Ice-Crystallization Kinetics in the Catalyst Layer of a Proton-Exchange-Membrane Fuel Cell
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Abstract
Nucleation and growth of ice in the catalyst layer of a proton-exchange-membrane fuel cell (PEMFC) are investigated using isothermal differential scanning calorimetry and isothermal galvanostatic cold-starts. Isothermal ice-crystallization rates and ice-nucleation rates are obtained from heat-flow and induction-time measurements at temperatures between 240 and 273 K for four commercial carbon-support materials with varying ionomer fraction and platinum loading. Measured induction times follow expected trends from classical nucleation theory and reveal that the carbon-support material and ionomer fraction strongly impact the onset of ice crystallization. Conversely, dispersed platinum particles play little role in ice crystallization. Following our previous approach, a nonlinear ice-crystallization rate expression is obtained from Johnson-Mehl-Avrami-Kolmogorov (JMAK) theory. A validated rate expression is now available for predicting ice crystallization within water-saturated catalyst layers. Using a simplified PEMFC isothermal cold-start continuum model, we compare cell-failure time predicted using the newly obtained rate expression to that predicted using a traditional thermodynamic-based approach. From this comparison, we identify conditions under which including ice-crystallization kinetics is critical and elucidate the impact of freezing kinetics on low-temperature PEMFC operation. The numerical model illustrates that cell-failure time increases with increasing temperature due to a longer required time for ice nucleation. Hence, ice-crystallization kinetics is critical when induction times are long (i.e., in the “nucleation-limited” regime for T > 263 K). Cell-failure times predicted using ice-freezing kinetics are in good agreement with the isothermal cold-starts, which also exhibit long and distributed cell-failure times for T > 263 K. These findings demonstrate a significant departure from cell-failure times predicted using the thermodynamic-based approach.