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, 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.

Kinetic models for detection of toxicity in a microbial fuel cell based biosensor.

Currently available models describing microbial fuel cell (MFC) polarization curves, do not describe the effect of the presence of toxic components. A bioelectrochemical model combined with enzyme inhibition kinetics, that describes the polarization curve of an MFC-based biosensor, was modified to describe four types of toxicity. To get a stable and sensitive sensor, the overpotential has to be controlled. Simulations with the four modified models were performed to predict the overpotential that gives the most sensitive sensor. These simulations were based on data and parameter values from experimental results under non-toxic conditions. Given the parameter values from experimental results, controlling the overpotential at 250 mV leads to a sensor that is most sensitive to components that influence the whole bacterial metabolism or that influence the substrate affinity constant (Km). Controlling the overpotential at 105 mV is the most sensitive setting for components influencing the ratio of biochemical over electrochemical reaction rate constants (K1), while an overpotential of 76 mV gives the most sensitive setting for components that influence the ratio of the forward over backward biochemical rate constants (K2). The sensitivity of the biosensor was also analyzed for robustness against changes in the model parameters other than toxicity. As an example, the tradeoff between sensitivity and robustness for the model describing changes on K1 (IK1) is presented. The biosensor is sensitive for toxic components and robust for changes in model parameter K2 when overpotential is controlled between 118 and 140 mV under the simulated conditions.

Butler-Volmer-Monod model for describing bio-anode polarization curves.

A kinetic model of the bio-anode was developed based on a simple representation of the underlying biochemical conversions as described by enzyme kinetics, and electron transfer reactions as described by the Butler-Volmer electron transfer kinetics. This Butler-Volmer-Monod model was well able to describe the measured bio-anode polarization curves. The Butler-Volmer-Monod model was compared to the Nernst-Monod model described the experimental data significantly better. The Butler-Volmer-Monod model has the Nernst-Monod model as its full electrochemically reversible limit. Contrary to the Nernst-Monod model, the Butler-Volmer-Monod model predicts zero current at equilibrium potential. Besides, the Butler-Volmer-Monod model predicts that the apparent Monod constant is dependent on anode potential, which was supported by experimental results.

Stabilizing the baseline current of a microbial fuel cell-based biosensor through overpotential control under non-toxic conditions.

A MFC-based biosensor can act as online toxicity sensor. Electrical current is a direct linear measure for metabolic activity of electrochemically active microorganisms. Microorganisms gain energy from anodic overpotential and current strongly depends on anodic overpotential. Therefore control of anodic overpotential is necessary to detect toxic events and prevent false positive alarms. Anodic overpotential and thus current is influenced by anode potential, pH, substrate and bicarbonate concentrations. In terms of overpotential all factor showed a comparable effect, anode potential 1.2% change in current density per mV, pH 0.43%/mV, bicarbonate 0.75%/mV and acetate 0.8%/mV. At acetate saturation the maximum acetate conversion rate is reached and with that a constant bicarbonate concentration. Control of acetate and bicarbonate concentration can be less strict than control of anode potential and pH. Current density changes due to changing anode potential and pH are in the same order of magnitude as changes due to toxicity. Strict control of pH and anode potential in a small range is required. The importance of anodic overpotential control for detection of toxic compounds is shown. To reach a stable baseline current under nontoxic conditions a MFC-based biosensor should be operated at controlled anode potential, controlled pH and saturated substrate concentrations.