A pore-scale model of a PEMFC cathode catalyst layer was developed using the pore network approach and used to predict polarization behavior. A volumetric image of a PEMFC catalyst layer was obtained using FIB-SEM with 4 nm resolution in all 3 directions. The original image only differentiated between solid and void, so a simple but effective algorithm was developed to insert tightly packed, but non-overlapping carbon spheres into the solid phase, which were then decorated with catalyst sites. The resultant image was a 4-phase image containing void, ionomer, carbon, and catalyst, each in proportion to the known Pt loading, carbon-to-ionomer ratio, and porosity. A multiphase pore network model was extracted from this image, and multiphysics simulations were conducted to predict the polarization behavior of an operating cell. It was shown that not only can beginning of life polarization performance be predicted with minimal fitting parameters, but degraded performance 30 k cycles was also well captured with no additional fitting. This latter result was accomplished by deleting catalyst sites from the network in proportion to the experimentally observed distribution of electrochemical surface area loss, obtained from TEM image of catalyst loading. The model included partitioning of oxygen into the ionomer phase, explicitly incorporating the oxygen transport resistance which dominates cell performance at higher current density. Although Knudsen diffusion is present at the scales present ($<100\mathit{nm}$), it represented a negligible fraction of the total transport resistance, which was dominated by the low solubility and slow diffusivity in the ionomer phase. This work showed that the performance of a typical PEMFC is highly dependent on the structural details of the catalyst layer, to the extent that polarization curves can be well predicted by direct inspection of an image of the catalyst layer. This work paves the way for a deeper understanding of the structure-performance relationship in these complex materials and the search for optimized catalyst layer designs.

使用孔隙网络方法开发了 PEMFC 阴极催化剂层的孔隙尺度模型，并用于预测极化行为。体积_使用 FIB-SEM 在所有 3 个方向上以 4 nm 分辨率获得 PEMFC 催化剂层的图像。原始图像仅区分固体和空隙，因此开发了一种简单但有效的算法，将紧密堆积但不重叠的碳球插入固相，然后用催化剂位点装饰。所得图像是包含空隙、离聚物、碳和催化剂的 4 相图像，每种图像均与已知的 Pt 负载量、碳与离聚物比率和孔隙率成比例。从该图像中提取多相孔隙网络模型，并进行多物理场模拟来预测运行单元的极化行为。结果表明，不仅可以用最小的拟合参数来预测寿命初期的偏振性能，但无需额外拟合即可很好地捕获 30 k 个循环的性能下降。后一个结果是通过根据实验观察到的电化学表面积损失分布（从催化剂负载的 TEM 图像获得）按比例从网络中删除催化剂位点来实现的。该模型包括将氧气分配到离聚物相中，明确纳入了氧传输阻力，该阻力在更高的电流密度。尽管克努森扩散存在于现有的尺度上（$<100\mathit{纳米}$），它占总传输阻力的一小部分可以忽略不计，这主要是由离聚物相中的低溶解度和缓慢扩散率决定的。这项工作表明，典型质子交换膜燃料电池的性能高度依赖于催化剂层的结构细节，甚至可以通过直接检查催化剂层的图像来很好地预测极化曲线。这项工作为更深入地了解这些复杂材料的结构与性能关系以及寻找优化的催化剂层设计铺平了道路。