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Modelling Carbon Nanotubes-Based Mediatorless Biosensor

This paper presents a mathematical model of carbon nanotubes-based mediatorless biosensor. The developed model is based on nonlinear non-stationary reaction-diffusion equations. The model involves four layers (compartments): a layer of enzyme solution entrapped on a terylene membrane, a layer of the...

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Detalles Bibliográficos
Autores principales: Baronas, Romas, Kulys, Juozas, Petrauskas, Karolis, Razumiene, Julija
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444095/
https://www.ncbi.nlm.nih.gov/pubmed/23012537
http://dx.doi.org/10.3390/s120709146
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author Baronas, Romas
Kulys, Juozas
Petrauskas, Karolis
Razumiene, Julija
author_facet Baronas, Romas
Kulys, Juozas
Petrauskas, Karolis
Razumiene, Julija
author_sort Baronas, Romas
collection PubMed
description This paper presents a mathematical model of carbon nanotubes-based mediatorless biosensor. The developed model is based on nonlinear non-stationary reaction-diffusion equations. The model involves four layers (compartments): a layer of enzyme solution entrapped on a terylene membrane, a layer of the single walled carbon nanotubes deposited on a perforated membrane, and an outer diffusion layer. The biosensor response and sensitivity are investigated by changing the model parameters with a special emphasis on the mediatorless transfer of the electrons in the layer of the enzyme-loaded carbon nanotubes. The numerical simulation at transient and steady state conditions was carried out using the finite difference technique. The mathematical model and the numerical solution were validated by experimental data. The obtained agreement between the simulation results and the experimental data was admissible at different concentrations of the substrate.
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spelling pubmed-34440952012-09-25 Modelling Carbon Nanotubes-Based Mediatorless Biosensor Baronas, Romas Kulys, Juozas Petrauskas, Karolis Razumiene, Julija Sensors (Basel) Article This paper presents a mathematical model of carbon nanotubes-based mediatorless biosensor. The developed model is based on nonlinear non-stationary reaction-diffusion equations. The model involves four layers (compartments): a layer of enzyme solution entrapped on a terylene membrane, a layer of the single walled carbon nanotubes deposited on a perforated membrane, and an outer diffusion layer. The biosensor response and sensitivity are investigated by changing the model parameters with a special emphasis on the mediatorless transfer of the electrons in the layer of the enzyme-loaded carbon nanotubes. The numerical simulation at transient and steady state conditions was carried out using the finite difference technique. The mathematical model and the numerical solution were validated by experimental data. The obtained agreement between the simulation results and the experimental data was admissible at different concentrations of the substrate. Molecular Diversity Preservation International (MDPI) 2012-07-03 /pmc/articles/PMC3444095/ /pubmed/23012537 http://dx.doi.org/10.3390/s120709146 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Baronas, Romas
Kulys, Juozas
Petrauskas, Karolis
Razumiene, Julija
Modelling Carbon Nanotubes-Based Mediatorless Biosensor
title Modelling Carbon Nanotubes-Based Mediatorless Biosensor
title_full Modelling Carbon Nanotubes-Based Mediatorless Biosensor
title_fullStr Modelling Carbon Nanotubes-Based Mediatorless Biosensor
title_full_unstemmed Modelling Carbon Nanotubes-Based Mediatorless Biosensor
title_short Modelling Carbon Nanotubes-Based Mediatorless Biosensor
title_sort modelling carbon nanotubes-based mediatorless biosensor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444095/
https://www.ncbi.nlm.nih.gov/pubmed/23012537
http://dx.doi.org/10.3390/s120709146
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