Abstract

Microbial electrochemical technologies (METs) represent a new class of electrochemical technologies in which one or more electrode reactions are catalyzed by microorganisms. The most common example of an MET is the microbial fuel cell (MFC). In MFCs, anode-respiring bacteria (ARB) catalyze the oxidation of simple organic molecules such as acetate, and respire to an anode, thus producing an electrical current. The electrons thus respired move through an external circuit to the cathode, where oxygen is reduced (non-biologically) to water. In this presentation, I will demonstrate through a decade of research I have been involved in how traditional interpretations of a typical fuel cell polarization curve do not apply to MFCs. For example, anodic polarization curves on ARB biofilms show that maximum current in produced with an overpotential of about 0.2 to 0.3 V. Any further increases in anode potential do not result in higher current densities, and the anodic polarization curve looks like it is dominated by a “mass transport limitation”; yet, no amount of higher substrate or buffer (to transport out the protons as a product of anode respiration) results in alleviation of this limitation. The limitation in current production is instead due to intracellular machinery in ARB that is optimized to respire to electron acceptors within a narrow range of redox potentials. On the other hand, cathode polarization curves show a large overpotential at low current densities, representative of “activation losses” as per standard fuel cell interpretations; yet these overpotentials, even for platinum-group metals when used in MFC cathodes, are larger than 0.5 V, which is unrealistic. The large cathodic overpotentials at low current densities are instead mass transport related losses that affect the cathode pH and thus the overall thermodynamics of the oxygen reduction reaction. Through a comprehensive understanding of the differences in overpotentials in MFCs in relation of typical fuel cell overpotentials, we can now begin to apply methods through which these could be overcome to design systems that would perform well as they are scaled up.

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