Effect of Toxic Components on Microbial Fuel Cell-Polarization Curves and Estimation of the Type of Toxic Inhibition

Polarization curves are of paramount importance for the detection of toxic components in microbial fuel cell (MFC) based biosensors. In this study, polarization curves were made under non-toxic conditions and under toxic conditions after the addition of various concentrations of nickel, bentazon, so...

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Main Authors: Karel J. Keesman, Gerrit van Straten, Hubertus V. M. Hamelers, Nienke E. Stein
Format: Article
Language:English
Published: MDPI AG 2012-07-01
Series:Biosensors
Subjects:
Online Access:http://www.mdpi.com/2079-6374/2/3/255
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spelling doaj-e320b821862c49079502558fba45c69d2020-11-25T02:17:27ZengMDPI AGBiosensors2079-63742012-07-012325526810.3390/bios2030255Effect of Toxic Components on Microbial Fuel Cell-Polarization Curves and Estimation of the Type of Toxic InhibitionKarel J. KeesmanGerrit van StratenHubertus V. M. HamelersNienke E. SteinPolarization curves are of paramount importance for the detection of toxic components in microbial fuel cell (MFC) based biosensors. In this study, polarization curves were made under non-toxic conditions and under toxic conditions after the addition of various concentrations of nickel, bentazon, sodiumdodecyl sulfate and potassium ferricyanide. The experimental polarization curves show that toxic components have an effect on the electrochemically active bacteria in the cell. (Extended) Butler Volmer Monod (BVM) models were used to describe the polarization curves of the MFC under nontoxic and toxic conditions. It was possible to properly fit the (extended) BVM models using linear regression techniques to the polarization curves and to distinguish between different types of kinetic inhibitions. For each of the toxic components, the value of the kinetic inhibition constant Ki was also estimated from the experimental data. The value of Ki indicates the sensitivity of the sensor for a specific component and thus can be used for the selection of the biosensor for a toxic component. http://www.mdpi.com/2079-6374/2/3/255toxicity detectionmicrobial fuel cellbiosensorleast square estimationlinear regression
collection DOAJ
language English
format Article
sources DOAJ
author Karel J. Keesman
Gerrit van Straten
Hubertus V. M. Hamelers
Nienke E. Stein
spellingShingle Karel J. Keesman
Gerrit van Straten
Hubertus V. M. Hamelers
Nienke E. Stein
Effect of Toxic Components on Microbial Fuel Cell-Polarization Curves and Estimation of the Type of Toxic Inhibition
Biosensors
toxicity detection
microbial fuel cell
biosensor
least square estimation
linear regression
author_facet Karel J. Keesman
Gerrit van Straten
Hubertus V. M. Hamelers
Nienke E. Stein
author_sort Karel J. Keesman
title Effect of Toxic Components on Microbial Fuel Cell-Polarization Curves and Estimation of the Type of Toxic Inhibition
title_short Effect of Toxic Components on Microbial Fuel Cell-Polarization Curves and Estimation of the Type of Toxic Inhibition
title_full Effect of Toxic Components on Microbial Fuel Cell-Polarization Curves and Estimation of the Type of Toxic Inhibition
title_fullStr Effect of Toxic Components on Microbial Fuel Cell-Polarization Curves and Estimation of the Type of Toxic Inhibition
title_full_unstemmed Effect of Toxic Components on Microbial Fuel Cell-Polarization Curves and Estimation of the Type of Toxic Inhibition
title_sort effect of toxic components on microbial fuel cell-polarization curves and estimation of the type of toxic inhibition
publisher MDPI AG
series Biosensors
issn 2079-6374
publishDate 2012-07-01
description Polarization curves are of paramount importance for the detection of toxic components in microbial fuel cell (MFC) based biosensors. In this study, polarization curves were made under non-toxic conditions and under toxic conditions after the addition of various concentrations of nickel, bentazon, sodiumdodecyl sulfate and potassium ferricyanide. The experimental polarization curves show that toxic components have an effect on the electrochemically active bacteria in the cell. (Extended) Butler Volmer Monod (BVM) models were used to describe the polarization curves of the MFC under nontoxic and toxic conditions. It was possible to properly fit the (extended) BVM models using linear regression techniques to the polarization curves and to distinguish between different types of kinetic inhibitions. For each of the toxic components, the value of the kinetic inhibition constant Ki was also estimated from the experimental data. The value of Ki indicates the sensitivity of the sensor for a specific component and thus can be used for the selection of the biosensor for a toxic component.
topic toxicity detection
microbial fuel cell
biosensor
least square estimation
linear regression
url http://www.mdpi.com/2079-6374/2/3/255
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