4.8 Article

Use of a segmented cell for the combinatorial development of platinum group metal-free electrodes for polymer electrolyte fuel cells

期刊

JOURNAL OF POWER SOURCES
卷 452, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.jpowsour.2020.227829

关键词

Segmented fuel cell; Electrode layer; Optimization; Combinatorial test; Conditioning; Non-noble metal catalyst

资金

  1. U.S. Department of Energy (DOE) [DE-AC36-08GO28308]
  2. U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Fuel Cell Technologies Office (FCTO)

向作者/读者索取更多资源

The development of novel platinum group metal (PGM)-free catalysts is a challenging task, particularly when coupled with the integration into a cathode catalyst layer (CCL). The optimization of such PGM-free electrode structures is often non-linear and iterative, making it a demanding task due to the high number of parameters that affect performance. To accelerate both materials discovery and electrode development, this work demonstrates the application of a high-resolution segmented cell (SC) with 121 segments of 0.413 cm(2) size for combinatorial high-throughput PGM-free catalyst screening and CCL optimization. The approach utilizes three flow-field strips with active areas of 4.45 cm(2) each, distributed over 11 segments. Electrodes with identical catalyst material and fabrication method result in reproducible data with typically less than 10% variation between each segment and match in performance with differential single cell data. High throughput testing of combinatorial sample sets, with varied PGM-free electrocatalyst materials or electrode composition, demonstrated the ability to rapidly discern high performing outliers, and establish or confirm trends in electrode optimization. The results indicated significant performance benefits of electrodes with lonomer content of 45 wt % over lower values, and of water rich inks with 82% water content over those with 50%.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据