Numerical investigation and optimization of vapor-feed microfluidic fuel cells with high fuel utilization

Vapor-feed microfluidic fuel cell (VF-MFC) has various advantages against the conventional liquid-feed microfluidic fuel cell, such as simpler fluidic management, higher fuel utilization, flow rate insensitiveness, and so on. To better understand the mechanisms behind its superiority and to further optimize its performance, a 3D isothermal numerical model has been developed in this work. The computational results agree very well with the previous and present experimental data, proving the validity of the current model for the VF-MFC simulation. Through this model, it is found that the dissolved fuel in the VF-MFC is well-controlled within a thin boundary layer nearby the anode catalyst surface, which can not only satisfy the demand of anode oxidation reaction but also greatly alleviate the wastage of fuel. In this manner, the VF-MFC can achieve satisfactory power output and high fuel utilization at the same time. In addition, the boundary layer effect on electrolyte flow rate can keep the fuel concentration in the thin layer relatively stable at different flow rates, which may be the reason behind the insensitiveness of VF-MFC performance to electrolyte flow rate. To further improve its power output and fuel efficiency, effects of the fuel evaporation area and the anode open ratio have also been thoroughly investigated with the present model. It is found that an evaporation-reaction area ratio of 11.1 is sufficient for the present VF-MFC, while a smaller fuel evaporation area can lead to improved fuel utilization at the expense of lower power output. To improve both the fuel utilization and power output, the electrode area towards the channel outlet is increased while keeping the vapor entrance area constant, i.e. the anode open ratio is reduced. By this strategy, the VF-MFC can achieve 48% higher power output and elevated fuel utilization from 27.5% to 41.8%, when an anode open ratio of 1:3 is adopted. (C) 2017 Elsevier Ltd. All rights reserved.