Abstract
This study introduces the utilization of self-powered microbial fuel cell (MFC)-based biosensors for the detection of biotoxicity in wastewater. Current MFC-based biosensors lack specificity in distinguishing between different pollutants. To address this limitation, a novel approach is introduced, capitalizing on the adaptive capabilities of anodic biofilms. By acclimating these biofilms to specific pollutants, an enhancement in the selectivity of MFC biosensors is achieved. Notably, electrochemically active bacteria (EAB) were cultivated on 3D porous carbon felt with and without a model toxicant (target analyte), resulting in the development of toxicant-resistant anodic biofilms. The model toxicants, Pb(2+) ions and the antibiotic neomycin sulfate (NS), were deployed at a concentration of 1 mg L(-1) during MFC operation. The influence of toxicity on biofilm growth and power production was investigated through polarization and power density curves. Concurrently, the electrochemical activity of both non-adapted and toxicity-adapted biofilms was investigated using cyclic voltammetry. Upon maturation and attainment of peak powers, the MFC reactors were evaluated individually as self-powered biosensors for pollutant detection in fresh wastewater, employing the external resistor (ER) mode. The selected ER, corresponding to the maximum power output, was positioned between the cathode and anode of each MFC, enabling output signal tracking through a data logging system. Subsequent exposure of mature biofilm-based MFC biosensors to various concentrations of the targeted toxicants revealed that non-adapted mature biofilms generated similar current-time profiles for both toxicity models, whereas toxicity-adapted biofilms produced distinctive current-time profiles. Accordingly, these results suggested that merely by adapting the anodic biofilm to the targeted toxicity, distinct and identifiable current-time profiles can be created. Furthermore, these toxicity-adapted and non-adapted biofilms can be employed to selectively detect the pollutant via the differential measurement of electrical signals. This differentiation offers a promising avenue for selective pollutant detection. To the best of our current knowledge, this approach, which harnesses the natural adaptability of biofilms for enhanced sensor selectivity, represents a pioneering effort in the realm of MFC-based biosensing.