ABSTRACT
The water contents at both the anode and cathode of PEMFCs depend on the water-transport mechanism at the membrane. The humidity at the outside layers of the membrane determines the diffusion of water through it. The operating temperatures and pressures regulate the humidity conditions in the system. Because these parameters are nonlinear, the water-transport mechanism is analyzed via the difference in the water concentration on each side of the membrane. In this work, an experimental configuration is designed to investigate the diffusion mechanism of water through the membrane. A flat membrane module is tested in an isothermal test chamber to test the influence of temperature on the water-absorption and -transport characteristics of Nafion 117 and Nafion 211 membranes. A parametric study is conducted to test the water-transport mechanism at an operating pressure of 1 bar; temperatures of 30 °C, 50 °C, 70 °C and 90 °C; and a relative humidity ranging from 10% to 100%. The results indicate that the water content of Nafion 211 is higher than that of Nafion 117. The water content and diffusion coefficient are proportional to the operating temperature. In addition, the diffusion coefficient reaches its peak at conditions of 1 bar, 100% humidity, and 90 °C for both membrane types.
ABSTRACT
Water transport in a hollow-fiber membrane depends on mass convection around the tube, mass convection inside the tube, and water diffusion through the membrane tube. The performance of water transport is then explained by the overall mass transfer coefficient in hollow-fiber membranes. This study presents the prediction of overall mass transfer coefficients of water transport in a hollow-fiber membrane module by an artificial neural network (ANN) that is used for a humidifier of a vehicular fuel cell system. The input variables of ANN are collected from water transport experiments of the hollow-fiber membrane module that is composed of inlet flow rates, inlet relative humidity, system pressures, and operating temperatures. The experimental mass transfer coefficients are the targets of the training model, which are determined via the effectiveness analysis. When unknown data are applied to the ANN model, the correlation of the overall mass transfer coefficient predicts precise results with R = 0.99 (correlation coefficient). The ANN model shows good prediction capability of water transport in membrane humidifiers.
ABSTRACT
As silver nanowires (Ag NWs) are usually manufactured by chemical synthesis, a patterning process is needed to use them as functional devices. Pulsed laser ablation is a promising Ag NW patterning process because it is a simple and inexpensive procedure. However, this process has a disadvantage in that target materials are wasted owing to the subtractive nature of the process involving the removal of unnecessary materials, and large quantities of raw materials are required. In this study, we report a minimum-waste laser patterning process utilizing silver nanoparticle (Ag NP) debris obtained through laser ablation of Ag NWs in liquid media. Since the generated Ag NPs can be used for several applications, wastage of Ag NWs, which is inevitable in conventional laser patterning processes, is dramatically reduced. In addition, electrophoretic deposition of the recycled Ag NPs onto non-ablated Ag NWs allows easy fabrication of junction-enhanced Ag NWs from the deposited Ag NPs. The unique advantage of this method lies in using recycled Ag NPs as building materials, eliminating the additional cost of junction welding Ag NWs. These fabricated Ag NW substrates could be utilized as transparent heaters and stretchable TCEs, thereby validating the effectiveness of the proposed process.
