Modelling and experimental validation of polarization behavior of airbreathing microfluidic fuel cell using some common fuels: Methanol, ethanol and sodium borohydride

Abstract

• MFC was fabricated with the prepared anode and airbreathing cathode. • A mathematical model is developed by considering various losses at anode and cathode. • The performance of the cell was assessed by evaluating polarization characteristics. • The predictions of the model-equations are validated to the experimental observations. Airbreathing microfluidic fuel cell (MFC) is emerging as the potential energy source for prospective use in glucose sensors, pacemakers, healthcare diagnostics, mobile phones, and DNA analysis devices, etc. A mathematical model of airbreathing MFC can aid in the explanation of a system as well as the prediction of behavior. In this line of work, the mathematical model for an airbreathing MFC was developed. Moreover, various losses such as activation, ohmic, and concentration overpotentials at anode and cathode were taking into account in the development of mathematical model. This developed model was validated with experimental data on current density vs. cell voltage characteristics at different fuel concentrations of 0.25 M to 1 M (CH 3 OH, C 2 H 5 OH) and 0.05 M to 0.3 M (NaBH 4 ) along with cell temperatures (33 °C, 50 °C and 65 °C). For these experiments, the electrocatalysts used to prepare the anodes were Platinum-Ruthenium (Pt-Ru) (30%:15% by wt.)/high surface area carbon (C HSA ) (fuel: CH 3 OH, C 2 H 5 OH), and Platinum (40% by wt.)/C HSA (fuel: NaBH 4 ). The cathodes were prepared using Pt (40% by wt.)/C HSA . The model's findings are in agreement with the experimental results well. Model prediction equation fairly reflected the influence of process variables such as fuel/electrolyte concentration and cell temperature on the prediction. The standard deviation values are found very low (0.02 to 0.082), indicating that developed mathematical model can be utilized in development of devices for industrial along with lab based applications. A high standard deviation, on the other hand, implies that the values are spread out across a higher range.