On-line detection of toxic components using a microbial fuel cell-based biosensor

Safe drinking water without toxic chemicals is crucial for people's health. A recently developed sensor for the detection of toxic components in water is the microbial fuel cell (MFC)-based biosensor. In this biosensor, substrate consumption rate and metabolic activity of bacteria are directly related to the electric current. A reduction in current under otherwise similar conditions is an indication of toxic inhibition. Under steady state conditions, current can be described by the Butler–Volmer–Monod (BVM) model. Knowing which parameters of this model change under toxic contamination can give an indication on the type of toxicity. The model requires that the substrate concentration is known. It is shown in this paper that is not possible to estimate both the substrate concentration as well as the BVM parameters on-line from current data at constant overpotential. However, it appears that substrate concentration and substrate consumption rate can be estimated on-line, and that after a linear reparametrization the BVM parameters can be estimated by ordinary least-squares techniques from a polarization curve that is generated as soon as a suspect change in current occurs. Analysis shows that a weighted least-squares method is necessary to secure a good fit at the overpotentials where current is most sensitive to changes in kinetic parameters. A protocol for on-line detection of toxicity and for detection of the type of kinetic inhibition is provided.

► Online detection of water quality changes.
► Online estimation of substrate concentration and consumption rate.
► Estimation of model parameters indicating the toxicity type from polarization curves.
► A protocol for operating the sensor is provided.