of biological analyte as the input signal and concentration of ions from the enzymatic reaction as the output signal. The modeling begins from the Michaelis–Menten formalism and analyzes the time dependence of the product concentrations that become the input signal for the next FET block. Comparisons within experimental data are provided. The analytical model proposed in this paper represents a general analytical tool in the design stage for enzymatic transistors used in clinical practices. MDPI 2022-06-30 /pmc/articles/PMC9313200/ /pubmed/35884278 http://dx.doi.org/10.3390/bios12070474 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Ravariu, Cristian
Srinivasulu, Avireni
Mihaiescu, Dan Eduard
Musala, Sarada
Generalized Analytical Model for Enzymatic BioFET Transistors
title Generalized Analytical Model for Enzymatic BioFET Transistors
title_full Generalized Analytical Model for Enzymatic BioFET Transistors
title_fullStr Generalized Analytical Model for Enzymatic BioFET Transistors
title_full_unstemmed Generalized Analytical Model for Enzymatic BioFET Transistors
title_short Generalized Analytical Model for Enzymatic BioFET Transistors
title_sort generalized analytical model for enzymatic biofet transistors
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313200/
https://www.ncbi.nlm.nih.gov/pubmed/35884278
http://dx.doi.org/10.3390/bios12070474
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