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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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 |
work_keys_str_mv |
AT kareljkeesman effectoftoxiccomponentsonmicrobialfuelcellpolarizationcurvesandestimationofthetypeoftoxicinhibition AT gerritvanstraten effectoftoxiccomponentsonmicrobialfuelcellpolarizationcurvesandestimationofthetypeoftoxicinhibition AT hubertusvmhamelers effectoftoxiccomponentsonmicrobialfuelcellpolarizationcurvesandestimationofthetypeoftoxicinhibition AT nienkeestein effectoftoxiccomponentsonmicrobialfuelcellpolarizationcurvesandestimationofthetypeoftoxicinhibition |
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1724886289000431616 |